专利摘要:
A method for estimating acoustic slowness, comprising: obtaining (700) a plurality of acoustic waveforms that are received by a plurality of receivers of a logging tool after transmission of a source acoustic wave by a transmitter, obtaining (710) models of slowness of the underground formation, a slowness model being defined by at least one constant slowness cell for at least one energy mode of calculating (720), for each slowness model, a set of candidate path times, a candidate path time of a set of candidate path times being calculated for a wave energy mode and a position of a receiver of the plurality of receivers, calculating (730) a relevance indicator for each set of candidate travel times, based on the recorded acoustic waveforms; searching (740) for a correspondence between the candidate travel time sets and the recorded acoustic waveforms by searching for an index of relevance that is optimal, and computing (750) an estimate of the acoustic slowness for underground training from a set of candidate travel times for which the relevance indicator is optimal. 公开号:FR3049355A1 申请号:FR1652596 申请日:2016-03-25 公开日:2017-09-29 发明作者:Bassem Khadhraoui；Saad Kisra 申请人:Services Petroliers Schlumberger SA； IPC主号:
专利说明:
METHOD AND DEVICE FOR ESTIMATING ACOUSTIC LENGTH IN UNDERGROUND FORMATION BACKGROUND OF THE INVENTION [0001] The acoustic logging may be performed in an underground formation using a logging tool, for example a cable working tool and / or a logging tool during drilling. A logging tool is placed in the subterranean formation (eg in a borehole) and includes at least one transmitter for emitting a reference acoustic wave. The logging tool may include multiple receivers receiving and recording incoming acoustic waves after propagation of the source wave through the subterranean formation. Acoustic logging provides acoustic data that can be used to characterize physical properties of the subterranean formation, such as properties of rocks within the subterranean formation. Acoustic data can be used to estimate acoustic delays (eg the inverse of velocity) in different parts of the subterranean formation. Slowness can be defined as the time a wave travels over a certain distance and can be measured in microseconds per foot. [0002] An acoustic slowness can be estimated by using either motion detection algorithms or look-alike processing algorithms, such as the Slowness Time Consistency (STC) algorithm. [0003] A first motion detection algorithm is disclosed, for example, in US6205087B1. This first motion detection algorithm operates on the basis of a single waveform, applying a waveform amplitude comparison with a given amplitude threshold defined by a user in a time window. When the waveform amplitude reaches for the first time the detection threshold, in the time window taken into account, the corresponding time value is calculated. The time values calculated for the recorded waveforms for the different logging tool receivers can be compared to each other. Incorrect time values can be identified and corrected accordingly. [0004] A second motion detection algorithm is described in the document entitled "Improved first-motion algorithm to compute high-resolution sony log" by HP Valero, M. Tejada, D. Murray, 2004, Society of Petroleum Engineers, 90995. This algorithm operates on the basis of a single waveform. An energy criterion is applied to each unique waveform to identify the waveform portion upon arrival of the first wave component (P-wave component). After the application of an energy criterion, a time window is defined to extract the waveform portion of interest. One or more criteria may be applied to the extracted waveform portions. For example, the Akaike Information Criterion (hereinafter referred to as AlC) or Bayes Information Criterion (hereinafter BIC) is then applied to the extracted waveform portion, to produce an estimate of the arrival time of the P wave component of the waveform taken into account. The AlC operator is based on the detection of a change of energy in a waveform, this however when it is used on the basis of a single waveform. The operator AlC therefore has a high sensitivity to unwanted early arrivals and can thus lead to a false detection of arrival time. When the waveforms recorded by the logging tool's receivers have been processed, statistical analysis can be applied to detect erroneous arrival times and to adjust erroneous arrival times. The detection of erroneous arrival times can be performed globally, by comparing the arrival times for different receivers taking into account the uniform spacing of the receivers, and / or from a single receiver. An example STC algorithm is disclosed in the document entitled "Semblance Processing of Borehole Acoustic Array Data", Geophysics, vol. 49, no. 3, pp. 274-281, by CV Kimball and T. Marzetta, 1984. While motion detection algorithms rely on estimation of arrival times for P-wave components, STC algorithm can estimate the speed of multiple wave components (eg P-waves, S-waves, etc.) incoming waves. The STC algorithm is based on a comparative