专利摘要:
Various embodiments include apparatuses and methods for estimating the properties of a rock, a drill bit (105; 1205), or a combination thereof associated with a drilling operation. Properties may include, but are not limited to, rock chip size or drill bit cutting edge (105; 1205) or drilling efficiency or a selected combination of rock chip size, drill bit sharpness (105; 1205) or drilling efficiency. The estimate can be made from the correlation of the detected acoustic emission and detected electromagnetic emissions. In various embodiments, the friability of the formation can be determined. The various estimates can be used to control a drilling operation. Additional devices, systems and processes are also disclosed.
公开号:FR3038070A1
申请号:FR1654388
申请日:2016-05-18
公开日:2016-12-30
发明作者:Paul F Rodney
申请人:Halliburton Energy Services Inc；
IPC主号:

专利说明:

[0001] FIELD OF THE INVENTION The present invention generally relates to apparatus and methods associated with measurements and operations related to gas and oil exploitation. Background In the scientific article "Acoustic And Electromagnetic Emission From Crack Created In Rock Sample Under Deformation", Yasuhiko Mori, Yoshihiko Obatal and Josef Sikula, J. Acoustic Emission, 27 (2009), background information is provided on the show. sound when the rock is broken by compacted polycrystalline diamond (PDC) crowns. In the results reported in this paper, sound emission produced by a drill bit, as measured by a vibration sensor on a rock sample, was directly related to the depth of cut of the drill bit. Above a certain threshold (80 iam for these studies), the crown causes microscrits of the rock and the emitted signal is erratic. For a depth of cut below this threshold, the acoustic emissions are more regular and have a smaller amplitude. In addition, for a given force or depth of cut, the amplitude of the signal was an increasingly linear function of depth or cutting force and also an increasing function of the cutting edge of the drill bit teeth. The scientific article "Experiments to demonstrate piezoelectric and pyroelectric effects," Jeff Erhart, Physics Education, 48 (4), 2013 IOP Publishing 25 Ltd., p. 438, reports electrical and acoustic emission measurements when rock samples are crushed in a controlled environment. Like the scientific article by Mori et al., Measurements were made on the rock. The authors separate two effects that operate in the generation of an electromagnetic field during fracturing of the rock. First, there is a low frequency electrical potential due to the piezoelectric effect, and secondly, an electromagnetic wave is emitted due to the seismic conversion. Seismoelectric conversion describes the creation of an electromagnetic wave when an acoustic wave passes through a porous medium. The movement of the fluid against the rock pores creates an electromagnetic field through a flow potential. The rock samples used in this test were cylindrical with a one-inch cut and a four-inch length. The samples were gradually milled with orthogonal force to the circular faces of the cylinder. An acoustic transducer was mounted at the base of the test apparatus and electrodes were attached along the cylinder body.
[0002] When the rock is fragmented, potential differences as high as about 1 V have been observed along the electrodes. The fracturing of the rock was characterized by electrical peaks followed by peaks in the acoustic output. Peak signatures were in the order of a millisecond. After a delay from the beginning of the peak voltage, an acoustic peak was observed with an exponential decay tint. The delay in the acoustic response can be explained by the difference in the wave speed between acoustic and electromagnetic signals. It was noted that the amplitude and polarization of the observed voltages varied according to the point of observation. Tests were carried out on both wet rock and dry rock. From these tests, the authors were able to separate the piezoelectric effect from the seismic effect in that the seismoelectric effect can not be obtained in dry rock. The authors noted that "when a saturated rock sample of fluid breaks up, the moving charges in the fluid induce electromagnetic waves, which propagate independently and can be received near or far from the fragmentation zone. In addition, the electrical signals recorded in the wet rock were stronger than those of the dry rock and varied very little in magnitude, signature or phase at the different measurement points. Concerning the piezoelectric effect, the authors note that when the acoustic amplitude is small, before the fragmentation of the rock, the level of 3038070 3 CC changes, the polarization and the magnitude of the change are dependent on the position at the level of the rock sample. This can be attributed to the piezoelectric effect. The polarization variability is caused by the variability of the orientation of the piezoelectric material (quartz) within the rock matrix. The reference "Experimental studies of seismoelectric effects in fluidsaturated porous media" Chen Benchi and Yongguang Mu, J. Geophys. Eng. 2 (2005) 222-230, Nanjing Institute of Geophysical Prospecting and Institute of Physics Publishing, presents a hybrid species between the experiments of Articles 10 of More et al. and Erhart, but with rock samples about 1/3 of those from More et al. Moreover, in Chen et al., Pulse events were counted as a function of penetration depth and it was noted that the number of such events, correlated between the electric field and acoustic measurements, increases with increase in penetration depth. It has also been noted that the magnetic signal can be detected with a "spring", even though no direct magnetometer measurements have been reported in Chen et al. The scientific article "A Transportable System for Monitoring Ultra Low Frequency Electromagnetic Signals Associated with Earthquakes", Darcy Karakelian, Simon L. Klemperer, Antony C. Fraser-Smith, and Gregory C. Beroza, Seismological Research Letters Volume 71, Number 4 , 423-436 July / August 2000, is not directly relevant to drilling, but confirms that the good conclusions regarding seismic effects in the measurements of Erhart's article have been drawn. One result that is common to all these references is that the amplitudes of the electrical and acoustic signals increase just before breaking.
[0003] Scientific Papers "Low Frequency Magnetic Field Measurements Near the Epicenter of the 7.1 Loma Prieta Earthquake", A. C. Fraser-Smith, A. Bernardi, P. R. McGill, M. E. Ladd, R. A. Helliwell, O.G. Villard, Jr., Geophysical Research Letters, Vol. 17, No. 9, pp. 1465-1468, August 1990; "The results of experimental studies of VLF-ULF electromagnetic emission by rock sampler due to 30 mechanical action", A. A. Panfilov, Nat. Hazards Earth Syst. Sci. Discuss., 1, 3038070 7821-7842, 2013; and "Performance Drilling-Definition, Benchmarking, Performance Qualifiers, Efficiency and Value," G. Mensa-Wilmot, S. Southland, P. Mays, P. Dumronghthai, D. Hawkins, P. Llavia, SPE / IADC 119826, presented to the SPE / IADC Drilling Conference and Exhibition held in Amsterdam, the Netherlands, 5 March 17-19, 2009, provides further information on rock fracturing and the emission of electromagnetic and acoustic signals. The results presented in these articles are about earthquakes and therefore represent a much larger scale than we are interested in here. However, they further confirm the underlying mechanisms. Each of these articles notes a relationship to the magnetic field and describes the same underlying mechanisms detailed in the small-scale experiments of Mori et al. and Erhart. BRIEF DESCRIPTION OF THE FIGURES FIG. 1 is a diagram of an exemplary structure of a drill bit having sensors mounted on or within the drill bit in accordance with various embodiments. Figure 2 is a diagram of an example of an electric field sensor mounted on or within the drill bit of Figure 1, according to various embodiments.
[0004] Figure 3 is a diagram of an example of a combined magnetic or electric field sensor that can be mounted on or within the drill bit of Figure 1, in accordance with various embodiments. Figure 4 is a diagram of an example of a combined magnetic or electric field sensor having local signal processing and a wireless device that can be mounted on or within the drill bit of Figure 1 according to various embodiments. Figure 5 is a flowchart of an example of electronic components with a wireless device that can be structured within a component arrangement of Figure 4, according to various embodiments.
[0005] Figure 6 is a schematic diagram of an example of an electric field sensor which can be mounted on or inside the drill bit in accordance with various embodiments. Fig. 7 is a diagram of an example of an electric field sensor assembly of Fig. 6 with vibration sensors according to various embodiments. Fig. 8 is a diagram of an exemplary alternative electric field sensor assembly of Fig. 6, in accordance with various embodiments. Figure 9 is a schematic of an example of using the module of Figure 6 with a compacted polycrystalline diamond drill bit in accordance with various embodiments. Fig. 10 is a diagram illustrating a different perspective of Fig. 1 showing additional locations for sensor mounting according to various embodiments. Figure 11 is a diagram of an example of toroidal and solenoid sensors that can be coupled to a drill bit in accordance with various embodiments. Figure 12 is a diagram of toroidal and solenoid sensors that can be coupled to a drill bit in accordance with various embodiments. Figure 13 is a schematic diagram of a front view of an example of a drill bit having a plugged nozzle with an installed electric field sensor in accordance with various embodiments. Figure 14 is a representation of an example of an oscillating pulse from rock fracturing, according to various embodiments. Fig. 15 is a series of exemplary power spectral density graphs for different values of a decomposition time characteristic of tingling after rock fracturing, in accordance with various embodiments. Fig. 16 is a set of power spectral density example graphs for different values of a parameter which is a characteristic time for stress accumulation with respect to a rock, in accordance with various embodiments. Fig. 17 is a set of power spectral density example graphs for different values of a parameter which is a characteristic time for stress accumulation with respect to a drill bit in accordance with various embodiments. Figure 18 is a sample set of graphs in a time segment of the Monte Carlo simulation, according to various embodiments. Figure 19 is a sample set of self-correlation graphs associated with the received simulated signal of Figure 18, according to various embodiments. Figure 20 is an enlarged view of Figure 19 in accordance with various embodiments. Figure 21 is an example of an autocorrelation graph of Figure 19 in comparison to the autocorrelation of the last signal illustrated in Figure 41, which is characteristic of catastrophic crown failure, in accordance with various embodiments. Figure 22 is an enlarged view of Figure 21, in accordance with various embodiments. Fig. 23 is a diagram of an exemplary system structured to operate with respect to optimization of drilling efficiency, in accordance with various embodiments. Figure 24 is a flowchart of an exemplary system structured to operate with respect to optimization of drilling efficiency, in accordance with various embodiments.
[0006] Figure 25 is a flow diagram of an exemplary efficiency calculation module according to various embodiments. Figure 26 is a flowchart of an exemplary flushing routine for defining drilling parameters for optimum efficiency using inputs from the efficiency calculation module of Figure 25, in accordance with various modes of operation. production.
[0007] FIG. 27 is a flowchart of an exemplary method of examining power spectral densities and inter-power spectral density according to various embodiments. Fig. 28 is a flowchart of an exemplary method of calculating step sizes for crown weight, rotational speed, and through-crown flow in accordance with various embodiments. Figure 29 is a flowchart of an exemplary method of determining cost functions for different lithologies, according to various embodiments.
[0008] Figure 30 is a flowchart of an exemplary method of downhole use of the cost function library according to various embodiments. Figure 31 is a flowchart of an example of acoustic and electrical or magnetic data flow analysis according to various embodiments. Figure 32 is a sample set of power spectral density graphs for rock fragmentation with normally distributed characteristic frequencies centered around a 300 Hz characteristic frequency for different standard deviations, according to various embodiments. .
[0009] Fig. 33 is a set of power spectral density chart examples for rock fragmentation in the absence of variance of the fragmentation parameters, for different values of the decomposition time characteristic, in accordance with various embodiments. Figure 34 is a set of graphical examples from a Monte Carlo simulation for rock fragmentation with center frequency spread distribution according to various embodiments. Figure 35 is a sample set of graphs derived from a Monte Carlo simulation for rock fragmentation with a standard deviation distribution of the characteristic times associated with the accumulation of stress in the rock, according to various modes. of realization.
[0010] Figure 36 is a sample set of graphs derived from a Monte Carlo simulation for rock fragmentation with a standard deviation distribution of characteristic times associated with stress accumulation in the rock, in accordance with various embodiments.
[0011] Figure 37 is a sample set of graphs derived from a Monte-Carlo simulation for rock fragmentation with a typical standard deviation distribution of time for rock fracture decomposition according to various embodiments. . Figure 38 is a sample set of graphs derived from a Monte Carlo simulation for rock fragmentation in which fragmentation of the corona is not included in the analysis, according to various embodiments. Figure 39 is a sample set of graphs from a Monte Carlo simulation for rock fragmentation in which crown fragmentation is included in the analysis, according to various embodiments. Figure 40 is a sample set of graphs derived from a Monte Carlo simulation for rock fragmentation in which crown fragmentation is included in the analysis, according to various embodiments. Fig. 41 is a time signature graph example set which has been analyzed to generate the power spectrum of Fig. 40, in accordance with various embodiments. Figure 42 is a flowchart of the features of an exemplary method using acoustic emissions and electromagnetic emissions emitted by the rock when it is fragmented in a drilling operation of a drill bit in accordance with various embodiments. . Figure 43 is a flowchart of the features of an exemplary functional system for controlling the operation of acoustic and electromagnetic sensors at or near a drill bit in a drilling activity, in order to perform measurements in a borehole. drilling, to implement a processing scheme for correlating the acoustic emission with an electromagnetic emission detected by the acoustic and electromagnetic sensors, and for estimating one or more of a rock chip size, the cutting of the drill bit or drilling efficiency, and / or to determine or identify the friability of the formation, in accordance with various embodiments. Detailed Description The following detailed description corresponds to the accompanying drawings which show, by way of illustration, and not limitation, various specific embodiments which may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice this and other embodiments. Other embodiments may be used, and structural, logical and electrical changes may be made to these embodiments. The various embodiments are not necessarily mutually exclusive, since some embodiments may be combined with one or other embodiments to form new embodiments. The following detailed description should not be taken in a limiting sense. It is known that rocks emit acoustic and electromagnetic emissions when they are fragmented. Several mechanisms are behind these emissions. Some of these mechanisms also affect the wear of PDC cutters. As described herein, a deduction for crown wear, cuttings size and lithology can be obtained by performing simultaneous measurements of acoustic and electromagnetic emissions from within or on a drill bit and correlating these measures. Simultaneous measurements of acoustic and electromagnetic emissions can be made near the drill bit, where the term "near the drill bit" means on a measuring tool or on a drill string at a distance of 10 feet from the drill bit drilling. These measurements can be made with acoustic or vibration sensors, electric field sensors and dynamic magnetic field sensors. These techniques can be applied to PDCs and tapered roller bits, even if these crowns bore with different mechanisms. PDC bits dig primarily by shear, while tapered drill bits dig primarily by grinding and scraping.
[0012] It is desirable to analyze acoustic emissions during drilling in order to obtain an estimate of drill bit wear. In a number of conventional approaches, it is not possible to measure this from the formation, since measurements must be made from inside, on or near the drill bit. This feature complicates matters since rock fragmentation is not the only source of acoustic emission during drilling. The drill bit continually strikes against the wall of the borehole, and may bounce off the bottom of the borehole. In addition, the drill string itself rubs and taps against the wall of the borehole. It is therefore desirable to obtain another signal which is correlated with rock fragmentation and / or bit wear and which is not correlated in the same way with drill bit and drill string dynamics. In various embodiments, procedures are implemented to obtain a cutting edge measurement of the drill bit cutter and a measure of the size distribution of the cuttings as the drill bit advances. Statistics can be generated on the size distribution of cuttings when cuttings are produced. Such procedures provide a mechanism for measuring drill bit wear at the bottom of the well. These measures can directly relate to access to the efficiency of the drilling operation and can be used to optimize this efficiency. Apparatuses and methods for determining drill bit cutting edge and cuttings size distribution provide information which, in the presence of appropriate communications and control, can be combined to optimize the efficiency of the drill operation. drilling. A PDC bit is used in the examples presented here, although similar embodiments can be embodied with tapered roller bits.
[0013] FIG. 1 is a diagram of an embodiment of an exemplary structure of a drill bit 105 having sensors 102, 103 mounted on or within the drill bit 105. The sensor 102 and FIG. the sensor 103 may be part of a set of sensors used to take electromagnetic and vibration measurements. The drill bit 105 may comprise a solenoid, an accelerometer and a gyroscopic micro-electro-mechanical system (MEMS) placed inside a drill bit 105 which is not visible in the view of FIG. It does not have to be the presence of all the sensors mentioned. A minimal subset may be in the form of a vibration sensor and either an electric field sensor or a magnetic field sensor. The electric or magnetic field sensors can be structured so that the electric or magnetic field sensors do not have static field sensitivity. These sensors may include, without limitation, an electric field sensor, a torus, an accelerometer, a solenoid and a gyroscopic MEMS.
[0014] An electric field sensor can be embodied as a plurality of electric field sensors. In its simplest form, an electric field sensor 102 may comprise a dielectric cylinder 108 having a metal disk 106 on one end, which is schematically illustrated in Figure 1, but in more detail in Figure 2. Note that the magnetic field sensor illustrated in Figure 3 can be used. Electric field sensors and magnetic field sensors may be mounted, as shown, in a cavity in the sidewall of the structures that support the drill bit teeth. The back of the sensor makes electrical contact with the bit body, which is an electrical conductor. An insulated wire 107 connected to a conduction plate 106 which floats in a dielectric cylinder 108 may constitute a second terminal of the electric field sensor 102. Each of these terminals may be moved away from there the front area of the bit towards the rear area of the bit, where electronic components can be placed, through a channel in the sidewall of the structure that supports the electric field sensor 102.
[0015] The electric field sensors and the magnetic field sensors 3038070 12 may also be mounted on the face of the drill bit and above the drill bit body. In an alternative embodiment, a battery and a wireless device according to IEEE 1902.1 (also known as RuBee) may be mounted with an electric field sensor or a combined electric field and magnetic field sensor. Wireless devices of this type are useful for short-range communication in magnetic environments, such as that of a drill bit. A sensor modified with this technology is illustrated in FIG. 4. A torus 103 may be mounted on a drill bit shaft 105. The torus 103 responds to the magnetic field generated by the variable currents in the time flowing along. the axis of the drill bit. The time-varying currents may be the result of an electric or seismoelectric field generated in the vicinity of the drill bit as well as the rotation of the drill bit in the Earth's magnetic field.
[0016] An accelerometer may be used or a plurality of accelerometer may be used. The accelerometers may be mounted just below the bit shank on the bit body, e.g., in region 109 of FIG. 1. The accelerometers are preferably responsive only to vibration, i.e. d., they do not respond to the Earth's gravitational field, and may also be called vibration sensors. The accelerometers may include axes of direction along the axis of the drill bit or orthogonal to the axis of the drill bit. Accelerometers that are orthogonal to the drill bit axis may also be orthogonal to each other. These accelerometers can be used to capture a transaxial vibration, but do not respond to the tangential (voltage) vibration. A solenoid can be placed around the drill bit shank. This sensor can be used to measure the component of the variable magnetic field over time along the axis of the drill bit. This field can be generated by sismoelectric probes or by the current induced in the piezoelectric effect. However, it is anticipated that the last effect would be small compared to the seismic effect. A gyroscopic MEMS can be used to capture the instantaneous rotation speed of the drill bit. Its use is optional, but it provides additional information on drilling dynamics.
[0017] Figure 2 is a diagram of an example of an electric field sensor 102 that may be implemented on, in, or near a drill bit. The electric field sensor 102 may comprise a metal can 104 with an open end, filled with a dielectric material 108, and in which a metal plate 106 can be floated parallel to the rear of the can. The base of the can can be inserted into a cavity in the sidewall of the structure that supports the teeth of the drill bit. Even if it is not necessary, it is advantageous that the surface of the disc 106 facing the open end of the can is covered with a dielectric material. The dielectric material should be resistant to abrasion and etching and should have the lowest dielectric constant possible, in accordance with other constraints. The disk 106 may have a diameter that is considerably smaller than the diameter of the bobbin 104. A design approach may be oriented to create a surface that responds to the potential gradient across the dielectric component with minimal capacitance to the earth so to maximize the voltage across the sensor. An alternating electric field sensor is shown in Figure 6, which can be adapted from US Patent No. 5,720,355. An isolated strip 107 connected to the disk can be placed along the side of the structure that supports the teeth of the body. drill bit, as previously described. Figure 3 illustrates a combined electric and magnetic field sensor 112. The sensor 112 may have a configuration that is substantially the same or identical to the configuration of Figure 2, with the addition of a magnetic field sensor. In this case, a solenoid winding 111 of the wire such as a copper wire is also floated in the dielectric material 108 with the disk 106 and the lead wire 107. Ideally, the dielectric material may also be ferrite. . Available ferrites are good insulators and, within the range of 3038070 relevant frequencies (e.g., frequencies below 10 kHz), have a relative dielectric constant of the order of 40. A terminal 114 of solenoid 111 can be grounded with the bobbin 104 while the other terminal 113 can be placed along the side of the structure that supports the drill bit teeth, where this portion of the solenoid wire 113 is to be isolated. FIG. 4 is a diagram of an electric field and a magnetic field combined with a device of the type of the IEEE 1902.1 116 standard. The combined electric field and magnetic field sensor can be embodied by the electric field sensor and combined magnetic field 112 of Fig. 3 and having a solenoid 111 and a disc 106 with a connector wire 107 in a bobbin 104. The connection 131 may be arranged as a connection to a subterranean portion of the solenoid 111 and a connection 132 may The IEEE 1902.1 116 type device may be coupled to a battery which may also power the signal processing module 118 IEEE 1902.1 standard type, similar or identical to the IEEE 1902.1 116 type device, are available, for example, from Visible Assets, Inc. These devices are They are compact and capable of communicating up to 50 feet on steel and liquids, and consume minimal amounts of electricity. The battery life is mentioned as being generally 5 to 15. One could expect a significantly reduced battery life in the presence of a continuous stream of data, but the lifetime should be considerably longer. than the lifetime of the typical mission of a drill bit. The same device can be used with an electric sensor or a magnetic field sensor. In both cases, it is desirable to have some signal processing for the sensor outputs prior to transmission with the IEEE 1902.1 standard device, which is illustrated in Figure 5. Figure 5 is a schematic of circuit elements of a system for having some signal processing for the outputs of sensors located at, in, or near the drill bit. Many of these elements are optional. The circuit may be powered by a battery 517, such as a silver oxide battery. Power from stack 517 may be provided to all system modules from a current processing module 519. Optionally, a current regulator may be added to the circuit - to improve efficiency in the system. the case where it would be necessary to operate other modules in the system at different voltages. The output from an electric field sensor 502, which is identified by a capacitor of FIG. 5, may first be transmitted to an operational amplifier 522 before the signal processing module 523. The operational amplifier 522 may have an input impedance as high as 10 allows practice, since the electric field sensor 502 may be a high impedance device. In most cases, it is desirable that this also allows amplification of the electric field signal. An operational amplifier 526 is also shown to be connected to the output of the magnetic field sensor 503, which is identified by an inductor in FIG. 5, prior to the signal processing module 527. This operational amplifier does not need to have a high impedance, is only optional and, if present, must have a gain sufficient to boost the amplitude of the signal. A device of the IEEE 1902.1 518 standard can communicate by generating a signal that is out of the frequency band that is relevant to various frequencies related to the measurements as described herein. For example, the type device of the IEEE 1902.1 518 standard may communicate at a frequency of about 100 kHz which is out of the relevant frequency band. Nevertheless, depending on the anticipated signal-to-noise ratio, it may be desirable to have additional isolation through low filters or band-pass filters for electric or magnetic field signals. Therefore, several methods can be implemented to provide an electric field signal and a magnetic field signal, processed or raw. Optionally, these signals may be transmitted to a multiplexer 524. The multiplexer 524 may reduce the number of components in the system since it makes possible the use of a single analog-to-digital converter 528 for sampling the signals. electric and magnetic fields. In addition, two analog to digital converters can be included in the system. A clock 529 may be used to synchronize the operation of the multiplexer 524, the analog-to-digital converter 528 and to transfer data to the IEEE.1902.1 device 518. Figure 6 is a schematic diagram of an alternating electric field sensor 602. This sensor can be mounted differently than the electric field sensor of FIGS. 1-4. The model can be adapted from US Pat. No. 5,720,355. Sensor 602 can include a conductive shaft 606 surrounded by an insulator 608 and housed in a housing. pressure housing 604 that can be inserted into a cavity in a drill bit or into a module that serves as an extension to the drill bit. The electric field can be measured by measuring the potential difference between the conductive shaft 606 and the device 602 and the drill bit body. As with the preceding electric field sensor device, an alternative embodiment (not shown) may include a magnetic field sensor in the form of a solenoid around a central electrode. In this case, it is desirable, but not necessary, that the conductive shaft is made of a magnetic material. The signals from these sensors may be transported over conductors to a central signal processing module, where they may be subjected to signal conditioning and possibly transmission through an IEEE-1902.1 type device, as was the case previously. Sensors of this type may be mounted on a module, such as that illustrated in Figure 7, which may also be adopted from US Patent No. 5,720,355. In Figure 7, elements 4-1, 4-2 and 4 3 refer to all the sensors of FIG. 6, whose conductive centers are at the level of elements 2-1, 2-2 and 2-3, respectively. In addition, vibration sensors 4A, 4B and 4C are shown in Figure 7. The vibration sensors can be mounted so that their sensitivity axes are orthogonal or nearly orthogonal. The module of FIG. 7 can be modified as in FIG. 8 to allow the measurement of the potential difference between electrode pairs 2-4, 2-3038070 17 5, 2-6, 2-7. The pairs can be taken either along the bit axis or orthogonal to the bit axis. The module of FIG. 7 can be mounted on a drill bit, such as that illustrated in FIG. 9, which can also be adopted from US Patent No. 5,720,355. The mounting of the associated electrical module can be similar to that of FIG. U.S. Patent No. 5,720,355. Figure 9 may include the electrical sensors 902-1, 902-2, the module 602 of Figure 6, and the vibration sensors 4A, 4B, and 4C shown in Figure 7. Figure 10 is another view of the instrumented drill bit 105 of FIG. 1. In this view, only the electric field sensors 1002-1, 1002-2, 1002-3 are illustrated. This view illustrates the placement of the electric field sensors 1002-1 and 1002-2 above the edges of the drill bit 105, the electric field sensor 1002-3 located in a similar manner to the sensor 102 of FIG. Electrical field 1002-1, 1002-2, 1002-3 can be made in a manner similar or identical to the sensors of Figures 1-4 which can measure the electrical properties. Space 1009 in which the torus and solenoid referenced to, but not illustrated in, Figure 1 are also illustrated. Figure 11 illustrates the construction of a toroid 1103 for sensing the induced current along the drill bit axis. The torus 1103 can be wound around a soft iron or a ferrite core. Such a current is induced by electric fields and by the rotation of the magnetic field of the earth, but mainly by the seismic field coming from the fracturing of the rocks and the flow of drilling fluid in the fracture. Any component induced in the torus by the rotation of the earth's magnetic field can be synchronized with the instantaneous rotation speed of the drill bit as is determined using a MEMS gyroscope. A solenoid 1119 is also illustrated in FIG. 11. The solenoid 1119 may generally have several turns and may consist of one hundred or more turns of copper wire. Solenoid 1119 may be sensitive to the time-varying magnetic field along the drill bit axis. This field comes mainly from the seismoelectric effect. Toroid 1103 and solenoid 3038070 1119 can be used, respectively, as electric field sensors and magnetic field sensors (electric current), together, on a structure. Figure 12 illustrates another embodiment in which an electric field sensor 1202 is installed on the face of a drill bit 1205, similar to but not limited to the drill bit 105 of Figure 1. In this case, a sensor such as that of Figure 6 can be used. This sensor 1202 can be sensitive to a combination of the electric field generated via the piezoelectric effect and the field generated by the seismoelectric effect. In a typical installation, a bit nozzle is plugged and replaced by a sensor 1202. The sensor 1202 may utilize electronic signal conditioning components and IEEE-1902.1 telemetry device to transfer the signal from the 1205 drill bit. to a module where the signal can be analyzed. On the other hand, one or more wires may be passed through the body of the drill bit 1205 and connected between the sensor 1202 and the electronics module. Also illustrated in Figure 12 is a torus 1203 and a solenoid 1219, as in Figures 1, 10 and 11, are installed on the drill bit shank 1205. The solenoid 1219 can be placed to pick up a magnetic field of CA on Along the axis of the drill bit 1205, the torus 1203 can be placed to capture the AC current on the pipe generated by the time varying electric fields. Figure 12 also shows another mounting location for the 1201 vibration sensors installed on the drill bit shank 1205. Three of these 1201 vibration sensors can be implemented to respond to vibrations in the orthogonal directions. Such sensors 1201 are less subject to degradation in this position and can be maintained with very little difficulty. Illustrated with the illustrated sensor 1201 is a 1233 connector at the base of the sensor housing 1234. When the sensor 1201 with the housing 1234 is totally passed through the cavity in the drill bit 1205 designed to receive it, it mates with an electrical connector which is used to transmit a sensor signal and a current (if any) to the sensor 1201. Figure 13 illustrates the face of the drill bit 1205 of Figure 12 with the sensor of FIG. 1202 electric field installed in the plugged nozzle. Before discussing the control aspects, the signals that the devices describe here are designed to receive can be presented. Various levels of signals are described in the literature cited for the electric field from grinding or fragmentation of rocks and drill bits. Very little information is available about the magnetic field in these references. Before proceeding, simple calculations can be made to estimate the order of magnitude of the effects and their distribution in the vicinity of the drill bit. If there are no external electric fields, the piezoelectric effect can simply be described by the relation b = d - T where D represents the displacement vector, d is a vector of the piezoelectric constants (more generally, this is a tensor), and T is a stress tensor. Assuming that the die 15 that is dug is mainly quartz, the main value of the piezoelectric constant is 2.3 10-12 C / N. (See "Experiments to show piezoelectric and pyroelectric effects," Jeff Erhart, Physics Education, 48 (4), 2013, IOP Publishing Ltd., Table 1). The weight on the bit can vary considerably. A value of 25,000 pounds is reasonable, and is not a low number for this analysis. With a bit diameter of 8, 5 inches and using a scalar form of the equation, T = 8500 * lb * 4.44822162 * N * lb-1 * (4.25 * .0254) 2 * m2 - Electric fields are given by E = D / so where £ 0 represents the permittivity of the free space, 10-9 / (367c) Farads / m. From this, the electric field strength is estimated at about 270,000 V / m. However, as noted in the introductory comments, the matrix is not a pure quartz crystal. Individual grains are randomly oriented. In accordance with experimental results reported in the literature, it is anticipated that, on average, the orientation of the crystals will not cancel each other out; there will be some favorite net directions, and this is also consistent with most geological situations. Even if there is an excess of only 0.01% of the grains in this preferred orientation, this will give us a field of 27 V / m. This does not represent the entire story either.
[0018] For a good approximation, the field decreases inversely to the cube of the distance from the source. In addition, the only signal that can be detected is the dynamic component, which probably represents no more than 10% of the static component under good drilling conditions. This brings the signal back to 2.7 V / m at the source. Due to the high electrical conductivity of the drill bit and a design requirement to protect the scrap sensors from abrasion and shocks, a substantial portion of the E-field sensor is shielded. With an overall sensor length of 2 cm, it can be expected that the voltage detected through these sensors is of the order of a few millivolts for the sensors in the environment close to the bit teeth, such as those illustrated. In Figures 1, the base of Figure 10 and Figures 12 and 13. The other electric field sensors described herein will not respond significantly to the piezoelectric component of the signal. Electric field sensors in the near-bit environment will be sensitive to piezoelectric fields from fragmentation of the rocks, the piezoelectric field arising from breakage of bit teeth and seismic field. An acoustic correlation can be expected with each of these. When a fracture of rock or teeth becomes imminent, the piezoelectric signature increases abruptly. Once the break is achieved, the flow of conductive fluid in the break zone causes the formation of a seismoelectric field, with a longer decomposition rate. In the reported results, the seismic field is significantly stronger than the piezoelectric field (depending on the location, in order of magnitude). From the estimates of the piezoelectric field, it is reasonable to expect that the observed seismoelectric signatures are of the order of 27 V / m (peak).
[0019] A rough estimate of the magnetic field that can be expected can be made using the relation of the impedance between the magnetic and electrical fields, which is represented by H = E / Z, where H represents the force of the field Magnet in amperes / meter and E represents the force of the electric field in volts / meter and Z is called the characteristic impedance of the medium in which the electromagnetic waves propagate. This relationship is true only for plane waves, but can be used to give an estimate of expected field strengths. For free space, Z = 377 Ohms. For most wellbore materials, the magnitude of Z is considerably smaller (and the value of Z is complex). Thus, using the value of the free space of Z, the estimate will tend to be pessimistic. Assuming that the seismoelectric effect is of an order of magnitude greater than the piezoelectric effect at a distance of a source radius from the point of generation of the field, and by multiplying the force of the magnetic field by the magnetic permeability of free space (which is typical for downhole formations), a magnetic field of 90 nT is obtained. Thus, magnetic signals in the range of about 1 to 100 nT can be expected depending on the distance to the bottom of the wellbore. The correlation of acoustic signatures with electrical and magnetic signatures serves to differentiate between sources. By comparing the correlations of the acoustic signatures with electrical and / or magnetic signatures near the bit face with the correlations of electrical and / or magnetic signatures remote from the bit, the piezoelectric effect can be identified because it will not be present. only in signals close to the trephine. (Further teachings are provided here with respect to the piezoelectric effect in the shale and with respect to a mechanism for improving the detection of the piezoelectric effect). A piezoelectric pulse in the absence of a corresponding seismic radiation peak (or with a very weak seismoelectric component) is due to the breaking of a bit tooth. The reason for this is that the seismic effect does not work when this occurs, and even if the tooth breaks only, the high electrical conductivity of the bits and teeth will suppress any seismic signal. A piezoelectric signal with a seismoelectric signal is an indication of the breaking of a rock. It should be noted that, given the fact that the piezoelectric and sismoelectric signals have different characteristic spectra, it is not strictly necessary to compare the signals close to the bit face with those close to the tail of the bit, but can be expected to improve signal-to-noise rejection in the latter case. Similarly, it is not strictly necessary to correlate the electrical and magnetic signals with the acoustic signals, but the signal-to-noise ratio, and thus the performance estimate of the bit, is improved by doing this. The amplitudes of the piezoelectric and seismoelectric events are an indicator of drill bit wear: the greater the amplitude (and the steeper the signal increase), the more sharp the bit is. In addition, the statistics of piezoelectric or seismoelectric events serve as key to the performance of the bit. If the statistics are more or less regular with little variation, the bit does not perform well. High performance occurs at a threshold between regular and somewhat erratic statistics in the fragmentation signatures of rocks. Signal analysis and optimization of drilling performance, interspectrum and power spectrum, interspectrum, autocorrelation and cross-correlation.
[0020] In various embodiments, the outputs of the various sensors, as previously described, can be correlated to obtain indicators of bit wear and drilling efficiency. Power and cross-power spectra are also very powerful indicators of drill bit wear and drilling efficiency. The power spectrum, i.e., the power spectral density (PSD), of a method is defined as the Fourier transform of the expected value of the autocorrelation of this method. Similarly, the cross-spectrum of two methods, i.e., the cross-power spectral density, is defined as the Fourier transform of the expected value of the cross-correlation of these methods. While useful information can be obtained by analyzing autocorrelations and cross-correlations, power spectra and cross-power spectra are very powerful tools for analyzing the types of methods described herein. From the literature, a general approximation expression for the acoustic and electromagnetic signature of an individual rock break or drill bit at time t = 0 can be represented by 5 fjt] = A * ett fit] = A * e * Cos [w * t] t> 0 In this expression, A represents an amplitude that varies from one break event to another, i and u represent the characteristic time periods, which are also random variables , and wo is a characteristic frequency for the process, which is also a random variable. The parameter r is associated with the accumulation of stress in the rock (or bit), while the parameter represents a characteristic time scale for the duration of the tingle after breakage of the rock (or bit). The parameters vary significantly depending on whether the material is broken and rock or whether it is the material in which is made the bit tooth (PDC diamond). In the case of a drill bit tooth, based on the literature, it is sufficient to assume that u = 0 and that there is therefore no characteristic frequency. Although the ringing can not be completely avoided, it is expected to have a significantly higher frequency than the ringing in the rock. Figure 14 is a representation of an example of an oscillating pulse, an acoustic waveform or EM, due to breakage of a rock fragment. The parameters are based on values published in "Experimental Studies of Electrical Fields on a Breaking Rock Sample", Zhenya Zhu, F. Dale Morgan, Chris J. Marone, and Mr. Nafi Toksoz, Earth Resources Laboratory Department of Earth, Atmospheric, and Planetary Sciences Massachusetts Institute of Technology, Cambridge, MA 02139. In Figure 14, the characteristic frequency is 300 Hz, when the amplitude units are arbitrary. The importance of PSD and cross-power spectral densities in the analysis of data obtained using the instruments described herein will be explained as the properties of these spectral densities are explained. (A derivation of power spectral density derivation of a process consisting of a pulse superposition from the breaking of a rock and pulses from the breaking of a bit is presented in the section called Appendix I If only the break times are random, the PSD for such a process is given by PSD [co] = PR * AR (v R2 + VR4CO2 + Rv R (1 + v, 2 (a) 2 + coR2) ) + 2 (1+ vR4 4 -I- 2 ± 2 COR 2) R 276 (1 + z R2 co2) (1 + vR4 (co2-coR2) 2 + 2vR2 (w2 + coR2)) + PR * AB2 * TB2 In which, the index "R" describes the rock, and the index "B" describes the trephine.
[0021] Deriving this expression, it has been assumed that breaking of the rock and the bit tooth are methods independent of the Poisson distribution with the speed parameters pR and pB. Several PSD graphs are shown in Figures 15, 16 and 17. All time units in these figures are in seconds, the frequencies are in Hertz and the amplitude units are arbitrary.
[0022] The peak spectral peak of Figures 15, 16 and 17 is due to the breaking of the rock component. The parameters that have not been varied in these graphs were obtained from the reference Zhu et al mentioned above. Figure 15 is a set of PSD graphs for different values of a characteristic time, UR, for the decomposition of the tingling after fracturing of the rock. In Figure 15, the spectral peak becomes wider and lower as the uR value decreases. This is logical since a diminishing value of uR corresponds to a faster decomposition of the characteristic ringing. As the bit becomes less sharp, the decomposition time decreases. This results in an enlargement of the spectral peak and a decrease in its amplitude.
[0023] Figure 16 is a PSD graph set for different values of a parameter which is a characteristic time, CR, for the accumulation of stress with respect to the rock. Figure 16 illustrates the variation of the PSD as a function of the TR parameter. As the duration of TR increases, the low frequency portion of the PSD 271-1 + 1R2 * co2 3038070 increases. It should be noted that the low frequency part is more or less flat below 10 Hz for all the cases studied. From these, it would seem that this low-frequency characteristic of the PSD provides an indicator of TR, and it does, but there is one condition.
[0024] Figure 17 is a PSD graph set for different values of a parameter which is a characteristic time for the accumulation of stress with respect to the drill bit. Figure 17 illustrates the variation of the PSD as a function of the TB parameter. Clearly, if bit break events are present with rock break events, the two can not be differentiated.
[0025] In addition, care must be taken to understand the statistical nature of the processes before drawing any conclusion. Other lessons will be offered to demonstrate how this problem can be solved. Before further discussing the statistical nature of the methods, consider the following comments regarding autocorrelations and cross-correlations with respect to Figures 18-22. Figure 18 is a graph set in a time segment of a Monte Carlo simulation of the methods described herein. Curve 1856 represents a signal that can be received, e.g., at a magnetic field sensor. Curve 1857, shown for clarity as being moved vertically by one unit of amplitude with respect to curve 1856, is representative, for example, of a signal that can be received by an accelerometer. Curve 1857 contains a time-shifted version of curve 1856 with additive noise from a process very similar to the process that produced curve 1856. Figure 19 is a set of autocorrelation graphs associated with the received signal. FIG. 19 includes an autocorrelation graph of curve 1856 of FIG. 18, the cross correlation of curve 1856 with the noise method that was introduced to produce curve 1857 in FIG. 18, and the cross-correlation between curves 1856 and 1857 of Figure 18. An enlarged view of Figure 19 is shown in Figure 20.