analysis of multiple incoming waves received by uniformly spaced receivers of a logging tool for a single emission of a source acoustic wave by a transmitter of the logging tool. The STC algorithm identifies the similarity (appearance analysis) between portions of the received waveforms taking into account the uniform spacing of the receivers. The output of the STC is then presented to an automatic post-processing algorithm that is called a "slowness relabeling". It may be that these algorithms are not robust enough, so that the slowness log output calculated by the latter must be subject to a quality control by users to, for example, to detect a detection wrong arrival time. When the quality of the recorded arrival waves is poor, for example due to acoustic noise in the borehole, the users often proceed to the editing of the output slowness logs, based on the appearance analysis waveforms. SUMMARY OF THE INVENTION The purpose of the summary of this invention is to present a selection of concepts which are described in more detail below in the detailed description. This summary is not intended to identify key or essential features of the subject matter of the invention, nor is it intended to contribute to limiting the scope of the subject matter of the invention. The embodiments of the disclosure may include one or more devices, devices, systems, methods, products in the form of computer programs, or computer readable media. In still other aspects, the disclosure relates to a method for estimating acoustic slowness, comprising the recording of multiple acoustic waveforms received by multiple receivers after the emission of a source acoustic wave by a transmitter through the subterranean formation to obtain multiple recorded acoustic wave forms, said multiple receivers being located at different positions in the subterranean formation. The method further includes obtaining at least two models of slowness of the subterranean formation, a slowness model being defined by at least one constant slowness cell; and calculating, for each slowness model, a set of candidate path times. A candidate path time of a set of candidate path times corresponds to a wave energy mode and a position of the receivers. The method further includes calculating a relevance indicator for each set of candidate travel times based on the recorded acoustic waveforms; searching for a match between the candidate travel time sets and the recorded acoustic waveforms by searching for the maximum relevance indicator; and calculating an estimate of the acoustic slowness for the subterranean formation from a set of candidate travel times for which the relevance indicator is maximum. In still other aspects, the disclosure relates to a product in the form of a computer program or a computer-readable medium comprising instructions executable by a computer which, when executed by a processor, cause said processor to execute a method for estimating acoustic slowness according to any one of the embodiments disclosed herein. In still other aspects, the disclosure relates to a computer system comprising one or more processors for processing data; and a memory operatively coupled to said one or more processors, which includes program instructions for causing said one or more processors to perform a method of estimating the acoustic slowness according to any one of the embodiments disclosed herein. BRIEF DESCRIPTION OF THE DRAWINGS The features and advantages of the embodiments described will be more readily understood by reference to the following description with reference to the accompanying drawings. [0013] FIG. 1 illustrates a first embodiment of a system comprising a logging tool for generating acoustic data; [0014] FIG. 2 illustrates a second embodiment of a system comprising a logging tool for generating acoustic data; [0015] FIG. 3 illustrates a third embodiment of a system comprising a logging tool for generating acoustic data; Figs. 4A and 4B illustrate an example of an embodiment of a computer system for processing acoustic data; [0017] FIG. 4C illustrates an example of an acoustic data processing system; Figs. 5A and 5C illustrate some aspects of acoustic data processing; [0019] FIG. 5D illustrates some aspects of acoustic waveform processing; [0020] FIG. Figure 6 illustrates an exemplary flowchart of an acoustic data processing method; Figs. 7A and 7B illustrate an exemplary flowchart of a method for estimating acoustic slowness; [0022] FIG. Figure 8 illustrates some aspects of acoustical waveform processing; [0023] FIG. 9 illustrates some aspects of acoustic waveform processing; and [0024] FIG. Figure 10 illustrates some aspects of acoustic waveform processing. DETAILED DESCRIPTION OF THE INVENTION The examples disclosed in this document relate to the acquisition of acoustic data for an underground formation, and the analysis of these acoustic data to characterize the physical properties (for example petrophysical, geophysical, mechanical, structural) of the underground formation. , for example, to allow accurate and / or reliable estimation of the slowness for at