[0026] As expected, the autocorrelation produces a peak with a time shift of 3038070-260. The cross-correlation demonstrates a prominent peak close to that of the autocorrelation. It also shows a strong peak at the largest possible offset value, which represents an artifact of how cross correlation was calculated. In Figure 20, it can be seen that the peak in the cross-correlation 2056 which is similar to the peak in autocorrelation 2057 is shifted by 10 ms, which represents the value introduced in the analysis. There is a curve 2053 along the axis around the value of 0 in Figure 20 which is a cross correlation between the signal of curve 1856 and the noise introduced during the production of curve 1857. As can be seen to see it, the noise is strongly suppressed by the cross-correlation. In the same way that spectral graphs can be used, graphs of this type can be used to track the performance of the bit. When the bit loses its edge, the character of the cross-correlation (or autocorrelation) changes. These examples are shown in Figures 21 and 22. These figures demonstrate the autocorrelation 2057 of Figures 19 and 20 compared to the autocorrelation 4157 of the last signal illustrated in Figure 41, which is characteristic of a catastrophic bit failure. . As can be seen, the components of the long offset of the autocorrelation have recovered considerably. Furthermore, in Fig. 22, which is an enlarged view of Fig. 21, the character of the autocorrelation has fundamentally changed in that the oscillations in the central peak have virtually disappeared. Until the catastrophic failure of the bit, it appears that very little information can be obtained directly concerning the breakage of a bit tooth from the examination of a PSD, a crossed PSD, a autocorrelation and cross correlation. There is much to be learned about rock and rock / bit interaction (especially from spectral measurements), which is related to breakage of the bit. Before discussing these topics further, some of the underlying assumptions in the analysis, as described so far, need to be revisited and qualified. Other materials for this part of the discussion are given below in the sections "Notes on the statistical nature of the signature parameters", Appendix I and Appendix II.
[0027] For a given lithology and bit state, the AR, AB, PR, PB, tR, TB, p.R and ohz parameters are all random variables. How are the results of the aforementioned analysis affected by this Note that the square modulus of the Fournier transform of a single signal is often called a "power spectrum," but this is not accurate (and a similar statement is given for the cross-spectrum). The density of a power spectrum of a process represents the expected value of the Fourier transform and its autocorrelation. An underlying concept in the definition of a power spectrum is the notion of an ensemble average. The measurements of the signal under analysis can be visualized as a set of measurements made on systems having the same statistical properties as the system of interest. The autocorrelation is taken for each measure of the set. It is more efficient to calculate the Fourier transform of the autocorrelation of each measurement in the set since these represent the square module of the Fourier transform for each measurement. An average is then taken on all the measurements. For processes that do not change over time, ie ergodic processes, the average of the set can be replaced by averaging windows over time. Windows are not necessarily overlapping. This procedure can also be performed with processes that vary slowly. Similar concepts apply to spectral densities of cross-power. Since power and spectral cross-power densities take into account the stochastic properties of signals, they can be used as global measures of drilling performance. Here, the use of the term "global" distinguishes between time domain measurements, when the time domain measurements are based on individual time series or cross correlations of individual time series, which provide comparatively less time domain measurements. complete information on the processes underlying the observed time series. Among other things, Appendix II discusses the effects of the randomness of the variable (AR) It should be noted that as the standard deviation of the frequencies r increases, the spectral distribution widens around of its peak (which is not surprising) There is also some reduction in the amplitude of the peak when the standard deviation of coR increases The other parameter which has an effect on the width and the amplitude When the DR decreases, the amplitude of the spectral peak decreases, but it never widens outside the envelope of the sharpest spectral peak possible.These behaviors are described both analytically and by Monte-Carlo analysis, the significance of these is as follows: as vR decreases, the drilling efficiency decreases and the size of the cuttings decreases.When the efficiency of drilling 10 progressively decreases, the spectral peak fall, but stays inside the original envelope On the other hand, an enlargement of the spectral peak with a small fall in amplitude corresponds to a condition in which the characteristic frequency varies more when the rock is destroyed. As previously noted, an increase in the variation of this frequency is an indication of an improvement in drilling efficiency, and vice versa. In some literature references, it is also noted that the characteristic frequency increases as bit efficiency decreases. It is also reasonable to think that the characteristic accumulation time until rock failure increases as the bit becomes less sharp. Through the Monte-Carlo analysis, Appendix II considers the variation of all the parameters of the model. This analysis confirms the above statements. For a given grade of cutting edge and given properties of the slurry, the drilling efficiency is a function of the weight on the bit, the speed of rotation, the flow of fluid through the bit as well as the weight of the sludge and rheology of the sludge. As demonstrated, the first three of these parameters can be individually controlled at the bottom of the well, even though the electromechanical mechanisms must be added to some conventional devices in order to use them with respect to the various embodiments taught herein. As is also well known, these parameters can be controlled from the drilling rig, but with a considerable lag in the response time and in the accuracy of the control. Methods for determining drilling efficiency and bit wear described herein may be used with a downhole controller, and communication links to downhole means for controlling weight on the bit, the rotational speed and flow rate to find and maintain optimum drilling efficiency. This architectural scenario is illustrated in Figures 23 - 28, which also include some features appropriate for a more general approach. Figure 23 is a diagram of a system structured to operate with respect to optimization of drilling efficiency. Surface equipment 2360 may include a MWD 2364 telemetry system, typically with an uplink and a downlink to allow communication between the well bed and the surface. It also includes a module 2363 having a hook load detector, a rotational speed detector and a flow controller. The flow controller can generally be accomplished by controlling the speed of the slurry pump (s). The surface equipment 2360 together may comprise a user interface 2368 and a processor 2366. The processor 2366 may be structured to control the operation of the sensor and / or the drilling operation and to process data from the sensors, as it is described here. In some embodiments, the information collected at the bottom of the well by the bit sensors, and possibly with other sensors above the bit, may be telemetrically transmitted to the surface unit 2360 and may be used to optimize the hook load (for bit weight control), rotation speed and throughput. In other embodiments, these three parameters can be dynamically controlled at the bottom of the well using the same information. In implementations in which bit weight (WOB), rotational speed (RS), and bit flow (Q) are dynamically modified at the bottom of the well to optimize drilling efficiency in real time the hook weight must first be set to a value corresponding to the maximum values of the WOB and Q that will be used during the time intervals 3038070 when the downhole system automatically controls the drilling efficiency. If a Positive Displacement Drill Motor (PDM) is used, the surface and Q must be defined so that the anticipated maximum downhole SAR can be obtained by the downhole system. If there is no such PDM or motor, the rotational speed can be controlled by communicating with the surface unit 2360, even when a rotating steerable tool is in the system. Shown at the bottom of the well in Fig. 23 is an instrumented drill bit 2305, as previously described, a mud motor or turbine 2371, a MWD / LWD system with a downhole treatment unit 10 2372, a WOB controller 2373, a Q (flow) controller 2374 which may include a vent 2378, and a rotational speed controller 2376, as previously described. A communication bus 2377 is also partially illustrated. The communication bus 2377 may include portions that are common to all elements of the MWD / LWD 2372 system, which may be wired with connectors between the elements. The communication bus 2377 may include portions that are connected with wires and connectors, acoustic communication links, and EM communication links. For example, an EM communication link may be used to transmit instrumented drill bit 2305 to the MWD / LWD 2372 system, or it may be wired through the mud motor 2371, or it may be wired to an acoustic link which transfers data through the mud motor 2371 to the MWD / LWD 2372. Similarly, the WOB controller 2373, the flow controller 2374 and the rotational speed controller 2376 can be connected by cable, by short EM or short acoustic connections. It should be noted that the operation of the flow controller 2374 can be coordinated with the operations of the rotational speed controller 2376, depending on the type of rotational speed controller 2376 used. If the rotational speed controller 2376 deflects the flow into the wellbore ring, then the deviated flow must be taken into account when setting the flow controller 2374, otherwise it should not be taken into account. The controller WQ.E3 2373, the flow controller 2374 and the system MWD / LWD 2372 can be in a different order than that shown in FIG. 23. FIG. 24 is a flowchart of a system structured to operate by related to the optimization of drilling efficiency, similar or identical to the exemplary system of FIG. 23 in which the components are described in more detail and more generally in FIG. 24. All the elements illustrated in FIG. 24 need not be present in a given embodiment described herein. The unit on the surface can be implemented as previously described. Figure 24 illustrates an input of the vibration sensor 2481 from the downhole vibration sensors, which may include accelerometers, an E-field sensor input 2482, E-field sensors, and an input of magnetic field sensor 2483 from the magnetic field sensors, when these sensors are at or near the drill bit as previously described. Also shown are WOB inputs 2486 from a downhole WOB sensor, a speed sensor input 2484 from a rotational speed sensor, an input from the torque sensor 2488 from one or more downhole torque sensors, an input of the flow sensor 2487 from a flow sensor and an input of the bending moment sensor 2489 from the downhole bending moment sensors. It is sometimes useful to measure downhole torque at several locations along the drill string. All these measurements can be made using commercially available equipment such as the. DrillDOC® Drilling Downhole Optimization Collar, available from Halliburton Energy Services, Inc. It should be noted that the various inputs may come from one or more of these sensors.
[0028] Also illustrated in FIG. 24 are well bottomed well formation (FE) sensor inputs 2491 from downhole FE sensors. These may include, for example, any number of types of resistivity sensors, acoustic sensors, nuclear sensors and RHIN-based sensors. In addition, a 2493 input from the downhole MWD calibres may be available, such as, without limitation, acoustic gauges. All of these measurements can be used to derive lithology from the formation, which can be used in the calculation of drilling efficiency in a more general embodiment of the present description to be discussed. In addition, the penetration velocity (ROP) can be derived from the correlated output of the shallow reading sensors having a known spacing between the actual centers of their measurement zones, as described, e.g. U.S. Patent No. 5,899,958. The ROP can also be estimated by dividing the estimated time between drilling breaks by the estimated length of a section of pipes added to the drill string. A ROP 2492 input can be provided.
[0029] A break in the bore may include the case where the drill is stopped and a section of pipe is added to the drill string, the pipe section can generally be between 30 and 90 feet. However, this procedure does not give an instantaneous value. In addition, the estimated ROP can be provided by downlink values measured at the surface. If this is done, the values must be corrected for WOB, sludge weight and friction. As noted above, the various inputs may be provided by one or more respective sensors of this type. A downhole processor 2495 may include hardware for communicating with the MWD uplink and a downhole receiver 2477; a module for defining WOB, RS and Q; a 2497 search module to undertake an optimal WOB, RS and Q search; and a calculation module 2498 for calculating bit efficiency or sufficient parameters related to bit efficiency to enable optimization of drilling efficiency through a search parameter algorithm of search module 2497. A downhole WOB 2494 control mechanism, a downhole speed control (RS) 2496 mechanism, a downhole flow control mechanism (Q) can provide an input to a set of parameters 2499 that the downhole processor 2490 can operate 2495. The parameter set 2490 may also include an input from the search module 2497.
[0030] The system of FIG. 24 may comprise a slurry tank 2457 with a mud flow in the hole of the drill pipe which is provided with a pump 2458. The flow of sludge through the hole of the drill pipe can be associated with a downhole MWD uplink transmitter / downlink receiver 2477 and an MWD downlink receiver / downlink transmitter associated with surface 2462. Similar or identical to the MWD telemetry system 2362 of Figure 23, the uplink receiver / downlink transmitter MWD associated with the surface 2462 can operate with a surface unit 2460 having a user interface 2468 and a processor 2466.
[0031] The surface unit 2460, the user interface 2468, and the processor 2466 may be the same or the same as the unit 2360, the user interface 2368, and the processor 2366 of Fig. 23. The processor 2466 may operate to provide a hook load setting point 2467-1 to hook load controller 2467, to provide a speed setting point 2468-1 to the surface speed controller 2468, and to provide a 2469-1 flow set point to the 2469 flow controller at the surface. The 2469 surface flow controller can be a pump speed controller. Figure 25 is a flowchart of an example of the efficiency calculation module 2598. The exemplary illustrated exemplary module 2598 does not calculate the effectiveness of the bit, but provides sufficient information to the search algorithm. parameters to allow optimization of the efficiency. This routine depends on the experimental results presented elsewhere in this document. Since these results constitute a limited subset of the possible behaviors of rocks and drill bits during breakage, this is only one specific example of the more general technique to be described later. In the specific technique referenced in Figure 25, it is assumed that the PSD tends to have a prominent spectral peak, which is characteristic of the breaking of the rock. It is assumed that the center frequency of this spectral peak, as well as the width around this center frequency, increases as bit efficiency decreases. In addition, it is assumed that the amplitude of the PSD 3038070 34 in the low frequency limit increases as drilling efficiency decreases and increases rapidly as the drill bit approaches the point. In order to maintain the simplicity of this diagram, only two sensors are used in this analysis, a vibration sensor and an E-field sensor. Additional sensors, as previously mentioned, may be included in a manner similar to that illustrated herein. In addition to the center frequencies of the dominant spectral peak, and the width of these spectral peaks in the spectral power and cross power densities, it is important to have an estimate of the standard deviation at these parameters. In addition, if other spectrally significant peaks are identifiable, it is important to follow these. It is also useful to follow the low frequency limit of power and cross-power spectral densities and the estimated standard deviation of this parameter. Various spectral parameters can be followed in this module. Such spectral parameters which can be reported are given in Table 1: SPECTRAL PARAMETERS Central frequency of the spectral peak with the highest amplitude Low frequency at mid-width of the spectral peak with the highest amplitude High frequency at mid -spectral peak width with the highest amplitude Low frequency amplitude limit Estimated standard deviation of the amplitude of the highest spectral peak Estimated standard deviation of the frequency of the spectral peak with the highest amplitude Estimated standard deviation from low frequency to mid-width of the spectral peak with the highest amplitude Estimated high frequency standard deviation at mid-width of the spectral peak with the highest amplitude Estimated standard deviation of the expe- low frequency amplitude 2nd, 3rd and highest peak spectral peaks 2 ", 3" and 4th highest peak spectral peaks 3038070 Standard deviation of amplitudes of d u 2nd, 38th and eine highest spectral peaks Frequency standard deviation of the 2nd, 38th and 4th highest spectral peaks Amplitudes of power spectral densities in the low frequency limit Estimated standard deviation in the amplitude of the spectral densities The power spectral densities and the cross-power spectral densities can be estimated by any number of methods, including, without limitation, any of Burg's multitaper (MTM) process. , multiple-signal classification (MUSIC), Welch or Yule-Walker autoregressive techniques. It is important to understand that these densities are never fully "measured", but are only estimated because they are statistical parameters. Estimates can be obtained using a range of sample speeds, window lengths, and the number of samples overlapping in successive windows. From these, it is possible to develop a series of cross-spectral and spectral estimates from which the standard deviation can be estimated at any frequency. Thus, as shown in Figure 25, a sampling rate is defined. This must be at least twice the frequency of the component with the highest frequency that would be present in the signals to be processed. After this, the size of the window must be specified, and then the particular algorithm chosen for the power spectrum estimation, with the necessary parameters for the algorithm (such as the number of overlapping samples).
[0032] In successive frames, series of power spectra and cross power spectra can be created for the vibration and E-field sensors. These successive spectral estimates are stored in a buffer. A pre-specified number of spectral peaks is then located in each of the estimated buffered power spectra. For the example of Figure 25, up to 4 spectral peaks are identified for each estimated power spectrum 3038070 in the buffer. Some details are left to implementation or can be determined by well-established experimental techniques, such as the technique for identifying spectral peaks, and to determine if there are, in fact, at least as many spectral peaks as the maximum specified (and if this is not the case, it should be noted that the maximum number has not been observed). An analysis is then performed on the spectral peaks at all power and cross-power spectra to identify a mean center frequency for the specified number of spectral peaks and the standard deviation in the frequency around these averages, and the standard deviation in the magnitude of these averages. Likewise, the low frequency limit of the amplitude and the standard deviation of this amplitude can be determined for each of the estimated power and cross power spectra. These parameters may be provided to a search algorithm, eg, the search algorithm of Figure 26. The flow of operations of Figure 25 may include the specification of the size of the sample window, SW, at the level of 2501, the specification of the parameters for the actor's estimate of the power spectrum at the level of 2502, the specification of the historical buffer length of the power spectrum at the level of 2503, and the specification- parameters for the buffers of processing of the windowed power spectra at the level of 2504. These activities can be performed as in Figure 24 can vibration sensor and an electric field sensor, in which activities can include, at 2505, the sampling rate of the sensor, SR, specified. At 2510, the vibration sensor is sampled at SR speed. At 2515, the electric field sensor is sampled at SR speed. At 2520, the 25 samples SW of the output of the vibration sensor is taken. At 2525, the SW samples of the E-field sensor output is taken. At 2530, a power spectrum from the windowed samples of the vibration sensor is calculated. At 2535, a power spectrum from the windowed samples of the electric field sensor is calculated. At 2540, a cross-power spectrum from the windowed samples of the vibration sensor 3038070 37 of the electric field sensor is calculated. At 2545, the spectrum is recorded in a windowed power spectrum buffer of the vibration sensor. At 2550, the spectrum is recorded in a windowed power spectrum buffer of the electric field sensor. At 2555, the cross-spectrum is recorded in the windowed power spectrum buffer of the vibration and electric field sensors. At 2560, the processed power spectra of the vibration sensor signals are produced. At 2565, the processed power spectra of the electric field sensor signals are produced. At 2570, the cross-power spectra processed from the vibration sensor and E-field signals are generated. At 2575, relative to the processed power spectra of the vibration sensor signals, the spectral parameters are estimated and reported to a parameter search algorithm. At 2580, relative to the processed power spectra of the electric field sensor signals, the spectral parameters are estimated and reported to a parameter search algorithm. At 2585, relative to the processed cross-power spectra of the E-field sensor signals, the spectral parameters are estimated and reported to a parameter search algorithm. The types and spectral parameters reported to the parameter search algorithm may include spectral parameters selected from Table 1. Figure 26 is a flowchart of an example of a search routine 2697 for defining drilling parameters for optimum efficiency using the inputs from the efficiency calculation module 2598 of FIG. 25. The search algorithm 2597 of FIG. 26 can be designed to determine the parameters of the WOB, RS and Q which optimize the efficiency of the bit. Several other ways are possible to search for these parameters. The illustrated routine is conservative in that it attempts to begin estimating the optimal position in space (WOB, RS, Q) using a previous best estimate of these parameters and taking steps of these parameters having a Pre-specified size to determine a gradient of a parameter that increases as drilling efficiency decreases, tracking this gradient to an optimum operating point, keeping the operation around that point as far as possible. it is noted that it is no longer an optimal operating point, and the initiation of new research when this occurs. The algorithm operates to maintain the values of (WOB, RS, Q) within the specified maximums and minimums. Before initiating a search, maximum and minimum values can be specified for (WOB, RS, Q). These can be default values in the system or values received through downlink telemetry. In addition, a maximum DT dwell time can be specified. This corresponds to the time during which the drilling data is acquired without modification of (WOB, RS, Q). The size of the step for each (WOB, RW, Q) can be specified, and in the same way, an initial value defined for the (WOB, RS, Q). The size of the step may be specified by default, a value from a previous use of the routine or downlink telemetry. The search for optimal parameters can begin by defining a label to indicate that a gradient search is to be performed. At 2605, a gradient search tag is set to equal "true". This can be the default value of the routine entry. At 2610, a decision is made to determine if a previous value is available for each rotational speed, bit weight, and bit throughput. Then, if previous values are available for (WOB, RS, Q), the values stored in the search routine are set to these values, at 2615. At 2620, if not, WOB, RS and Q are set to their initial values, which may be the initial default values. In addition, a label for a mode defined as "cruise" mode, in which drilling is performed without changing parameters until it is determined that the drilling efficiency is no longer optimal, is set to "false". ". Then the sensor outputs are sampled and an input provided. At 2625, the efficiency module 30 is controlled to initiate sampling of the sensor outputs.
[0033] 3038070 39 The input may come from processing routines and hardware similar to or similar to such entities as described in Figures 24 and 25. Drilling is then maintained at the (WOB, RS, Q) set of values. for a time period of DT, at 2630. After this, the efficiency parameters can be calculated as described with respect to Figure 25, in this case, or using a more general procedure described above. below. At 2635, the efficiency module is probed for efficiency parameters and these are stored as an initial set of efficiency parameters. The inputs may be spectral parameters that can be reported from the drill efficiency module of Figure 25, which may include the parameters shown above in Table 1 with respect to Figure 25. At the 2640 level, a determination is made to know if this is the first set of spectra in a series. If this is the first time that the spectra have been calculated, it is possible to enter the gradient search mode at 2645, e.g., as described in more detail with respect to FIG. Gradient search is to find the direction in space (WOB, RS, Q) in which the rate of increase in drilling efficiency is maximum. Once the gradient search has been completed, the power and cross-power spectral densities can be analyzed, in a manner as associated with Figure 27, in which the highest amplitude spectral peak is identified, with the width of this peak. The width of a peak is the frequency separation between the two half-points of power on either side of the peak. At 2650, power spectral densities and cross-power spectral density are examined (refer to Fig. 27) and the highest spectral peak and half-power spread of the highest spectral peak 25 are calculated from the cross-spectrum. The next set of drilling parameters can be defined by taking or estimating the gradient of a cost function at 2655. This cost function is usually pre-specified before drilling, but could be downloaded by downlink telemetry or even learned in 30 situ. In the specific example that is described, the cost function is given by 3038070 C (WOB, RS, Q) -fc (WOB, RS, Q) + μ-Ofc (WOB, RS, Q), where k> 0 and g> 0 are the weighting factors in the cost function and fc is an empirically derived function of WOB, RS and Q (eg, FC could be a center frequency in the spectral peak). Nominally, X and g may be equal, but the experiment at a given lithology may allow the determination of better values for these parameters. A more general form of the cost function is C '(WOB, RS, Q) X'-fca (WOB, RS, Q) + μ'-Afca (WOB, RS, Q), where k> 0 and g> 0 are the weighting factors in the cost function and a> 0 10 and 13> 0. In general, the cost function should be designed to increase when drilling efficiency decreases to impose a penalty. for an inefficient operation. The strategy is to vary the drilling parameters to minimize the cost function, and this can be done through a gradient search. Clearly, it would also be possible to define a "cost function" which increases with the effectiveness of the bit, in which case a maximum of the cost function is sought. The cost function can be specified as above, eg C [WOB, RS, Q] = I fc [WOB, RS, Q] + Afc [WOB, RS, Q] where 1.4 I> 0 20 WOB e Weight on the bit RS e Rotational speed Q -m Throughput through the bit fc = Central frequency of the spectral peak with the highest amplitude 25 Afc = Half-power spread of the spectral peak around fc Calculate a modified gradient of the cost function that always points in the direction of increasing operating cost (other cost functions can be identified so that the decrease direction could be used).
[0034] 3038070 41 OC = (2 * a fc +, u * a Afc) W OB + a W OB a WOB (2 * fc +, u * AfcjilS '42 * a fc + * -a Afc) -0 aRs a aQ aQ WOB is a unit vector along the weight axis on the RS is a unit vector along the axis of the velocity of (2 is a unit vector on the bit rate through the axis at 2660 level, using the gradient, the step sizes for WOB, RS and Q are calculated The magnitude of each of these three terms is compared with the estimated standard deviation at that term and a suitable step size change. for each of the three terms is determined The value of the DT is adjusted, if it is controlled by the statistics The cruise mode label is set to the appropriate state (refer to Figure 28).
[0035] From the estimated gradient of the cost function, the sizes of the step (with the appropriate algebraic sign) can be calculated in the WOB, RS and Q so that the cost function must decrease when the (WOB, RS , Q) are modified by these values. Depending on the size of the step and the complexity of the functional variation of the central peaks and the spreading of the central peaks with these parameters (WOB, RS, Q), this may or may not occur. In addition, errors can be estimated at the WOB, RS, and Q step sizes. Since it is potentially counterproductive to step in the wrong direction, step sizes can be compared to their errors. before taking this step. In one approach, if the magnitude of the error is less than 0.5 of the magnitude of the calculated step size, the size of the calculated step can be used. No change will be made to a parameter that does not meet this criterion, ie, the calculated step size in this parameter will be set to 0. The choice of 0.5 is somewhat arbitrary and can be chosen anywhere between 0.1 and 1. The rotation trephine system 3038070 42 can continue to operate in a gradient search mode until all step sizes have been set to 0. this point, the system is put into cruise mode. In cruise mode, the value of (WOB, RS, Q) is not changed, but the efficiency is monitored and the gradient estimates and the proposed step sizes are calculated. The cruise mode is exited if the step size of any of the parameters (WOB, RS, Q) is not zero and is statistically significant compared to its estimated error. In one sense, then, the system is still in the gradient mode, but this mode is suppressed when the available information is insufficient to justify a change in the operating parameters. This can happen if the measured noise is high, the training does not follow the assumed model in the cost function with enough fidelity to allow the control of the efficiency through this cost function, or an optimal performance has been achieved and she is maintained.
[0036] At 2665, a determination is made to check whether the cruise mode label is equal to "false". If this is not the case, the procedure returns to 2625. If this is the case, the WOB, RS and Q are modified by the step sizes calculated using the gradient and the standard deviations, and then the procedure 2625, at which the efficiency module is commanded to initiate sampling of the sensor outputs. Fig. 27 is a flowchart of an embodiment of an exemplary procedure for examining power spectral densities and cross-power spectral density. This procedure may flow from the procedure of FIG. 26. The highest spectral peak and the half-power spread of the highest spectral peak are determined from the crossover spectrum. In particular, three frequencies are determined: f1, f2 and f3, at 2710. Respectively, these are the locations of the maximum amplitude of the power spectra of the vibration sensor, the E-field sensor, and the cross power spectrum of these two sensors, wherein fl represents the location of the maximum amplitude power spectral peak of the vibration sensor, f2 represents the location of the maximum amplitude of the power spectral peak of the EM sensor and f3 represent the location of the maximum amplitude of the cross-power spectral peak of the vibration and EM sensors. Three frequency differences can be determined from its frequencies: Df1,2 = fl - f2 Df1,3 = ff3 Df2,3 = f2 f3. As determined at 2710, the standard deviation of the location of fl is sdl; the standard deviation of the location of f2, sd3 and the standard deviation of the location of f3, sd3.
[0037] Each of these frequency differences can be compared with an estimate of its standard deviation. If the estimated standard deviation in all the differences is greater than 0.5 by their estimated standard deviations, then the frequency peak fc3 corresponding to the cross-spectrum can be used in the analysis and the spread of the frequency can be taken to be the half-power spread around fc3.
[0038] When fc3 is chosen, there is an indication that the spectral vibration densities and the E-field spectral densities have a common and dominant spectral peak, and therefore the correlation must be less affected by noise than either the other separate spectra. In addition, this is an indication that the formation is permeable, and therefore a label can be defined to provide this information. In the case where a common spectral peak is not identified, then the system makes use of the fl, the most prominent peak in the PSD of the vibration, and the half-power spread around this peak is calculated and used in the calculation of efficiency. It should be noted that in the method associated with Figure 27, the variances can be used in the comparison rather than the standard deviations. This simply avoids using a square root. In addition, the low frequency limit of the appropriate PSD can be examined to determine if there is a significant increase in its magnitude. If this is the case, this is a sign of impending bit failure, and a tag is set to issue a warning. In an embodiment at 2720, a determination is made as to whether Df1,22> .25 * (sd12 + sd22) and Dfl, 32> .25 * (sd12 + sd32) and 3038070 44 Df2,32> .25 * (sd22 + sd22). If this is the case at 2740, use the current f3, fc3, in the analysis; identify the formation as a permeable formation and calculate the half power spread in the center frequency using the upper and lower half power points of the crossover spectrum. If this is not the case at 2730, use the current f1, fc1, in the analysis; identify training as an impermeable formation; and calculating the half power spread in the center frequency using the upper and lower half power points in the spectral power density of vibration. At the 2750 level of 2740 or 2730, the low frequency limit of the selected spectrum PSD (the one with fcl or the one with fc3) is compared. If there is a significant increase in this value, eg greater than 2 standard deviations, a warning of impending bit failure should be issued. The procedure of Figure 27 can be concluded by going back to the flow of the procedure of Figure 26. Figure 28 provides the details that were described when calculating the step sizes for WOB, RS and Q. The example Figure 28 provides the appropriate state frame for the cruise mode label of the procedure of Figure 26 and the gradient search mode referenced in the procedure of Figure 26. At the level of 2805, a determination is performed to check if the gradient search label is set to "true". If so, at the level of 2810, a gradient search tag is set to be equal to "true". At 2815, we enter the gradient search mode; during the first pass in this mode, the WOB is increased by the minimum of the size of the step WOB and the RPM and the Q are not modified; during the second pass through this mode, the WOB and reset to its original value, the RPM is increased by the minimum step size of RPM and the Q is not changed; during the third pass through this mode, the RPM is reset to its original value, the Q is increased by the minimum step size of Q, and the WOB is not changed; and a gradient search label is set to be equal to "false". If this is not the case for 2805, at 2820, a determination is made to verify whether the cruise mode is set to "true". If this is the case of 3038070 2820, at the level of 2825, the WOB, RS or Q are not changed; power spectra and crossover spectra are always monitored; and if there is a significant change in the center frequency, the low frequency limit value, or the lithology, the cruise mode label is set to "false" so that the system will search for the new point. optimal operation. If this is not the case of 2820, at the 2830 level, a gradient search has been performed; the gradient of the cost function is calculated using finite differences for the derivatives and the cost function defined with respect to Figure 26; and the error weight of the step: on the bit, the error of the step RS and the error of the step "Q are estimated using the formulas: AWOB = Minimum step size weight on bit ARS = Minimum step size of rotation speed AQ = Minimum step size of bit flow through bit SWOB = Size of step WOB along gradient 15 SRS = Size of step step RS along gradient 3Q = Size of step Q along SWOB gradient SWOB = - AW OB 8 8W SWOB fc +, ux dfcj 8RS = -ARS x (.1 x fc +, ux Sdfcj 8RS SR 8 82 = -dQ4 / 1, x- 82 fc +, ux-8 dfcj 82 20 WOB _Step _ Error = AWOB 1 2 i 11 2 Pfe [WOB1] 2 + ofc [WOB2] 2 + p2 (6dfc [WOB1] 2 + cdfe [WOB2] 2)) WOB2 - WOB1 1 t RS _Step _Error - ARS .v 22 e [RS1] 2 + OFC [RS2] 2 ± p2 (o-Afc [RSU2 + cdfc [RS 2] 2 »RS2-RS1 Q _Step _Error -WOB2 WOB1 v 22 (cec [Q1] 2 + ofc [Q2] 2, u2 (o 1fc [Q1] 2 + 64fc [Q2] 2)) of c [W OB1] and ofc [WOB2] refer to the standard deviation of the fc at the level of the 25 parameters 1 and 2 of the WOB while now constants RS and Q. Of the same way, ofc [RS1.] and ofc [RS2] refer to the standard deviation of fc at the parameters 1 and 2 of the RS while maintaining constant WOB and Q , and crfc [Q1] and 6fc [Q2] refer to the standard deviation of fc at parameters 1 and 5 2 of Q while keeping WOB and RS constant. o-Llfc [WOB1] and 0'dfc [INOB2] refer to the standard deviation of the spectral peak width at the frequency fc at the parameters 1 and 2 of the WOB while keeping RS and Q constant, crdfc [RS1] and 6dfc [RS2] refer to the standard deviation of the width of the spectral peak 10 at the frequency fc at the parameters 1 and 2 of the RS while maintaining constant WOB and Q, and ndfc [Q1] and crAfc [Q2] refer to the standard deviation of the spectral peak width at frequency fc at parameters 1 and 2 of Q while keeping WOB and RS constant.
[0039] 15 WOB_Step_Error <.5 X I SWOB RS_Step_Error <.5 X I SRS I Q_Step_Error <.5 X I SQ where I ... I denotes an "absolute value". At 2835, the determination is made to see if WOB_Step_Error <.5 X SWOB. If this is not the case for 2835, at 2840, WOB_Step_Size is set to 0. If this is the case for 2835, at 2845, a determination is made to check if RS_Step_Error <.5 X ERS. If this is not the case for 2845, at 2850, RS_Step_Size is set to 0. If this is the case for 2845, at 2855, a determination is made to check if Q_Step_Error <.5 x SQ. If this is not the case for 2855, at 2860, Q_Step_Size is set to 0. If this is the case for 2855, at 2865, a determination is made to check if (WOB_Step_Size) and ( RS_Step_Size) and (Q_Step_Size) = 0. If this is not the case with 2865, at 2870, the gradient search mode is maintained. If this is the case of 2865, at the 2875 level, cruise mode is entered and the cruise mode label is set to equal "true". It should be noted that lessons at this point are based on published test results from a number of somewhat limited sources. It is unclear whether the characteristics identified in these sources apply to all rock / trephine interactions or even if they are characteristic of typical drill situations. All these results were obtained using test platforms. There is an important difference between the dynamic characteristics of a test platform and those of a real drilling platform. Similarly, none of the analyzes or simulations noted above and in the appendices take into account the dynamics of the drill string. For example, in general, it may be wrong to say that when a rock breaks, there is an acoustic signal characterized by an oscillation that decomposes exponentially. Nevertheless, the general procedures described here are applicable. That is, it is well established that the generation of acoustic noise characterizes the breaking of the rock, and it is well established that the piezoelectric signal is generated when the rock is stressed and that Seismic signal is generated when the acoustic signal is emitted by a porous medium. In a more general approach, acoustic and electromagnetic signatures can be monitored in situ and changes in their power spectral densities, cross-power spectral densities, autocorrelations and cross-correlations can be noted when varying. drilling parameters such as bit weight, rotation speed, flow rate and sludge density. When formation during drilling (FEWD) sensors are used in the drilling process, eg, as shown in Fig. 24, variations in lithology can also be noted from the changes. eg, at the level of natural gamma ray activity, resistivity, neutron porosity, acoustic porosity, gamma scatter density, porosity / permeability derived using magnetic resonance tools and azimuth images based on electromagnetic, acoustic or nuclear measurements. The azimuth images 3038070 48 often provide a clear indication when a boundary between two types of training has been crossed. The speed of penetration can be monitored from the surface of the earth. In addition, the penetration rate can be monitored using shallow-reading FEWD sensors, eg, as described in U.S. Patent No. 5,899,958. In addition, other parameters can be measured. at the bottom of the well that are related to drilling efficiency. As previously noted, the WOB, torque, and bending moments as well as rotational speed can be measured at the bottom of the well with various commercially available services. In addition, the flow rate can be measured at the bottom of the well and can be measured implicitly in various models of downhole controllers. These inputs can be used more generally than what has been described up to this point. It is well known that inputs from formation evaluation sensors can be used to determine lithology. In the more general technique, the cost functions are constructed for lithologies defined by a range of sensor inputs of the formation. For example, a certain shale may be characterized by limited resistivities between the p1 and p2 values, the natural gamma radioactivity limited between cl and c2 count rates, the transit times of the compression wave interval of t1 and t2, the shear wave velocities between s 1 and s 2; a sandstone can be characterized by another range of parameters, and it is the same for limestones, turbidites, etc. The characteristic of the time domain and the characteristics of the power spectral domain for the signals obtained during the breaking of the rock or the bit can be compiled for the type of lithology and for each type of bit and related to the efficiency of the drilling. The compilation of these characteristics can be done in situ, or under laboratory conditions. Within a given lithology, the inputs must be limited to vibration sensors and electromagnetic sensors, but may include dynamic WOB values, dynamic torque values, bending moments, rotational speed and of the flow. Here, the phrase "30 dynamic values" is used to differentiate these values from the average 3038070 values. Appropriate cost functions can then be constructed for each combination of lithology / trephine and stored in downhole banks. During drilling, the appropriate cost function is used to optimize drilling efficiency in a manner similar to that previously described.
[0040] Figure 29 is a flowchart of an embodiment of an exemplary method of determining cost functions for different lithologies. The general characteristics of the process can be applied to the determination of in situ cost functions or the determination of cost functions under laboratory conditions. With respect to the in situ, the number of lithologies to be analyzed may not be known in advance, but the loop may be executed as needed when unknown lithologies are encountered. In a lab, and there may be a schedule of L different lithologies that need to be examined. The laboratory may include one or more test wells with well-known lithologies, or a test platform equipped to drill rock samples with known physical properties. Once the number and types of lithologies to be tested have been determined, a determination of the relevant properties of the formation can be made which can be available at the bottom of the well through FEWD or cable measurements. These can be selected from attributes such as gamma ray natural activity, resistivity, etc. (as previously listed). Each lithology can be identified by a certain range of properties of the formation. For example, some shale may be characterized by natural radioactivity between 100 and 150 API units, resistivity between 0.5 and 2 μm, compressional transit time between 170 and 130 ps / ft. Other parameters may not be relevant to the identification of this particular shale, while some sandstone may be characterized by natural radioactivity between 20 and 70 API units, a resistivity between 1.5 and 40 compressional transit between 90 and 60 μs / ft, a neutron porosity between 0.15 and 0.25 PU, a density derived from gamma-gamma between 2.5 and 2.6 gm / cc, where other parameters may be not be relevant to the identification of this particular sandstone. These parameters can be 3038070 used to identify lithology during drilling and select the cost function associated with this lithology. No definition was given for drilling efficiency at this point, because none was needed. As used in the general approach taught here, the drilling efficiency e can be defined as the inverse of the mechanical specific energy (MSE). From Chapter 5: "Electromagnetic radiation induced in fractured materials" pp.379 - 458 of "Tensile Fracturing in Rocks: Tectonofractographic and Electromagnetic Radiation Methods," Bahat, Dov, Rabinovich, Avinoam, Frid, Vladimir, 2005, XIV, 570 p.
[0041] 302 Mus., Springer-Verlag, the MSE can be given by MSE = [40,000 WOB 40,000 - RS-T1 .14504, D2 D2 ROP where the WOB represents the weight on the trephine in Klbs, D represents the diameter of the bit in inches, RS is the rotation speed in revolutions / minute, T 15 is the torque in Kft * lbs and ROP is the penetration rate in feet / h. Thus, for this approach, a minimum set of drilling parameters is used to calculate the efficiency, including WOB, RS, ROP, D, and T. For a given system using the teachings described herein, additional drilling parameters may be obtained. It is assumed that D is fixed even though variations in D as the bit wears can be taken into account. In laboratory measurements, the WOB and either RS or T can be controlled over the pre-specified ranges. The ROP and either the T or the RS, regardless of which parameter has not been monitored, are measured. When the measurements are made in situ (at the bottom of the well) and in real time, the ranges of these values may be limited to their ranges used in the specific drilling operation in which the cost function is determined. Other drilling parameters that can be measured include acceleration or vibration at one or more points and along one or more axes beside the drill bit, electrical field measurements, such as It has been previously described and magnetic field measurements, as previously described, and bending moments near the bit. When the cost function is determined in situ and in real time, the ROP can be determined by correlating the tables of the shallow-reading sensors with a known separation as previously described. Consider a method for determining a cost function as shown in Figure 29. It is assumed that the lithology does not change during a single pass through the process described in Figure 29. The overall strategy is to measure drilling efficiency over specified time intervals. Only average values of WOB, RS, T and ROP can be used in these determinations since instantaneous or dynamic values would likely result in a very noisy data sequence. While data is acquired for averages used in calculating drilling efficiency, time sequences are created for all other variables. The formation measurements that are described here are used to determine the lithology. The drilling parameters J that can be controlled ideally include (WOB, RS) or (WOB, T); Q can also be included in this list even if it does not enter the MSE calculation. The dynamic parameters can be composed of any or all of the vibration or acceleration measurements, the E-field measurements, the magnetic field measurements, the bending moments, the measurements of the property of the formation. in real time (resistivity, natural gamma ray, etc., as previously described), and even dynamic values of WOB, RS, T or Q. When the average values of WOB, RS, T or ROP are available, the efficiency is calculated and the power spectra and the cross power spectra are calculated for the dynamic parameters. A complete set of cross-spectra can be calculated if the measurements are made in a laboratory and a determination made after the cross-spectrum relevance test. If the measurements are made in situ and in real time at the bottom of the well (as when unfamiliar lithology is encountered), it would be impractical to examine all the spectra, and a determination can be made before the system is sent to the bottom of the well for which the crossed spectrum will be determined. After determining the densities of the power spectrum and cross power, the following characteristics can be extracted from these: the frequency location of the spectral peaks, the amplitudes of the spectral peaks, the half-maximum widths of the peaks spectral values, the high frequency limiting values of the spectrum or of the crossed spectrals, the low frequency limiting values of the spectrum or the crossed spectrum, and the locations of the zero spectra. Other parameters can be specified based on experience. It should be noted that rather than power spectra and cross power spectra, any number of other spectral measurements may be used, such as wavelet transforms. But also, it should be noted that time domain measurements can be used. A brief example of these will be given later.