least a portion of the subterranean formation. The underground formation may be a natural formation or an artificial formation. An underground formation is located in an underground geological area. An underground geological area is a geographic area that is below the land or ocean level. In one or more embodiments, the subterranean geologic region includes the subterranean formation in which a borehole is or may be drilled and any subterranean region that may influence drilling of the borehole, for example due to constraints and deformations present in the subterranean region. In other words, the underground geological area may include not only the area immediately around a borehole or replacement hole where a borehole could be drilled, but also any zone that influences or could influence the borehole or replacement where the borehole could be drilled. One or more embodiments of the technology may relate to an estimate of the slowness in the formation located around a borehole. An estimate of slowness can be used to identify natural gas entry points in the borehole. An estimate of slowness may also be used to estimate the porosity of a rock or other material forming the borehole, to characterize the anisotropies or induced or natural orientations of the rock, or to characterize the geomechanical properties of the rock. the rock to, for example, define a fluid weight to be used during the drilling of the borehole. An estimate of slowness can also be used to establish a time / depth relationship for the borehole, thus enabling conversion of seismic data acquired for the borehole into depth data, and to produce a mapping of the properties of the hole. sounding. One or more embodiments of the technology may relate to the real-time management of drilling operations. In particular, a drilling model is calibrated. Simulations are performed continuously using the calibrated drill model. A predicted measurement value from the simulations is compared to a real measured value acquired in the field. If the actual measurement value corresponds to the simulated measurement value, the simulations can be used to determine a simulated state of the drilling operation. On the basis of the simulated state, a state of the drilling operation is determined and one or more control signals of the drilling operations are executed. One or more embodiments of the technology may relate to a real-time system based on a drilling simulation for monitoring, diagnostics and optimization of a drilling operation. In particular, one or more embodiments can perform diagnostics and optimization for drilling. For example, one or more embodiments can achieve real-time vibration mitigation, real-time velocity-of-penetration (ROP) optimization, real-time monitoring of the trajectory, and a recommendation for directional drilling, real-time optimization of borehole quality, borehole logging / measurement during drilling (LWD / MWD) in real time, quality assurance of measurements, real-time monitoring of fatigue life , real-time load balancing of the drill bit, real-time monitoring of drill bit and reamer wear, and real-time buckling and tool weight (WOB) monitoring. Path monitoring may include ensuring that the path is within a threshold of the intended intended direction. The quality of the borehole is the degree of straightness of the hole. The management of the fatigue life is a management of the constraints applied to the equipment, for example during the rotation during the drilling of the hole. [0030] FIG. 1 illustrates a drilling rig system in which the examples disclosed in this document can be implemented. The drilling site can be located on land or at sea. In this exemplary system, a borehole 11 is formed in underground formations by rotary drilling. However, the examples described herein may also use directed drilling, as will be described hereinafter. A drill string 12 may be suspended within the borehole 11 and has a downhole assembly 100 which includes a drill bit 105 at its lower end. The surface system may include an assembly consisting of a platform and a boring tower 10, positioned above the borehole 11. The assembly 10 may include a turntable 16, a shank drive 17, a hook 18 and a rotary injection head 19. The drill string 12 can be rotated by the rotary table 16. The rotary table 16 can be actuated by a device or a system not shown right here. The rotary table 16 can engage with the drive rod 17 at the upper end of the drill string 12. The drill string 12 can be suspended from the hook 18, which is attached to a muffle mobile (which is not represented here either). The drill string 12 can be positioned through the drive rod 17 and the rotary injection head 19, which allows the drill string 12 to pivot relative to the hook 18. A drive system The upper system may be used to impart rotational movement to the drill string 12. In this example, the surface system further includes a fluid or drilling mud 26 stored in a pit 27 formed on the site. drilling. A pump 29 brings the drilling fluid 26 inside the drill string 12 through an orifice in the rotary injection head 19, which results in a downward flow of the drilling fluid. 