[0042] The drilling in a specific lithology can continue until a predetermined range of controllable drilling parameters are specified. Then, there is a set of drilling efficiencies associated with spectral properties over the controlled range of drilling parameters. Then, a regression can be determined between the sequence of efficiency values and the sequence of spectral properties. It would be better, rather than performing a single regression, to select a number of forms for regression, to regression these shapes, and to select the regression that produces the least squares error to serve as an estimator for the regression. efficiency. For example, a linear regression between efficiency and spectral properties can be used and then the regression can be matched by performing a second regression using only those variables that have statistically significant coefficients in the regression. Nonlinear regressions provide more flexibility and allow the expression of efficiency in the form of DE = p = 1Ap-13SPP-30 Regressions can be performed using well-established techniques using other 3038070 53 forms readily available in software packages such as Matlab. Once an appropriate regression has been determined, a cost function can be determined from the regression function. This can be done by examining the regression equation. The terms can be selected from the equation and pooled in the cost function to create a cost function that increases as efficiency decreases. In this sense, a simple cost function is the reciprocal of the estimated efficiency. This is not always the best approach. It may be apparent from the regression that some variables play a much larger role than others as predictors of drilling efficiency. Suppose instead that the regression is performed, not for the efficiency, but for the MSE, and the regression is in the form of 'c MSE = Ap - PSpBP p = 1 The PSp variables have been chosen so that they are always positive. The MSE increases (and so s increases) for the increases in the variables PSp so that (Ap, Bp)> 0 or (An, Bp) <0 and decreases otherwise. Since the goal is to minimize the cost function, an appropriate cost function can be in the form DC =, tp - ps'BP, where p = 1 where is chosen as a positive number if (Ap, Bp)> 0 or (An, Bp) <0 or else 20 as a negative number The magnitudes of these values can be selected based on the importance of the parameter p in the regression. It is clear that a specialist in the field can easily make several variations on this technique. For example, an even simpler cost function may be in the form DC PS p P = 1 where the Ap's are chosen based on the algebraic signs of Ap and Bp so that C increases when efficiency decreases ( or when the MSE increases). It would be better in the aforementioned form if at least one exponent having the same algebraic sign as Bp is used. The method illustrated in the figure comprises at 2910, a number L of lithologies identified as being included in the library of lithologies and a lithology counter, i, set at i = 1. At 2920, a set of N measurements 5 of the training is identified that will be measured in real time (or otherwise available in real time) as {Fi, F2, ... FN}. At the level of 2930, the ranges of the formation parameters that define the lithology i: (L ,, 1, U41), (Li, 2, U ,, 2), - - - (L4N, U ,, N) are determined. At the level of 2940, the drilling parameters J, P2, ... that can be controlled are defined. At 2950, dynamic parameters D, (S1, S2, - SD), which can be monitored at the bottom of the well are identified. At the level of 2960, when drilling inside the lithology i, (Fi, P2, ... FJ) are measured repeatedly for a time interval t and the drilling efficiency e is determined from the diameter of the bit and the average values of 15 WOB, RS, torque, ROP. The sequences of the samples of (S1, S2, ... SD) are acquired during the same time interval, t. The PSDs and the crossed PSDs are computed for the data sequences derived from the samples of (S1, S2, ... SD). Characteristics are determined from the crossed PSDs and PSDs, the power characteristics include spectral locations, spectral peak amplitudes, half-maximum spectral peak widths, low frequency limits, high frequency limits, Zero spectral locations, where there are parameters k in all, which can be defined as (PSI, PS2, PSk). Each value of e is associated with the values of the spectral parameters so that for each specific value of e, (called the en), there exists a set of parameters (PSI, p, PS2, p, PSk, p). At 2970, a cost function is determined from the sequences of ep values and (PSi, p, PS2, p, PSk, p). A regression is performed between the values of ep and (PSi, p, PS2, p, PSk, p) using all the data acquired inside the lithology i. The regression function is named e (PSi, PS2, ...
[0043] 30 Pk). A cost function is defined from the functional forms used 3038070 in e (PS 1, P52, PSk). The cost function is entered in the cost function band and the index i is increased by 1. At 2980, a determination is made to see if i> L. If so, this procedure ends at level of 2990, otherwise, the next lithology is taken into account.
[0044] Figure 30 is a flow chart of an embodiment of an exemplary method of downhole use of a cost function bank. A processing routine is illustrated for the selection of cost functions based on lithology. At 3010, the formation evaluation sensors are measured to produce, at a given depth, a data set {F1, F2, - - - 10 FN}. Lithologies in the lithology library, which can be stored at the bottom of the well in the MWD drill string, can be identified by specific ranges of formation assessment parameters that uniquely identify a lithology. At the level of 3020, a library is searched for lithology which incorporates the values of {F1, F2, ... FN} into its range of parameters. At 3030, a determination is made to see if a lithology match is identified. If so, at the 3040 level, the identified cost function is chosen from the cost function bank and is used for drill optimization in this lithology. The measurements from the FEWD sensors can be compared to the limits of the lithology table, so that good lithology can be identified, and the proper cost function for this lithology can be provided to the system. If this is not the case from 3030, at 3050 the nearest cost function from the bank is selected and a value database of e. 25 (P1, P2, ... PD) constructed, and the values of {F1, F2, ... FN) are recorded. This mode of operation is maintained until a recognizable lithology is captured. When exiting this mode, the range of values {F1, F2, - - FN) encountered when in this mode is recorded and a cost function is determined as described with respect to Figure 29. news 30 information on the lithology are entered in the bank.
[0045] As previously noted, if no lithology can be found that matches the FEWD value set, then the procedure can be implemented to select the lithology that is closest to the observed lithology and start using of the cost function that is appropriate for this lithology. A simple parameter that can be used to determine "closest" is the li parameter defined below, although other parameters may be used. is a series of numbers representing L + U = Fi - 2 "J = 1 the distance between the set of measurement values of the formation {FL} and the property ranges of the formation medium defining the lithology i. the total number of formation properties required to uniquely identify a lithology i, and and 11.0 are used with respect to Figure 29. The lithology library may contain more than 1 formation parameters and the units defined below. above can be changed to ignore parameters that are irrelevant.
[0046] The cost function, associated with the value of "i" for which is minimal, can be selected as the initial cost function. When the system is operating in this mode, evaluation of formation, drilling, and dynamic drilling data must be continually acquired. Once the system enters a formation that can be identified, the set of training evaluation measures should be analyzed, as previously described, to define a single lithology. Then, the drilling data and the dynamic drilling data must be used to determine a cost function as previously described. If sufficient processing power is available at the bottom of the well, the cost function can be determined at the bottom of the well. Otherwise, it can be determined when the tool is brought to the surface of the earth and its memory read. On the other hand, if a high data rate telemetry system is available, the cost function can be determined at the surface during drilling and the appropriate cost function downloaded to the downhole system through downlink telemetry. .
[0047] 3038070 57 The following is an example of the time domain analysis. Although most of the discussion has focused on frequency domain analysis, as previously mentioned, the teachings herein can also be realized using time domain analysis. This section presents a brief example of how this can be achieved. It should be understood that this example can be extended and generalized in the same way as were the lessons of the frequency domain. Figure 31 is a flowchart of an embodiment of an exemplary analysis of an acoustic and electrical or magnetic data stream. This general analysis scheme can be used to analyze data in the time domain, e.g., when a single acoustic channel or a single electric or magnetic field channel is correlated. The results of this analysis are indicators of drill bit wear and drilling efficiency. It should be understood that similar correlations can be made among the sensors of a system. In addition, it may be noted that it is not essential to have acoustic sensors in this system. Consider a continuous data stream with a given specified sampling rate generated by and from an acoustic sensor, at 3105, and a continuous data stream with a given specified sampling rate generated by and from an electrical or magnetic sensor, at 3110. At 3115, the correlation between the data streams is calculated. The sampled data can be windowed into consecutive (or possibly overlapping) windows of a specified length. Then, the autocorrelation can be calculated for each acoustic data window and for each window of the electrical or magnetic sensor data. The cross-correlation can be calculated between the windows of the acoustic and electrical or magnetic sensor data covering the same time intervals. The autocorrelations and cross-correlations can be performed using any number of known techniques, such as, for example, using the xcorr function in Matlab, which allows considerable flexibility in these calculations. In one embodiment, an option selected in the Matlab procedure may be an "unbiased" option, while an offset range is chosen as the default. The use of the windowing function is optional, but a windowing function such as the Hann window, the Hamming window, the cosine window, the Tiaussian window, any of the 28 popular windowing functions, or other Windowing functions can be used. One advantage of using a window is that it helps to minimize anomalies at the ends of the correlation function created by the data windowing process. For a given time interval, autocorrelations and cross-correlations can be examined to identify correlation peaks. At 3120, the peak offsets for autocorrelation of the acoustic sensor, the autocorrelation of the electrical or magnetic sensor, and the cross correlation of the acoustic electrical educator or magnetic sensor are determined. At 3125, thresholding is performed.
[0048] The peaks can then be subjected to a thresholding method in which only peaks having an amplitude greater than a pre-specified limit are accepted. The pre-specified limit can be based on the experiment, or by default, or for the autocorrelations, can be chosen as 0.25 of the amplitude of the central peak (each autocorrelation must have a peak at the offset 0), while in cross-correlation, the peak can be set to 0.25 μl (Ampl .Acoustic 0 lag peak) - (Ampl electric or magnetic sensor 0 lag peak) where "Ampl.Acoustic 0 lag peak" represents the amplitude of the offset peak 0 in the acoustic auto-correlation, and "Ampl.electric or magnetic sensor 0 lag peak" represents the amplitude of the offset peak 0 in the autocorrelation of the electrical or magnetic sensor. The thresholding operation serves two functions: 1) to identify correlation and autocorrelation peaks that could be related to important events, and 2) to identify regions in which ringing may occur.
[0049] Concerning the first objective served, the amplitude of each peak is noted 3038070 as well as the time width up to the half amplitude on either side of each peak and the number of situations within the points. half amplitude. At 3130, for each peak of the threshold acoustic sensor autocorrelations, the peak amplitude, peak half-amplitude width, and number of oscillations within the half-amplitude points are determined. . At 3135, for each peak of autocoffelations of the electrical or magnetic threshold sensor, the peak amplitude, the width of the peak half-amplitude, and the number of oscillations within the half-amplitude points are determined. At 3140, for each cross-correlation peak between the electrical or magnetic threshold sensor, the peak amplitude, the width of the peak half-amplitude, and the number of oscillations within the half-points of the peak. amplitude are determined. Regarding the second objective served, each event with a threshold in an autocorrelation or a cross correlation corresponds to a particular time in the time series. At 3145, events with a threshold in time are identified. These times can be identified and can be used to define successive intervals that can be examined for ringing and non-ringing events. A tingling event of interest would be characterized by an exponential increase in amplitude followed by oscillation with exponential decomposition, while a non-tinting event would not have this characteristic. At 3150, windows between threshold events are generated. At 3155, events are analyzed to determine which ones are ringing. At 3160, the statistical frequencies of tingling events are calculated. At 3165, the statistical frequencies of the non-tapping events are calculated.
[0050] At 3170, the decomposition rates and the time frequency of tingling events are calculated. The statistical frequency can be determined for tingling and non-ringing events. Put differently, for a given window, the number of tingling and non-processing events can be identified. Techniques familiar to those using NMR analysis can be used to identify tingling events. These techniques (effectively curve fitting) can also be used to estimate the exponential increase and decomposition constants for tinting components as well as the temporal frequency of the oscillations.
[0051] Finally, these statistics can be tabulated and associated with the given time window in which they were observed. At 3175, the statistics are results, when the statistics include data from 3130, 3135, 3140, 3160, 3165, and 3170. When successive windows of data are analyzed, trends in peak offsets occur. For correlation, their amplitudes and widths can be followed at the exponential increase observed and the decomposition rates and the temporal ringing frequencies and in the statistical frequencies of the tingling and non-tinting events. An enlargement of the correlation peak, a decrease in its amplitude or a shortening of an exponential decomposition rate and an increase in the frequency of oscillation are an indication that the drilling efficiency has decreased. A large increase in the number of non-tingling events is an indication of impending bit failure. The discussion could continue with a complete analogy to the discussion of frequency domain measurements. Specific cost functions and optimization routines have been described here as well as specific control means. It should be understood by those skilled in the art that the theory of optimization through cost functions is mature enough and any number of other techniques could be adopted in accordance with the teachings herein. Similarly, the state of the known controllers is mature enough and different types of controllers, not specifically identified here, can be used, eg, proportional-integral-derivative (PID) controllers. In addition, by taking schemes similar to or identical to the schemes that have been described herein, the neural networks can be calibrated to convert such pattern information into drill-related control parameters, such as drilling efficiency. . In various embodiments, the apparatuses and methods described herein may be related to the determination or identification of friability of the formation. Friability is a parameter of interest for both drilling and fracturing and has become particularly important in ponds and soils and "unconventional" ponds (areas in which hydrocarbons have accumulated or could occur). accumulate). A material is friable if it has a linear elastic behavior up to the breaking point. That is, such a material has no ductility. In practice, almost all materials demonstrate some ductility. According to "The Effect of Mechanical Rock Properties and Brittleness on Drillability," Olgay Yarali, Eren Soyer, Scientific Research and Essays Vol. 6 (5), pp. 1077-1088, March 4, 2011, referred to herein as Yarali reference, "friability is defined as a property of materials that break or fracture with little or no plastic flow". Thus, it is desirable to have a measure of friability. There is no standard in the industry at the moment; in "Assessment of some brittleness indexes in rock-drilling efficiency, Rasit Altindag, Rock Mech. Rock Eng (2010) 43; 361-370, referred to here as the Altindag reference, we note that there are 20 proposed definitions. Although there is no general agreement on a definition, the definitions that have been proposed are illuminating. The reference Altindag enumerates the following elements: B1 = a '(dimensionless) cr, B2 = c I (dimensionless) Cc -20 ", B3 = with units of (Mpa) 2 25 B4 = [B3 with units of Mpa The reference Yarali proposed 03 B4 '= 0_1) 2 3038070 62 where cr, represents the uniaxial compressive force, and crt represents the tensile strength of the material.A reliability index can be determined from the measurements of the speed of compression, shear rate and density of the formation This index can be given by BI = (cl -E + c2v) 12, where y the Poisson ratio is given by Vp-2, Vs2 v = 2 (V; - Vs2) E is the Young's modulus given by E = 2-p - (1 + v), where p represents the density of the rock matrix, cl and c2 are coefficients that can act as equalizers to the the significance of y and E as an indicator of friability, yet another friability index is defined as the percentage of 15 materials that passes 11.2 mm mesh after grinding the aggregates by 20 impacts in a specifically designed mortar. See the reference Yarali, in which this is identified as Sm. This may seem a little ad hoc, but most friability indices have been derived based on correlation observations between very direct friability measurements and other mechanical properties of the rock. In this respect, the drilling speed index (DRI), described in the Yarali reference, is of particular importance. The DRI is derived by cross-plotting the measured values of S20 with the measured values of a parameter called Sievers' JOValue (SJ), which is a measure of hardness. The equation for B4 was empirically determined from tests with measured values of S20 and DRI. Thus, useful and important parameters, called friability indices, related to the breaking of the rock can be obtained from the measurements of elements such as the Young's modulus, the compressive force of the rock, the force tensile strength of the rock, the density of the rock, the velocity of the compression wave and the speed of the shear wave. Another material property of the rock is its piezoelectric constant, or more correctly, its piezoelectric tensor, which, as previously described, relates to the stress tensor and the electric field displacement vector. For very friable materials, there is a linear relationship between stress and tension to failure. AinSi, the displacement vector increases as the tension increases until a friable rock breaks. Now, take the equations of the piezoelectric effect: S = s * T + T * E 10 D = * TIE * E For simplicity, the appropriate tensor and vector notation has been removed in these equations since they do not play a significant role in the following considerations. In this equation, S represents the voltage tensor, T represents the stress tensor, E represents the electric field vector, D represents the electric displacement vector, the tensor of the resilient tensor, E is the dielectric polarization tensor, and is the piezoelectric tensor. There seems to be no standard notation for the piezoelectric tensor. It should be noted that this is the electric displacement vector that is driven by the stress. If a measurement of the electric field is made, the driven field is divided by the dielectric constant. Particularly at low frequencies, schists (of particular interest for unconventional terrains) tend to have very high dielectric constants (values as high as 107 relative to the dielectric constant of a vacuum have been reported). Thus, it may be difficult to observe the induced transient electric field via the dielectric effect when shales break up. A better approach is to measure the density of the magnetic flux. After a few manipulations of the dielectric equations and the Maxwell equations, one obtains (by working in the frequency domain) 30 V2 / 3 + (e *, u * co2 -i *, u * o- * co) * E- = - co *, u * * V x ' Thus, the stress tensor loop serves as a source for the magnetic field, and the dielectric constant only appears in the wave vector. In the limit of the plane wave, the component of a wave vector along the direction of propagation is given by JE 5 k = ce) Ve 2 * p2 * co2 p 2 * 62 + E * /. 1 2 2 * 2 +, u2 * 2 * / 1 2 2 + Cr -E p 2 Clearly, through the stress tensor and the piezoelectric effect, a correlation exists not only between the friability of things such as the velocity of compression and shear velocity, but also with respect to electromagnetic signals emitted when the shale is fractured by a drill bit.
[0052] In this case, particular attention must be paid to the exponential increase in the signal just before the breaking of the rock. As previously mentioned, friability, compression wave velocity, shear wave velocity, and magnetic field measurements can be performed in a laboratory with a variety of lithologies to identify 15 appropriate correlations between friability and signatures of the magnetic field. The measurements can be performed when the experiments described above, compared to the generalization, are performed. Correlations between friability and magnetic field signatures may include magnitude and rise time, as well as low frequency trends and high frequency limits and cross power spectra. As was the case, the cross-correlations between acoustic and / or vibration measurements and magnetic field measurements can be understood. In this case, rather than producing a cost function with the previous teachings, a friability index could be created.