26, which passes in the drill string 12, in the direction indicated by the arrow 8. The drilling fluid 26 exits the drill string 12 through holes in the drill bit 105 and then back through the annular space between the outside of the drill string 12 and the wall of the borehole 11, in the direction indicated by the arrows 9. In this way, the drilling fluid 26 lubricates the drilling tool 105 and transports the cuttings to the surface as it is returned to the pit 27 for recirculation. The bottom hole assembly 100 of the example illustrated in FIG. 1 includes a logging-while-drilling (LWD) module 120, a measurement-while-drilling (MWD) module 130, a steerable rotary system and motor 150, and the drilling tool 105. The LWD module 120 may be housed in a special type of mass-rod and may include one or more logging tools. In some examples, the downhole assembly 100 may include additional LWD and / or MWD modules. The LWD 120 may include capabilities for measuring, processing, and storing information, as well as communicating with surface equipment. The LWD 120 may include an acoustic measuring device. The MWD module 130 may also be housed in a drill collar and may include one or more devices for measuring characteristics of the drill string 12 and / or the drill bit 105. The MWD module 130 may further include an apparatus (not shown here) for generating electrical energy for at least some portions of the downhole assembly 100. The apparatus for generating electrical energy may include a turbogenerator driven by the sludge, actuated by the flow of the drilling fluid. However, other power systems and / or batteries can be used. In this example, the MWD 130 module includes one or more of the following types of meters: a tool weight meter, a torque meter, a vibration meter, a meter shock, a stick-slip measuring device, a direction measuring apparatus and / or a tilt measuring apparatus. [0035] Although the elements of FIG. 1 are represented and described as being implemented in a particular type of transport, the examples disclosed in this document are not limited to a particular type of transport but, instead, may be implemented in conjunction with different types of transport. transport including, for example, spiral tubes, a cable drill pipe and / or any other types of transport known in the industry. [0036] FIG. 2 illustrates an acoustic borehole logging tool (LWD) that can be used to implement the LWD tool 120, or can be part of a series of LWD 120A tools. An offshore platform 210 having a source or set of acoustic emission 214 may be deployed near the water surface. In at least some embodiments, any other type of source (or "downhole") or "downhole" source may be present to transmit the acoustic signals. In some examples, a downhole processor controls the transmissions from the transmitter 214. The hole-head equipment may also include acoustic receivers (not shown here) and a recorder (not shown here) for capturing reference signals near the source of the signals (e.g., transmitter 214). The hole-head equipment may also include telemetry equipment (not shown here) to receive MWD signals from the downhole equipment. The telemetry equipment and the recorder may be coupled to a processor (not shown here) so that the recordings can be synchronized using downhole and downhole clocks. A downhole LWD module 200 includes at least acoustic receivers 230 and 231, which are coupled to a signal processor, so that recordings can be made of signals detected by the receivers in synchronization with the transmissions of the source. of signals. In operation, the transmitter 214 transmits signals and / or waves that are received by one or more receivers 230, 231. The received signals can be recorded and / or recorded in legacies to generate associated waveform data. The waveform data may be processed by processors 232 and / or 234 to determine slowness values as disclosed herein. [0039] FIG. 3 is an example of an apparatus that can be used to implement the examples disclosed herein. In some examples, underground formations 331 are traversed by a borehole 332. The borehole 332 may be filled with fluid and / or drilling mud. In the illustrated example, a logging tool 310 is suspended from an armed cable 312 and may be provided with optional centering devices. The cable 312 extends up the borehole 332, and passes around a muffle pulley 320 on a drilling rig 321 before arriving at a winch which is part of the surface equipment 350. An apparatus depth measurement may be present to measure the movement of the cable over the muffle pulley 320 and the depth at which the logging tool 310 is located in the borehole 332. In some examples, a device is included in the logging tool 310 to produce a signal indicative of an orientation of the body of the logging tool 310. Processing circuits and interface present in the tool log files 310 amplify, sample and / or digitize the information signals of the tool for transmission, and communicate the signals to the surface equipment 350 via, for example, cable 312. Electrical and control power supply for coordinating the operation of the logging tool 310 is generated by the surface equipment 350 and communicated through the cable 312 to the circuits present in the logging tool 310. The equipment surface includes a processor 370, peripheral equipment and / or a recorder 326. In the present description, reference is made to functions, motors, block diagrams and flow chart illustrations of methods, systems and computer programs according to one or more exemplary embodiments. Each function, engine, block schemas and flowchart illustration described can be implemented in hardware, software, software packages, middleware, firmware, or any combination of these suitable for this purpose. If implemented in software, the functions, the motors, the blocks of the block diagrams and / or the illustrations in the form of flowcharts can be implemented by instructions of computer programs or software codes, which can be stored or transmitted on a computer-readable medium, or loaded into a multi-purpose computer, a dedicated computer or other programmable data processing apparatus to produce a machine, such that computer program instructions or software code running on the computer or other programmable data processing device create the means to implement the functions described in this document. Embodiments of computer readable media include, but are not limited to, computer storage media and communications media including any medium that facilitates the transfer of a computer program from one location to another. other. More specifically, software instructions or program code readable by a computer, designed for the implementation of the embodiments described herein, may be stored, temporarily or permanently, in whole or in part, on a non-portable medium. computer-readable transient of a local or remote storage device comprising one or more storage media. As used herein, a computer storage medium can be any physical medium that can be read, written or accessed in the more general sense of the term by a computer. Examples of computer storage media include, but are not limited to, a flash disk or other flash memory devices (eg, USB sticks, USB pens, storage keys), a CD-ROM, or other storage device. optical, a DVD, a magnetic disk storage device or other magnetic storage device, a memory chip, a random access memory, a read only memory, an electrically erasable programmable read only memory, smart cards, or any other suitable media for this purpose among those that can be used to transport or store program code in the form of instructions or data structures that can be read by a computer processor. In addition, various forms of computer-readable media may be used to transmit or convey instructions to a computer, including a router, gateway, server, or other transmission device, wired (coaxial cable, fiber, pair). twisted, DSL cable) or wireless (infrared, radio, cellular, microwave). The instructions can include code from any computer programming language, including but not limited to C, C ++, Basic, HTML, PHP, Java, Javascript, and more. The computer system 100 can be implemented in the form of a single hardware device, for example in the form of a personal computer (PC) desktop, a laptop, a personal digital assistant , a smartphone, or it can be implemented on separate hardware devices interconnected, connected to each other by a communication link, with wired and / or wireless segments. In one or more embodiments, the computer system 100 operates under the control of an operating system and executes or relies in another way on a variety of software applications, components, programs, objects, modules or computer data structures, etc. As schematically illustrated in FIG. 4A, the computer system 400 includes a processing unit 410, a memory 411, one or more computer storage media 412, and other associated hardware such as input / output interfaces (e.g., device interfaces such as USB interfaces, etc., network interfaces such as Ethernet interfaces, etc.) and a reader 413 for read-write access to said one or more computer storage media. The memory 411 may be a random access memory, a cache memory, a non-volatile memory, a backup memory (for example programmable memories or flash memories), read-only memories, or any combination thereof. The processing unit 410 may be any microprocessor, integrated circuit, or central processing unit (CPU) suitable for this purpose, comprising at least one physical processor or a processing core. In one or more embodiments, the at least one computer storage medium 412 includes computer program instructions that, when executed by the computer system 400, cause the computer system 400 to execute one or more of the disclosed methods. in this document. The processing unit 410 is a physical processor that processes instructions. For example, the processing unit 410 may be an integrated circuit that serves to process instructions. For example, the processing unit may consist of one or more cores or microcines of a processor. The processing unit 410 of the computer system 