[0053] As described herein, the embodiments of the methods and apparatus may include the use of a correlation between acoustic and electromagnetic emission emitted by a rock when crushed or fractured, so-called here "Breakage", to give a chip size and cutting edge of the drill bit and drilling efficiency. The statistical frequency and time domain methods can be used in such methods and apparatus. In addition, as described herein, embodiments of the methods and apparatus may include using the determined drilling efficiency with a control mechanism to optimize drilling efficiency. Data on drilling efficiency can be acquired in situ, with the incorporation of this information into a database and a control model during drilling. Further, as taught herein, embodiments of the methods and apparatus may include the use of the correlation of electromagnetic emission emitted by a rock when crushed or fractured to determine friability . As described herein, embodiments of the methods and apparatus may include acoustic and electromagnetic emission monitoring through sensors that will be mounted in, or near, a drill bit. The correlations of these measurements and their associated power spectrum densities and cross power spectrum can provide authorizations for the drill bit and for training diagnoses. Such authorizations can provide an improved method for determining the drill bit cutting edge which could include the simultaneous use of acoustic and electromagnetic signatures and / or their cross power power spectra or spectrum using sensors mounted on, or near, a drill bit. In addition to the acoustic and electromagnetic signatures emitted by the breakage of the rock, there are several other sources of acoustic and electromagnetic noise at or near the drill bit. The simultaneous use of the two signals helps to clearly identify the component related to the breaking of the rock. Acoustic noise sources of the signals may include, among other sources of noise, bit contact with the formation as it strikes the side of the wellbore, bit bouncing, drill string contact with the drill bit. wellbore and cuttings that strike the downhole module. The electromagnetic noise sources of the signals may include, among other noise sources, a flux potential from the bit bits, and a signal induced through the rotation of the drill string in the Earth's magnetic field, even if this induced signal should normally be quite small due to the magnetic properties of the bit and the high electrical conductivity of the bit matrix.
[0054] With respect to these methods and apparatus, the acoustic signature, and thus the electromagnetic signature, changes as the bit becomes dull. A signature obtained from the correlation of acoustic and electromagnetic signals also changes as the bit becomes dull. Thus, the variation in the signature can be used as an indication of drill bit wear and drilling efficiency. The power spectral density of the acoustic signature or electromagnetic signature, or the cross power spectral density between acoustic and electromagnetic signatures may be used to provide an indication of drill bit wear and drilling efficiency. Statistical time domain techniques similar or identical to the techniques taught herein may be implemented for signature analysis to determine drill bit wear and efficiency. As described herein, embodiments of the methods and apparatus may include drill bit clearances and training diagnoses from measurement correlations for acoustic and electromagnetic emission monitoring through sensors. which are mounted in, or close to, a drill bit and their associated power and cross power spectral densities. Various authorizations can provide new mechanisms for identifying the size distribution of drill bit cuttings that are produced when the rock is broken. As the bit 25 becomes dull, the average size of cuttings torn from the formation when a wellbore is constructed decreases. This causes an offset in the acoustic and electromagnetic spectra to higher frequencies and a loss of signal amplitude. Other authorizations may provide novel bitshell lithology identification mechanisms that may utilize electromagnetic signatures. The electromagnetic signature has three components. There is a contribution of the piezoelectric effect when the rock is energized and when it is broken. When the rock that is broken is porous and permeable to fluid transfer, another signature will be emitted due to the seismic effect. Since the spectral components from piezoelectric and seismoelectric lattice effects are different, this signature can be used as an indicator of lithology. Furthermore, given the differences between the piezoelectric and seismoelectric creation of the electromagnetic signals, it is also possible to differentiate between these two components by making simultaneous use of the electric field sensors and the dynamic magnetic antennas. The detection of these signatures is increased by the correlation with each other and with the acoustic signal. In the case where the PDC cutters are electrical insulators, there is a contribution of the pyroelectric effect.
[0055] Other authorizations may provide novel mechanisms for identifying the fracture of the drill bit teeth. When the bits of the drill bit fracture, they also emit acoustic and electromagnetic signals. These events are less common than the breaking of the rock and have a different signature.
[0056] Other authorizations can provide new mechanisms for optimizing drilling efficiency. The information collected through acoustic and electromagnetic signature acquisition and through their correlation is transmitted to a controller. Through the modification of bit weight, bit torque, or timing of the forces sent / bending angles of a rotatable steerable system in response to measured parameters, a condition for optimum drilling efficiency can be obtained. Other authorizations may provide novel mechanisms for identifying friability of the rock, in which use is made of the signature of the magnetic field signals that are emitted when the formation is dug by the drilling method.
[0057] The devices and processes that operate the apparatus can provide a number of improvements to drilling operations. As the penetration rate decreases in a drilling operation, it is often not known whether the decrease is due to a change in the cutting edge of the bit where a change in lithology occurs. The apparatus and methods described herein may provide indicators of formation wear and changes in lithology. In addition, as described herein, this knowledge of formation wear and lithological changes can be used to optimize drilling efficiency as part of an automated process. In addition, such apparatuses and methods can be adapted to provide a formation friability determination, which is a property of training that is important and difficult to determine and that is relevant to unconventional terrain. Annex I. Rock properties and bit signals.
[0058] Both the signal from the rock break and the signal from the bit can be represented as tf [t] = A * and t <_0 f [t] = A * e * Cos [wo * t] t> 0 where A is an amplitude, and depending on the measured signal, A can be m / s2, m / s, in Pascal units, volts, volts / meter, nanoTeslas or Oersted; t is the time in seconds.
[0059] The term r is a characteristic time of stress accumulation. Constants of this nature are often called "decomposition constants". For the sake of clarity, when referring to a rock, the symbol ri / is used, and when referring to a trephine, the symbol TB is used. The term v is a characteristic time for the decomposition of tinnitus after fracturing of the rock. This may be applicable to the drill bit as well, but if so, it is thought to be very short. Nevertheless, for the sake of clarity, the DR symbol may be used, if necessary, to identify the rock component of the signal. The term w0 is a characteristic frequency for ringing a rock or a bit. Since it is believed that the oscillations of the bit are of very high and negligible frequencies, the symbols coo and coR can be used interchangeably, when the index "R" refers specifically to the rock. oo is in units of reciprocal seconds and is 27t times the characteristic frequency in Hz. The pulses from the breaking of the rock or bit at times other than t = 0 can be considered through the time offset. The following convention is used for the Fourier transform: +.0 F [w] 1 f e '* 0) * t * f [t] * dt -N / da)] * de2Tt 10 For rock pulses or bit F [co] = * ee ° where AR [co] = 2 * e- (y2 + v4co_2 + 2, r (v + v3 (c02 0) 02)) r2 (1 + y40) 04 + v2 (cd + 20) 02))) (1+ r2cd) 0+ v4 (cd-012) 2 + 2v2 (w2 + w02)) and O [w] = ArcTa w (-v2 + v4 (- (02 + ag) + r2 (1+ v2 (0) 2+ 24) + v4 (-co2c4 + 4)) + (co2 + 4) + r2vco2 (1+ v2 (co2 + 4)) + r (1+ v4 (co2-4) 2 + 2v2 (co2 + 4)) The phase would be of no interest in subsequent analyzes since it plays no part in the power spectral density. If several pulses are present in a given time interval, it is assumed that the (average) phases add up randomly. R [0] = = A -Iv2 + 2r (v + v3 coo2) +1.2 (1 2v2c02 v46004) (1 + r2co02) (1 + 4v2co02) 1+ v2 cce R [w0] o0iA Ive V4C / 0 2r ( v + 2v3o0) + r2 (1 + 3v2o02 + v404) 3038070 A (r + v) Lim [R [co], co or)] = 27r rvw2 Two independent processes are reported as follows: 1) rock break 2) breaking of the bit tooth. With respect to the breaking of the rock, the breaking of the rock occurs at random times. This randomizes the phase of the events. The parameter distributions can be taken as follows: AR has a normal distribution with an average of ARO and a standard deviation of oAR; TR has a normal distribution with a mean of 'tRO and a standard deviation of atR; DR has a normal distribution with a mean of vRO and a standard deviation of cs'R; coR has a normal distribution with an average of CoR0 and a standard deviation of Ge. It should be noted that distributions can not be strictly normal since negative values of AR, TR, le and co R are inadmissible. It is therefore assumed that the standard deviations are sufficiently small compared to their respective averages for the probability of a negative value of a variable to be neglected. Rock break events are statistically independent of each other and with respect to events involving breakage of the bit. The parameter u is such that u »T. The rock breaking events obey the Poisson statistics with a speed parameter pR With respect to the breakage of the bit teeth, the breakage of the bit teeth occurs at random moments. This randomizes the phase of the events. The parameter distributions can be taken as follows: AB has a normal distribution with an average of Am and a standard deviation of AB; TB has a normal distribution with an average of tBO and a standard deviation of at13; uB = 0, which is an approximation, but because of the conductivity of the teeth near the drill bit, no oscillation is expected; and no supposition is needed about coB, because of the assumption about vB. It should be noted that the distributions can not be strictly normal since negative values of AB and 'tB are inadmissible. It is therefore assumed that the standard deviations are sufficiently small compared to their respective averages for the probability of a negative value of a variable to be neglected.
[0060] 3038070 71 Teeth break events are statistically independent of each other and with respect to breakthrough bit events. The bit teeth break events obey the Poisson statistics with a speed parameter pB, where pB "pR. If that is not the case, then there is a severe malfunction of the trephine. When calculating spectral densities, it should be assumed, as a first approximation, that all listed variables can be processed using their average value. Without reproducing the details of the derivation, which are simple, with its assumptions, the power spectral density of a drilling method, visualize either by an E-field sensor or an acoustic field sensor, is given by w. + 2wR2))) + PSD [w] = (1 + TR 2 CO2) (1 ± VR4 ((02 - COR2) 2 + 2VR 2 (CO2 ± (OR2 PB * AB2 * TB2 27r 1 + 1-2 * w2 PR * AR2 * (v R2 + v R4 co2 + 2, r, Rv Ro + v R2 (0) 2 coR2)) r R2 0 ± v R4 coR4 v R2 At a resonance peak PSDVDR] = * VR2 ± VR4COR2 ± 2r '* vp (1 + 2vR2COR2) + TR20 ± 3vR2COR2 + vR4 (COR + 4) pR * AR2 27r (1+ zR2w, 2) (1+ 4v, 2wR2) PB * AB2 * 1-B2 27t. 'rB2 * w, 2 A useful approximation can be made by noting that the contribution from breakage of the bit teeth should be small in this part of the spectrum compared to the contribution from the breaking of the rock. above the resonance, that is, when w >> wR, and including the contribution of the trephine * A 2, B * AB2 * 1 L ',,,, PSDco [= PR * Y'R 2 VR2 R2 CO4 27r w2 If the term bit does not really have a situation, then it can dominate at very high frequencies, but the relative magnitudes of the amplitudes and probabilities are taken into account. In the low frequency limit, o << wR, PSDkoL pR * AR2 * (VR2 (1 ± VR2W2) ± 2 rRvR (1 + vR2 * coR2 r R2 (i + v R2 * coR2) 2 '+ (»R 2ir ( 1+ r co2) (1 + vR2 * co, 2) 2 PB * AB2 * rB2 2, r 1+ '1 "B2 * CO2 at frequency 0 PSD [0] I = pR AR2 VR2 + 2TR * VR * (1 + VR2W R2) 4. r R2 vR2 coR2, '2 B * AB2 * 27r (1 + vR2C) R2) 2 27r PS MO] p R AR2 VR2 2 2TR * vR ,,. J ± B * A B2 * 2 2n (1 ± VR2 COR2) 2 (1 ± VR2 COR 2) 2 13 2, r 13 7r 10 In examining these limits, it does not appear that there is a proper way to identify the contribution of the bit to the behavior of frequency, except, perhaps, very high frequency behavior.With a low pB, TB is expected to be considerably less than rR.It may be that AB> AR, and the first two terms of rock may be small in comparison to the third, even if it is not known, if so, then A 2 * A 2 11 2 PB 271. 2-1B * 2 PSD [0] = PR 22R r R If the parameters of rock can be well known If the bit parameter can be known from this, it is anticipated that the errors in the parameters of the rock and the relative size of the terms will prevent such a determination. ANNEX II. Analysis of the spread of spectral peaks from the breakage of rocks due to the statistical distribution of the characteristic frequency of a break.
[0061] 3038070 73 As already mentioned, it is assumed that the breaking of the rock occurs at random times. This randomizes the phase of the events. We also assume that ohz. has a normal distribution with a mean () R ° and a standard deviation of (5, R. As part of this analysis, the statistical nature of the other parameters is neglected Ignoring the overall factor of pR * A2 / (27t ), and considering only the component of the rock, the square modulus of the Fourier transform of the signature of a rock break is given by v, 2 + vR4c02 + 2r, (v, + v'3 (co2 + co'2)) + / - '2 (1 + v, 4co'4 + v, 2 (co2 + 2co, 2))) Ski), co, = 10 (1 +) (1 + v R4 (co2 - co R2) 2 + 2v R2 (co2 + co;)) The normal distribution of the breakout frequencies coR will be assumed to be distributed around a frequency el, with a standard deviation of 'y as follows: (a). -%) 2 The expected value of the square module of the Fourier transform, ie, the power spectrum, is given by 1 r, 2 + v, 4c02 + 21-, 0), + vR3 (0) 2 + co, 2)) + r, 2 (1 + vR4co, 4 + v, 2 (co2 + 2c, 2))) * e ('% 0) 72 * dco, P [col = y * (1 + - R2 cp2) (1+ v R4 (co2-co, 2) 2 + 2v R2 (co2 + coR2)) It does not seem possible to evaluate this or any reasonable approximation to these in a closed form. In addition, digital integration is slowed by the extremely slow convergence of the integral; even if the integral can be evaluated, this takes a considerable processing time. Figure 32 is a PSD graphics game for rock breakage with normally distributed characteristic frequencies centered around a characteristic frequency of 300 Hz for different typed gaps. The different standard deviations are 3.125 Hz, 6.25 Hz, 12.5 Hz, 25 Hz and 50 Hz. The steepest curve is the value obtained without any variation in the frequency 1 F [, coR = * 2y2 y * -eir 3038070 74 feature. In all curves, CR = 0.002 s, v R = 0.05 s. In comparison, the power spectrum when all parameters are essentially constant is given in Figure 33 for a range of value of OR.
[0062] Figure 33 is a set of PSD graphs for breaking the rock with no variance of break parameters, for different values of the characteristic decomposition time, OR. For large values of 0R, distributions have an abrupt spectral peak. The peak is reduced when DR is decreased, but it should be noted that all spectral peaks remain within the same envelope. In comparison, in Figure 32, the spectral peaks are somewhat reduced in amplitude and widen considerably as the standard deviation of the characteristic frequency increases. For the same range of parameter variation, the effect of the change in frequency in reducing the height of the spectral peak is significantly smaller than the effect of the change in the characteristic decomposition time. As expected, the broadening of the spectral peaks as a function of the standard deviation in the expected characteristic frequency gives curves that are not contained within a single envelope, but which extend beyond the envelope corresponding to a lower standard deviation. The significance of this is as follows: as DR decreases, the drilling efficiency decreases and the size of the cuttings decreases. When drilling efficiency decreases progressively, the spectral peak falls, but remains within the original envelope. On the other hand, an enlargement of the spectral peak with a small amplitude fall corresponds to a condition in which the characteristic frequency varies more when the rock is destroyed. As previously noted, an increase in the variation of this frequency is an indication of an improvement in drilling efficiency, and vice versa. Without performing detailed Monte-Carlo syntheses, when the parameters 0R and 'y are random variables, it is only possible to make some generalizations from its observations. By combining the results involving the variation with y with those in which the PSD is reported for individual 3038070 values of uR with all the other variables being constant, it can be safely concluded that a reduction in drilling efficiency is always accompanied by a fall in the amplitude of the resonant peak without widening of the peak.
[0063] Other analyzes are possible through Monte Carlo simulations. Figures 34 to 40 were obtained by Monte Carlo simulation. It is assumed that all statistical parameters, except for rock and bit break rates, are normally distributed. It is assumed that the breaking rates of rock and bit are distributed according to the distribution of fish.
[0064] The signals were "sampled" at a speed of 10 kHz for a period of 50 seconds. Individual traces tend to resemble noise, although there is enough difference in the final simulation that is shown in Figure 41 as a series of displaced traces. The spectral power density of Walsh was used to produce power spectral densities. In the Fourier transforms, 16,384 points were used, while 8,192 windows were used. This gave a very fine spectral resolution. Figure 34 is a graph series derived from the Monte Carlo simulation for rock breakage with a central frequency spread distribution. Frequencies were normally distributed with standard deviations in coR which varied in 9 steps from 100 * n / 128 to 200 * n / second. Figure 34 should be compared to Figure 32. Both are on a log-log scale, but Figure 34 is in decibel units. In addition, there are differences in amplitude due to the nature of the Fourier transform used. However, it can be seen that the trends identified in Figure 32 are also evident in Figure 25 34. A wider spectral distribution of rock break frequencies results in a widening of the peak in the power spectrum, but has very little effect on the low frequency or high frequency response. Figure 35 is a graph set derived from a Monte Carlo simulation for rock breakage with a standard deviation distribution, cs, R, of the characteristic times associated with the accumulation of stress in the rock. Figure 3038070 does not correspond to a case that has been solved analytically. In this case, the characteristic accumulation time until rock break was varied over nine plots. It is not possible to make definitive statements about the effects of this variation. This variation is similar to that seen in Figure 38, or the nine plots were all simulated using the same parameters. Figure 36 is a graph set derived from a Monte Carlo simulation for rock breakage with standard deviation distribution, a ,, R, characteristic times associated with stress accumulation in the rock. Figure 36 also shows the variation in spectral behavior as a function of various values of build-up time to break-up of the rock. In this case, a larger variation in the characteristic frequency was used than that in Figure 35. A very hypothetical conclusion, particularly since the variation in Figure 28, when the nine plots were obtained using the same parameters That is, the low frequency and high frequency components are increased by increasing the build up time until the break of the rock. This is counter-intuitive for high frequency behavior. Figure 37 is a graph set derived from a Monte-Carlo simulation for rock breakage with a standard deviation distribution, CYDR, of characteristic times for decomposition of the tingle after breakage of the rock. In Figure 37, the characteristic decomposition time is the only variable that is changed from plotted to plotted. For the sake of clarity, this is illustrated with a linear frequency scale. This behavior is essentially the same as that shown in Figure 33.
[0065] Figure 38 is a graph series derived from the Monte Carlo simulation for rock breakage in which bit break is not included in the analysis. In Figure 38, all parameters related to rock breakage are varied with a fixed set of standard deviations. However, each graph is for the same range of parameters. The spread between the spectra is an indicator of the effect of the overall randomness of the variables. This must be taken into account in the actual spectrum analysis (in comparison to the synthetic spectrum). Figure 39 is a series of graphs from the Monte Carlo simulation for rock breakage in which bit break is included in the analysis. The parameters of the rock are the same as in Figure 38, but the parameters of the Poisson's rate of the break of the rock with respect to the breaking of the bit are varied from one chart to another. Rather than increasing the overall bit Poisson speed parameter, the ratio of the bit to the rock break speed parameter has been increased. In the final graph 3990, the bit and the rock break, on average, at the same speed (catastrophic bit failure). The low frequency response is significantly increased as breakage of the bit increases. The high frequency behavior does not appear to be a systematic function of the bit breaking frequency. Figure 40 is a graph series derived from the Monte Carlo simulation for rock breakage in which bit break is included in the analysis. In Figure 40, an attempt was made to track the performance of the bit taking into account the vibration of the rock / bit interaction as bit performance decreases. According to the literature, it has been assumed that the characteristic frequency increases as the performance of the bit deteriorates, the characteristic decomposition time decreases as the performance of the bit deteriorates, and it has been assumed that the accumulation time to at the break of the rock and the breaking of the bit increases as the performance deteriorates. Individual plots can be considered as being separated in time by several hours of drilling.
[0066] The behavior thus obtained is a mixture of previously observed behaviors and provides no surprise. Figure 41 illustrates the time signatures that were analyzed to produce the power spectra in Figure 40. Marked exponential peaks with no subsequent oscillation are virtually not present in the first 30 plots, but increase as the bit continues to fail.
[0067] 3038070 78 The characteristic decomposition times of oscillations also decrease clearly. What is not so obvious is the general tendency for longer accumulations to break the rock or trephine, although a detailed examination of the plots also demonstrates this.
[0068] The expected values and variances of all parameters will vary with time and lithology as well as with drilling efficiency. In order to break a given mass of rock, a different amount of energy is needed as a function of the lithology. For a given bit edge, this means that the characteristic time TR and its distribution are a function of the lithology.
[0069] In various embodiments, described herein, an apparatus and method can be structured to provide a cutting edge measurement of a drill bit and measuring the size distribution of the cuttings of the formation when a drill bit is formed. advances. This may directly relate to evaluating the efficiency of a drilling operation and may be used to optimize this efficiency. Figure 42 is a flowchart of the features of an embodiment of an exemplary method 4200 using acoustic emissions and electromagnetic emissions emitted by a rock when it is broken during a drilling operation of a drill bit. drilling. At 4210, the acoustic emission 20 and the electromagnetic emission emitted by a rock when it is broken in a drilling operation of a drill bit are detected. At 4220, the acoustic emission is correlated with the electromagnetic emission. At 4230, the properties of rock, drill bit, or combinations thereof are estimated using correlation. The estimated properties may include, but are not limited to, rock chip size or drill bit cutting edge or drilling efficiency or a selected combination of rock chip size, drill bit cutting edge, or drill bit size. drilling efficiency. Correlation, estimation, or correlation and estimation may use a statistical frequency domain analysis, a statistical time domain analysis or both a statistical frequency domain analysis 3038070 and an analysis of the statistical frequency domain. of the field of statistical time. Drilling efficiency can be estimated and the estimated drilling efficiency can be used with a controller to control drilling operations relative to optimization of drilling efficiency.