400 may be configured to access said one or more computer storage media 412 for storing, reading and / or loading computer program instructions or software codes which, when they are executed by the processor, cause the processor to execute the blocks of a method described herein. The processing unit 410 may be configured to use the memory 411 when executing the blocks of a method described herein for the computer system 400, for example for loading computer program instructions and for storing data. data generated during the execution of computer program instructions. In one or more embodiments, the computer system 400 receives a number of inputs and outputs for the communication of information outside the system. For the interface with a user or an operator, the computer system 400 generally includes a user interface 414 incorporating one or more input / output devices intended for a user, such as a touch screen, a keyboard, a mouse, a microphone, a touchpad, an electronic pen, or any other type of input device. Furthermore, a user input can be received, for example, on a network interface coupled to a communication network, from one or more external computing devices or systems. The computer system 400 illustrated in FIG. 4A can be connected to a network or be part of a network. For example, as shown in FIG. 4B, the network 420 may comprise multiple nodes (for example X-node 422, Y-node 424). Each node may correspond to a computer system, such as the computer system shown in FIG. 4A, or a group of combined nodes may correspond to the computer system shown in FIG. 4A. By way of example, embodiments may be implemented on a node of a distributed system that is connected to other nodes. As another example, embodiments may be implemented on a distributed computer system having multiple nodes, each part of one or more embodiments may be located on a different node within the distributed computer system. In addition, one or more elements of the aforementioned computer system 400 may be located at a remote location and connected to the other elements on a network. Although this is not indicated in FIG. 4B, the node may correspond to a blade, in a server chassis, which is connected to other nodes via a backplane. As another example, the node may correspond to a server in a data center. As another example, the node may correspond to a computer processor or a micro-heart of a computer processor with memory and / or shared resources. The nodes (e.g., the X-node 422, the Y-node 424) in the network 420 may be configured to provide services for a client device 426. For example, the nodes may be part of a cloud system. The nodes may include functionality for receiving requests from the client device 426 and sending responses to the client device 426. The client device 426 may be a computer system, such as the computer system shown in FIG. 4A. In addition, the client device 426 may include and / or execute at least a portion of one or more embodiments. The computer system 400 (FIG 4A), an X node 422 or Y 424 (FIG 4B) of a computer system or the surface equipment 350 (FIG 3) may further comprise a data repository for storing acoustic data, intermediate data and / or resulting data. A data repository is any type of device and / or storage device (such as a file system, database, array collection, or other storage mechanism) for storing data . In addition, the data repository may include a plurality of different storage units and / or different storage devices. The plurality of different storage units and / or different storage devices may or may not be of the same type or located at the same physical location and / or on the same physical device. [0054] FIG. 4C illustrates a system comprising a data repository 440 for storing acoustic data and related data. Data repository 440 may be used to store recorded acoustic waveforms 452, preprocessed acoustic waveforms 459, receiver and transmitter positions 453, slowness models 454, including geological cells 456 , energy mode indices 460 and slowness values 455. Data repository 440 may be used to store other data related to the acoustic data, e.g., relevancy indicators 450, candidate travel times 451, corresponding travel times 458, and slow maps 457. The data repository 440 may be operatively connected to a field application 470 for performing field operations and / or for implementing a method disclosed herein. The field application may be performed by a device operatively connected to a logging tool, for example by the computer system 400 (Fig. 4A), by one or more X nodes 422, Y 424 (Fig. 4B) of a computer system, or by the surface equipment 350 (Fig. 3) or any other control device of a field operation. Acoustic waveform data processing methods acquired for underground formation are described in detail hereinafter. The methods may be implemented by a device operably connected to a logging tool 310, for example by the computer system 400 (Fig. 4A), by one or more X-nodes 422, Y 424 (Fig. 4B) of a computer system, or by the surface equipment 350 (Fig. 3) or any other control device of a field