[0070] Process 4200 or a similar method may include acquiring well-bottom drilling efficiency data in a drilling operation, and incorporating drilling efficiency data into a database and a model. control during drilling. The method 4200 or a similar method may include determining the friability of the rock by correlating electromagnetic emission to friability. The method 4200 or a similar method may include the detection of acoustic emission and electromagnetic emission by monitoring acoustic and electromagnetic emissions with sensors mounted in a drill bit, on a drill bit, near a drill bit, or a combination thereof. Such methods may include determining the cutting edge of the drill bit based on, through the sensors, the simultaneous use of the acoustic and electromagnetic signatures or the power spectra of the acoustic and electromagnetic signatures, or the cross power spectra of the acoustic and electromagnetic signatures, or combinations thereof. Such methods may include using, through the sensors, a variation in the obtained signature of the correlation of acoustic emission with electromagnetic emission as an indication of bit wear and efficiency. drilling. Such methods may include providing an indication of bit wear and drilling efficiency by using, through the sensors, the power spectral density of an acoustic signature, the spectral power signature of an electromagnetic signatum where the spectral power density cross between acoustic and electromagnetic signatures. Such methods may include identifying a drill bit cutter size distribution that is generated when the rock is broken by monitoring, with sensors, displacements in the acoustic spectra and electromagnetic spectra to the sample. higher frequencies and loss of signal amplitude. Such methods may include generating a lithology indicator based on a difference between the spectral components from a piezoelectric effect and a seismic effect. Such methods may include the identification of acoustic and electromagnetic emissions which are a signature of the bit fracture of the drill bit. The method 4200 or a similar method can detect acoustic emission and electromagnetic emission by monitoring acoustic and electromagnetic emissions through sensors mounted in a drill bit, on the drill bit, near the drill bit, or a combination thereof, transmitting, to a controller, data obtained by acoustic and electromagnetic signature detection and through their correlation; and modifying the bit weight or torque on the bit, or a timing of bending / flexion forces to a rotatable rotating system in response to the measured parameters, or a combination thereof to obtain a condition of optimum drilling efficiency, the modification performed through the controller working on the data. The method 4200 or a similar method may include the simultaneous use of the detected acoustic emission and the detected electromagnetic emission to identify an acoustic noise component and / or an electromagnetic noise component. The acoustic noise component can be identified from sources of acoustic noise including bit contact with the formation when it strikes one side of the wellbore, bouncing of the bit, contact of the drill string with the wellbore and cuttings striking a downhole module of the drill string. The component of the electromagnetic noise can be identified from the sources of electromagnetic noise and includes the flux potential from the drill bit nozzles and signals induced through the rotation of the drill string in the Earth's magnetic field. Method 4200 or a similar method may include calculating acoustic power spectrum from windowed samples of acoustic emissions; calculating an electromagnetic power spectrum from windowed samples of electromagnetic emissions; calculating a cross-power spectrum of windowed samples of acoustic emissions and electromagnetic emissions; estimating spectral parameters based on the acoustic power spectrum, the electromagnetic power spectrum and the cross power spectrum; using one of the spectral parameters selected to search for a module; and generating the parameters of bit weight, rotational speed, and flow rate through the drill bit by operating the search module based on one of the selected spectral parameters. Such methods may include dynamically changing bit weight (WOB) parameters, rotational speed (RS), and flow rate through the drill bit (Q) at the bottom of the well to control the efficiency of the drill bit. real-time drilling for optimization of drilling efficiency. The operation of the search module includes performing a gradient search, using a cost function, to determine the direction in space (WOB, RS, Q) in which the speed of the increase of the drilling efficiency is the largest. The features of any of the foregoing treatment techniques as described herein, or other combinations of features may be combined in a method according to the teachings herein.
[0071] In various embodiments, a computer-readable storage device may include instructions stored therein which, when executed by a computer, cause the computer to perform operations including one or more features. similar or identical to those described with respect to the methods and techniques described herein. One or more processors can operate the physical structures of such instructions. Execution of these physical structures may cause the machine to perform operations to: detect acoustic emissions and electromagnetic emissions emitted by a rock when it is broken in a drilling operation of a drill bit; correlate acoustic emission with electromagnetic emission; and to estimate the properties of rock, drill bit, or combinations thereof, using the correlation. Properties may include, but are not limited to, rock chip size or drill bit cutting edge or drilling efficiency or a selected combination of rock chip size, drill bit cutting edge or efficiency. drilling. The instructions may include instructions controlling the drilling operation. The operations may include operations to: calculate an acoustic power spectrum from acoustic emissions, an electromagnetic power spectrum from electromagnetic emissions, and a cross power spectrum from acoustic emissions and electromagnetic emissions; estimate the spectral parameters based on the acoustic power spectrum, the electromagnetic power spectrum and the cross power spectrum; and dynamically modifying, based on spectral parameters, bit weight (WOB), rotational speed (RS), and throughput parameters (Q), and determining the direction in which The space (WOB, RS, Q) in which the rate of increase of drilling efficiency is greatest. In addition, a computer readable storage device, here, is a physical device that is a non-transient device that records data represented by the physical structure within the device. Such a physical device may be a non-transient device. Examples of machine readable storage devices may include, without limitation, ROM, RAM, magnetic disk storage, optical storage, flash memory, and other electronic memory devices, magnetic and / or optical.
[0072] Figure 43 is a flowchart of an embodiment of an exemplary 4300 system that can be implemented at a drilling site to operate with increased drilling efficiency through the use of acoustic emissions and electromagnetic emissions from a drilling operation of a drill bit. The components of the 4300 system can be distributed throughout the drilling site, such as at the surface or bottom of the well. The 4300 system can be placed similarly or identically to the system associated with Figures 23-31. The system 4300 can be placed to perform various operations on acoustic data and electromagnetic data in a manner similar or identical to any of the treatment techniques described herein. The system 4300 may include sensors 4302, a processor 4341 and a memory 4342 operably coupled to the processor 4341. The sensors 4302 may include 4306 acoustic sensors and 4304 electromagnetic sensors and may be configured to detect acoustic emissions and emissions. electromagnetic emitted by a rock when it is broken in a drilling operation of a drill bit. The processor 4341 operably coupled to a memory 4342 may be placed to correlate the acoustic emission to the electromagnetic emission acquired by the sensors 4302 and to estimate the size of the rock chips, the cutting edge of the drill bit, the efficiency or a combination of rock chip size, drill bit cutting edge and drilling efficiency from the correlation. The 4300 system may also include an electronic apparatus 4343 and a communication unit 4345. The communication unit 4345 may comprise combinations of different communication technologies, which may include wired communication technologies and wireless technologies. The processor 4341, the memory 4342 and the communication unit 4345 can be set to function as a processing unit for controlling the drilling operation. In various embodiments, the processor 4341 may be embodied as a processor or group of processors capable of operating independently depending on the intended function. The 4341 processor may be structured on a drill string and may be structured to acquire drilling efficiency data at the bottom of the well in a drilling operation. The memory 4342 may be in the form of one or more databases.
[0073] The processor 4341 and the memory 4342 may be placed to correlate the electromagnetic emission sensed to the friability and to determine the friability of the rock. The processor 4341 or the processor 4341 and the memory 4342 may comprise an efficiency calculation module and a search module for dynamically modifying, based on spectral parameters, weight parameters on the bit (WOB), of rotation speed (RS) and flow rate through the drill bit (Q) and to determine the direction in space (WOB, RS, Q) in which the rate of increase of drilling efficiency is the bigger. The efficiency calculation module and the search module can be structured to calculate an acoustic power spectrum from acoustic emissions, an electromagnetic power spectrum from electromagnetic emissions and a cross power spectrum from emissions. acoustic and electromagnetic emissions, and to estimate spectral parameters based on the acoustic power spectrum, the electromagnetic power spectrum and the cross power spectrum.
[0074] The sensors 4302 may comprise sensors mounted in the drill bit of the drilling operation, on the drill bit, in the vicinity of the drill bit, or a combination thereof. The processor 4341 and the memory 4342 may be placed to generate acoustic and electromagnetic signatures or power spectra of acoustic and electromagnetic signatures, or cross-power spectra and electromagnetic signatures, or a combination thereof, across emissions received at the drill bit, on the drill bit, in the vicinity of the drill bit, or a combination thereof. The processor 4341 and the memory 4342 are placed to determine the variation in a signature obtained from the correlation of the acoustic emission with the electromagnetic emission as an indicator of bit wear and drilling efficiency. emissions received at the drill bit, on the drill bit, in the vicinity of the drill bit, or a combination thereof. The 4300 system may also include a 4347 bus, the 4347 bus having electrical conductivity among the 4300 system components. The bus 4347 may include a bus address, a data bus, and a control bus, each independently configured . The bus 4347 may be configured using a number of different telecommunication backgrounds for the distribution of 4300 system components. The bus 4347 may include instruments for network communication. The use of the 4347 bus may be regulated by the 4341 processor. The 4300 system may also include peripheral devices 4346. The peripheral devices 4346 may include displays, additional storage memory, or other control devices that may operate in conjunction with the 4341 processor or the 4342 memory. The peripheral devices 4346 may be arranged with a display, as a distributed component, which may be used with instructions stored in the memory 4342 to implement a user interface 4362 to manage the device. operation of the 4300 system according to its implementation in the system architecture. Such a user interface 4362 may be used in conjunction with a communication unit 4345 and the bus 4347. The peripheral devices 4346 may comprise a controller, the controller may be positioned to control the drilling operation with respect to optimization of the efficiency based on the drilling efficiency data estimated from the processor 4341. The controller can be placed to receive data obtained by acoustic and electromagnetic signature detection and correlation thereof and to operate on the data to modify the data. weights on the bit or torque on the bit, or a timing of bending / flexion forces to a rotatable rotating system in response to the measured parameters.
[0075] A system 1 may comprise: sensors positioned to detect acoustic emission and electromagnetic emission emitted by a rock when it is broken in a drilling operation; a processor; and a memory operably coupled to the processor, processor and memory placed to correlate acoustic emission to electromagnetic emission acquired by the sensors and to estimate properties of the rock, drill bit, or combinations of 3038070 86 these. Properties may include, but are not limited to, rock chip size, drill bit cutting edge, drilling efficiency, or a combination of rock chip size, drill bit cutting edge, or drilling efficiency. drilling from the correlation.
[0076] A system 2 may comprise the structure of the system 1 and may include a controller positioned to control the drilling operation with respect to optimizing the drilling efficiency based on the estimated drilling efficiency data from the processor. A system 3 may comprise the structure of any one of the 1-2 systems and may include the structured processor on a drill string and is structured to acquire drilling efficiency data at the bottom of the well in one operation. drilling. A system 4 may comprise the structure of any of the 1-3 systems and may include the processor and memory placed to correlate electromagnetic emission to friability and to determine friability of the rock. A system 5 may comprise the structure of any one of 1-4 and may include the sensors mounted in a drill bit, on the drill bit, in the vicinity of the drill bit, or a combination thereof. A system 6 may comprise the structure of any one of the systems 1-5 and may include the processor and memory placed to generate acoustic and electromagnetic signatures or power spectra of acoustic and electromagnetic signatures, or power spectra. crossed acoustic and electromagnetic signatures, or a combination thereof. A system 7 may comprise the structure of any one of the systems 1-6 and may include the processor and memory placed to determine the variation in a signature obtained from the correlation of acoustic emission to electromagnetic emission as a indication of drill bit wear and drilling efficiency. A system 8 may comprise any of the systems 1-7 and may include a controller set to receive data obtained by acoustic and electromagnetic signature detection and correlation and to act on the data to change the weight on the data. the bit or torque on the bit, or a timing of bending / flexion forces to a rotatable rotating system in response to the measured parameters.
[0077] A system 9 may comprise the structure of any of the systems 1-8 and may include the structured processor to include an efficiency calculation module and a search module for dynamically modifying, based on the spectral parameters. , the parameters of weight on the bit (WOB), rotational speed (RS) and flow rate through the drill bit (Q) and to determine the direction in space (WOB, RS, Q) in which the rate of increase in drilling efficiency is greatest, the efficiency calculation module and the search module being structured to calculate an acoustic power spectrum from acoustic emissions, an electromagnetic power spectrum to electromagnetic emissions and a cross-power spectrum from acoustic emissions and electromagnetic emissions, and to estimate the spectral parameters based on the spectra of acoustic power, electromagnetic power spectra and cross power spectrum. One method 1 comprises: detecting an acoustic emission and electromagnetic emission emitted by a rock when it is broken in a drilling operation of a drill bit; correlation of acoustic emission with electromagnetic emission; and estimating, using the correlation, properties of rock, drill bit, and combinations thereof. Properties may include, but are not limited to, rock chip size or bit cutting edge or drilling efficiency or a selected combination of rock chip size, drill bit cutting edge, or drill bit size. drilling efficiency. A method 2 may comprise the elements of method 1 and may include correlation, estimation, or correlation, and the estimation uses a statistical frequency domain analysis, a statistical time domain analysis, or both allergy from the field of statistical frequency and a statistical domain analysis of time. A method 3 may comprise the elements of any of the processes 1-2 and may include the drilling efficiency being estimated and the estimated drilling efficiency may be used with a controller to control the drilling operations relative to a drilling operation. optimization of drilling efficiency. A method 4 may comprise the elements of any of the methods 1-3 and may include acquiring the well bottom drilling efficiency data in a drilling operation, and incorporating the efficiency data. drilling in a database and a control model being drilled.
[0078] A process 5 may comprise the elements of any of processes 1-4 and may include determining the friability of the rock by correlating electromagnetic emission to friability. A method 6 may comprise the elements of any of processes 1-5 and may include the detection of acoustic emission and electromagnetic emission by monitoring acoustic and electromagnetic emissions with sensors mounted in a drill bit. , on a drill bit, near a drill bit, or a combination thereof. A method 7 may comprise the elements of any of the methods 1-6 and may include determining the cutting edge of the drill bit 20 based on, through the sensors, the simultaneous use of the acoustic and electromagnetic signatures or the power spectra of acoustic and electromagnetic signatures, or cross-power spectra of acoustic and electromagnetic signatures, or combinations thereof. A method 8 may comprise the elements of any of the methods 1-7 and may include the use, through the sensors, of a variation in the obtained signature of the correlation of the acoustic emission on transmission electromagnetic as an indication of drill bit wear and drilling efficiency. A method 9 may comprise the elements of any of the methods 1-8 and may include providing an indication of drill bit wear and drilling efficiency using, through sensors, the power spectral density of an acoustic signature; the power spectral density of an electromagnetic signature; or the cross-power spectral density between acoustic and electromagnetic signatures.
[0079] A method 10 may comprise the elements of any of methods 1-9 and includes identifying a drill bit cuttings size distribution that is generated when the rock is broken by monitoring, with sensors, shifts in acoustic spectra and electromagnetic spectra to higher frequencies, and losses in signal amplitude. A method 11 may comprise the elements of any of the methods 1-10 and may include generating a lithology indicator based on a difference between the spectral components from a piezoelectric effect and a seismic effect.
[0080] A method 12 may comprise the elements of any of the methods 1-11 and may include monitoring to understand the identification of acoustic and electromagnetic emissions that are a signature of the drill bit tooth fracture. A method 13 may comprise the elements of any of the methods 1-12 and may include transmitting, to a controller, data obtained by acoustic and electromagnetic signature detection and through their correlation; and modifying the weight on the bit or the torque on the bit, or a timing of bending / bending force forces to a rotatable rotating system in response to the measured parameters, or a combination thereof to obtain a condition of optimum drilling efficiency, the modification performed through the controller working on the data. A method 14 may comprise the elements of any of the methods 1-13 and may include the simultaneous use of the detected acoustic emission and the detected electromagnetic emission to identify an acoustic noise component and / or a component electromagnetic noise and for differentiating 3038070 from the acoustic noise component and / or a component of the electromagnetic noise. A method 15 may comprise the elements of any of the methods 1-15 and includes an acoustic noise component being identified from acoustic noise sources including bit contact with the formation when striking a side of the well. drilling, bouncing of the bit, contact of the drill string with the wellbore and cuttings striking a downhole module of the drill string. A method 16 may comprise the elements of any of the methods 1-15 and may include an electromagnetic noise component identified from the sources of electromagnetic noise including the flux potential from the drill bit nozzles and the induced signals. through the rotation of the drill string in the Earth's magnetic field. A method 17 may comprise the elements of any of the methods 1-16 and may include computing an acoustic power spectrum from the windowed acoustic emission samples; calculating an electromagnetic power spectrum from windowed samples of electromagnetic emissions; calculating a cross-power spectrum of windowed samples of acoustic emissions and electromagnetic emissions; the estimate of the spectral parameters based on the acoustic power spectrum, the electromagnetic power spectrum and the cross power spectrum; using one of the spectral parameters selected to search for a module; and generating the parameters of bit weight, rotational speed, and flow rate through the drill bit by operating the search module based on one of the selected spectral parameters. A method 18 may comprise the elements of any one of methods 1 to 17 and may include dynamically changing bit weight (WOB) parameters, rotational speed (RS), and flow rate through the bit. drilling (Q) at the bottom of the well to control the drilling efficiency in real time relative to an optimization of drilling efficiency.
[0081] A method 19 may comprise the elements of any one of the methods 1 to 18 and may include the operation of a search module comprising performing a gradient search, using a cost function, to determine the direction in space (WOB, RS, Q) in which the rate of increase in drilling efficiency is greatest. A machine-readable storage device 1 having instructions stored thereon which, when executed by a machine, causes the machine to perform operations, the operations comprising: detecting an acoustic emission and an electromagnetic emission emitted by a rock 10 when it is broken in a drilling operation of a drill bit; the correlation of acoustic emission with electromagnetic emission and the estimation, using correlation, of the properties of the rock, the drill bit, or combinations thereof. Properties include, but are not limited to, rock chip size or drill bit cutting edge or drilling efficiency or a selected combination of rock chip size, drill bit cutting edge or efficiency. drilling. A machine readable storage device 2 may comprise the structure of the machine readable storage device 1 and may include operations including: computing an acoustic power spectrum from acoustic emissions, an electromagnetic power spectrum from electromagnetic emissions and a cross-power spectrum from acoustic emissions and electromagnetic emissions; estimate the spectral parameters based on the acoustic power spectrum, the electromagnetic power spectrum and the cross power spectrum; and dynamically modifying, based on spectral parameters, bit weight (WOB), rotational speed (RS), and throughput parameters (Q), and determining the direction in which the space (WOB, RS, Q) in which the rate of increase of drilling efficiency is greatest. Although specific embodiments have been illustrated and described herein, it will be appreciated by those skilled in the art that any arrangement that is calculated to achieve the same purpose may be substituted by the specific embodiments illustrated. Various embodiments use permutations and / or combinations of the embodiments described herein. It should be understood that the foregoing description is intended to be illustrative, not restrictive, and that the phraseology or terminology used herein is for descriptive purposes. Combinations of the aforementioned embodiments, and other embodiments, will become apparent to those skilled in the art after studying the above description.

权利要求:
Claims (30)
[0001]
REVENDICATIONS1. Simultaneous acoustic and electromagnetic emission measurement system for rock breakage comprising: sensors (102, 103; 112; 502, 503; 1002-1, 10D2-2, 1002-3, 1103; 1201, 1202, 1203) arranged to detect acoustic emission and electromagnetic emission emitted by a rock when it is broken in a drilling operation of a drill bit (105; 1205); a processor; and a memory operatively coupled to the processor, processor and memory placed to correlate acoustic emission to electromagnetic emission acquired by the sensors (102, 103; 112; 502, 503; 1002-1, 1002-2; 1002-3, 1103; 1201, 1202, 1203) and for estimating rock chip size, drill bit cutting edge (105; 1205), drilling efficiency, or a combination of rock chip size , cutting edge of the drill bit (105; 1205) and drilling efficiency from the correlation. 20
[0002]
The system of claim 1, wherein the system comprises a controller positioned to control the drilling operation with respect to optimization of drilling efficiency based on the estimated drilling efficiency data from the processor. 25
[0003]
The system of claim 1, wherein the processor is structured on a drill string and is structured to acquire drilling efficiency data at the bottom of the well in a drilling operation. 30
[0004]
The system of claim 1, wherein the processor and the memory are set to correlate the electromagnetic emission with friability and to determine the friability of the rock.
[0005]
The system of claim 1, wherein the sensors (102, 103; 112; 502, 503; 1002-1, 1002-2, 1002-3, 1103; 1201, 1202, 1203) comprise sensors (102, 103 112, 502, 503, 1002-1, 1002-2, 1002-3, 1103; 1201, 1202, 1203) mounted in a drill bit (105; 1205) on the drill bit (105; 1205); near the drill bit (105; 1205), or a combination thereof.
[0006]
The system of claim 5, wherein the processor and the memory are arranged to generate acoustic and electromagnetic signatures or power spectra of acoustic and electromagnetic signatures, or cross power spectra of acoustic and electromagnetic signatures, or combination of these. 15
[0007]
The system of claim 5, wherein the processor and the memory are arranged to determine the variation in a signature obtained from the correlation of the acoustic emission to the electromagnetic emission as an indication of bit wear (105; 1205) and drilling efficiency. 20
[0008]
The system of claim 1, wherein the system comprises a controller set to receive data obtained by acoustic and electromagnetic signature detection and correlation thereof and operate on the data to modify the bit weight (105; 1205). or the torque on the bit (105; 1205), or a timing of bending / bending angle forces to a rotatable rotating system in response to the measured parameters.
[0009]
The system of claim 1, wherein the processor comprises an efficiency calculation module and a search module for dynamically modifying, based on the spectral parameters, the weight parameters on the bit (105; ) (WOB), rotational speed (RS) and flow rate through the drill bit (105; 1205) (Q) and to determine the direction in space 3038070 95 (WOB, RS, Q) in which the speed of increase in drilling efficiency is the largest, the efficiency calculation module and the research module being structured to compute a sound power spectrum from acoustic emissions, an electromagnetic power spectrum from electromagnetic emissions and a cross-power spectrum from acoustic emissions and electromagnetic emissions, and to estimate spectral parameters based on acoustic power spectra, spectra of electromagnetic power and cross power spectrum.
[0010]
A method (4200) of simultaneous measurements of acoustic and electromagnetic emissions from rock breakage including: acoustic emission detection and electromagnetic emission from a rock when broken in a drilling operation. a drill bit (105; 1205); correlation of acoustic emission with electromagnetic emission; and estimating, using rock chip size correlation or drill bit cutting edge (105; 1205) or drilling efficiency or a selected combination of rock chip size, bit sharpness drilling (105; 1205) or drilling efficiency.
[0011]
The method (4200) of claim 10, wherein the correlation, the estimate, or the correlation and the estimate use a statistical frequency domain analysis, a statistical time domain analysis or both an analysis. of the field of statistical frequency and an analysis of the statistical time domain.
[0012]
The method (4200) of claim 10, wherein the drilling efficiency is estimated and the estimated drilling efficiency is used with a controller to control the drilling operations relative to an optimization of the drilling efficiency.
[0013]
The method (4200) of claim 12, wherein the method comprises acquiring data of well drilling efficiency in a drilling operation, and incorporating drilling efficiency data. in a database and a control model being drilled. 5
[0014]
The method (4200) of claim 10, wherein the method comprises determining the friability of the rock by correlating electromagnetic emission to friability.
[0015]
The method (4200) of claim 10, wherein the detection of acoustic emission and electromagnetic emission by monitoring acoustic and electromagnetic emissions with mounted sensors (102, 103; 112; 502, 503; 1002). -1, 1002-2, 1002-3, 1103, 1201, 1202, 1203) in a drill bit (105; 1205) on a drill bit (105; 1205) near a drill bit ( 105; 1205), or a combination thereof. 15
[0016]
The method (4200) of claim 15, wherein the method comprises determining the cutting edge of the drill bit (105; 1205) based on, through the sensors (102, 103; 112; 502; 503; 1002). 1, 1002-2, 1002-3, 1103, 1201, 1202, 1203), simultaneous use of acoustic and electromagnetic signatures or power spectra of acoustic and electromagnetic signatures, or cross power spectra of acoustic signatures. and electromagnetic, or combinations thereof.
[0017]
17. The method (4200) of claim 15, wherein the method comprises using, through the sensors (102, 103; 112; 502, 503; 1002-1, 1002-2, 1002-3, 1103; 1201, 1202, 1203), a variation in the signature obtained from the correlation of acoustic emission to electromagnetic emission as an indication of drill bit wear (105; 1205) and drilling efficiency. 30
[0018]
18. The method (4200) of claim 15, wherein the method comprises providing an indication of drill bit wear (105; 1205) and drilling efficiency using, through the sensors ( 102, 103, 112, 502, 503, 1002-1, 1002-2, 1002-3, 1103, 1201, 1202, 1203), the power spectral density of an acoustic signature, the power spectral signature of a electromagnetic signature where the spectral power density cross between acoustic and electromagnetic signatures.
[0019]
19. The method (4200) of claim 15, wherein the method comprises identifying a size distribution of cuttings of a drill bit (105; 1205) that are generated when the rock is broken by monitoring, with sensors (102, 103, 112, 502, 503, 1002-1, 1002-2, 1002-3, 1103, 1201, 1202, 1203), shifts in acoustic spectra and electromagnetic spectra to higher frequencies and losses in the amplitude of the signal.
[0020]
20. The method (4200) of claim 15, wherein the method comprises generating a lithology indicator based on a difference between the spectral components from a piezoelectric effect and a seismoelectric effect.
[0021]
21. The method (4200) of claim 15, wherein the monitoring comprises identifying acoustic and electromagnetic emissions which are a signature of the bit fracture of the drill bit (105; 1205).
[0022]
The method (4200) of claim 15, wherein the method comprises: transmitting, to a controller, data collected through acoustic and electromagnetic signature detection and through their correlation; and modifying the weight on the bit (105; 1205) or the torque on the bit (105; 1205), or a timing of bending / bending angle forces to a rotatable system in response to the measured parameters or a combination of these to obtain a condition of optimum drilling efficiency, the modification performed through the controller working on the data. 30
[0023]
23. The method (4200) of claim 10, wherein the method comprises the simultaneous use of the detected acoustic emission and the detected electromagnetic emission to identify a component of the acoustic noise and / or a component of the acoustic noise. electromagnetic noise and to differentiate in relation to the component of the acoustic noise and / or the component of the electromagnetic noise.
[0024]
The method (4200) of claim 23 wherein the acoustic noise component is identified from acoustic noise sources comprising bit contact (105; 1205) with the formation when striking one side of the wellbore, the bouncing of the bit (105; 1205), the contact of the drill string with the wellbore and the cuttings striking a downhole module of the drill string.
[0025]
25. The method (4200) of claim 23, wherein the electromagnetic noise component is identified from the electromagnetic noise sources including the flux potential from the drill bit nozzles (105; 1205) and induced signals. through the rotation of the drill string in the Earth's magnetic field.
[0026]
26. The method (4200) of claim 10, wherein the method comprises: calculating an acoustic power spectrum from windowed acoustic emission samples; calculating an electromagnetic power spectrum from windowed samples of acoustic emissions; calculating a cross-power spectrum from windowed samples of acoustic emissions and electromagnetic emissions; estimating spectral parameters based on the acoustic power spectrum, the electromagnetic power spectrum and the cross power spectrum; providing the selected spectral parameters to a search module; and generating the weight parameters on the bit (105; 1205), rotational speed, and flow rate through the drill bit (105; 1205) by operating the search module based on the selected spectral parameters.
[0027]
27. The method (4200) of claim 26, wherein the method comprises dynamically changing bit weight (105; 1205) (WOB), rotational speed (RS), and throughput parameters. drill bit (105; 1205) (Q) at the bottom of the well to control the drilling efficiency in real time relative to an optimization of drilling efficiency. 10
[0028]
The method (4200) of claim 27, wherein the operation of the search module comprises performing a gradient search using a cost function to determine the direction in space (WOB, RS, Q). ) in which the speed of increase in drilling efficiency is greatest. 15
[0029]
29. A machine-readable storage device having instructions stored thereon which, when executed by a machine, cause the machine to perform operations, the operations comprising: detecting acoustic emission and electromagnetic emission emitted by a rock when it is broken in a drilling operation of a drill bit (105; 1205); correlation of acoustic emission with electromagnetic emission; and estimating, using rock chip size correlation or drill bit cutting edge (105; 1205) or drilling efficiency or a selected combination of rock chip size, bit sharpness drilling (105; 1205) or drilling efficiency.
[0030]
30. The machine readable storage device of claim 29, wherein the operations include: calculating an acoustic power spectrum from acoustic emissions, an electromagnetic power spectrum from electromagnetic emissions, and a cross-power spectrum from acoustic emissions 3038070 100 and electromagnetic emissions; estimating spectral parameters based on the acoustic power spectrum, the electromagnetic power spectrum and the cross power spectrum; and dynamically modifying, based on spectral parameters, weight parameters on the bit (105; 1205) (WOB), rotational speed (RS), and flow rate through the drill bit (105; 1205). ) (Q) and determine the direction in space (WOB, RS, Q) in which the rate of increase in drilling efficiency is greatest. 10

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同族专利:

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公开号 | 公开日

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WO2017003434A1|2017-01-05|

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CA2990154A1|2017-01-05|

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US20180171772A1|2018-06-21|

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法律状态:
2017-04-12| PLFP| Fee payment|Year of fee payment: 2 |

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2018-04-25| PLFP| Fee payment|Year of fee payment: 3 |

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2018-07-06| PLSC| Search report ready|Effective date: 20180706 |

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2019-05-23| PLFP| Fee payment|Year of fee payment: 4 |

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2021-02-12| ST| Notification of lapse|Effective date: 20210105 |

优先权:

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申请号 | 申请日 | 专利标题

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PCT/US2015/038354|WO2017003434A1|2015-06-29|2015-06-29|Apparatus and methods using acoustic and electromagnetic emissions|

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