operation. In one or more embodiments, the logging tool 310 includes one or more transmitters and one or more receivers. The different receivers may or may not be of the same type. More generally, in the present disclosure, the word "different", with respect to receivers, is used to designate instances of receivers that may or may not be of the same type. Each transmitter of the logging tool is configured to emit a source acoustic wave. An acoustic wave can be a sound wave in the frequency range of 1 to 25 kHz. The source wave is an oscillating wave, for example a sine wave. The transmitter may be a unipolar transmitter or a bipolar transmitter. In the case of a unipolar transmitter, the energy of the source wave is emitted in each direction from a central position, while a bipolar transmitter emits energy in a certain direction. The emitted source wave can be received and recorded by the different receivers after its propagation through the subterranean formation, i.e. after its propagation through the borehole (for example through the fluid present in the borehole or through empty areas of the borehole) and / or after its reflection on the walls of the borehole and / or after its propagation along the walls of the borehole and / or after its refraction through the borehole walls and its propagation through solid materials of the subterranean formation (eg rock or materials in which the borehole is drilled). The wave propagation direction is perpendicular to the wavefront. A wave received and recorded by a receiver can thus comprise different types of wave components, according to the propagation path followed by the source wave before arriving at the receiver. The received wave may comprise, for example, wave components such as a P wave component, an S wave component, a Stoneley wave component, a mud wave component, a component Rayleigh wave, etc. For a given geological material (for example a given rock, a given fluid), each of these wave components has a specific propagation speed. A P wave (also called a compression wave) is an elastic wave that oscillates in the direction of propagation of the wave. A wave S (also called shear wave) is an elastic wave that oscillates perpendicular to the direction of propagation of the wave. A Stoneley wave is a wave that propagates along a solid / fluid interface, for example along the wall of a borehole filled with fluid. A P wave can be produced by reflection of a source wave on a wall of the borehole. A P-wave or S-wave can be produced when a source wave propagates through a borehole wall of a source wave and enters the subterranean formation in which the borehole has been drilled, being refracted (the propagation direction is changed when Bonde crosses the wall). A Rayleigh wave is a surface wave that moves near the surface of solid materials. A mud wave is a compression wave transmitted by a fluid in a borehole. When considering a single receiver, the different types of wave components produced by a source wave emitted by a transmitter, after propagation through the underground formation, arrive at the receiver at different arrival times. The waveform recorded by this receiver thus comprises different types of waveform components. Fig. 5D illustrates this aspect. After the emission of Fonde source at time TO, the wave component P arrives at time T1, the wave component S arrives at time T2, the Rayleigh wave component arrives at time T3, the wave component mud arrives at time T4, and the Stoneley wave component arrives at time T5. It will be noted that the different types of wave components may have different amplitudes, different frequencies or more generally different waveform attributes. Each wave component corresponds to a given energy mode and to a manner in which the acoustic energy of a wave propagates in one or more directions. For example, a first energy mode E1 corresponds to the P waves. A second energy mode E2 corresponds to the S waves. A third energy mode E3 corresponds to the Stoneley waves. In one or more embodiments, slowness models are used to estimate the slowness in an underground formation. The slowness model is a model of the slowness of acoustic waves through an underground formation. A slowness model is used to predict the travel time of an acoustic wave through an underground formation. A slowness model can predict the travel time of one or more wave components / acoustic wave wave energy modes. A slow model Sm can be defined by geological cells of constant slowness for a given energy mode / wave component (e). A geological cell thus has a constant slowness value for at least one energy mode / one wave component. Without loss of generality, the slowness model can be defined such that each geological cell has a constant slowness value for each energy mode. Therefore, the slowness model can be common to the various multiple energy modes. In at least some embodiments, a slowness model may be defined for a single energy mode and a plurality of slowness models may be defined for the various energy modes / wave components. A geological cell may correspond to a volume zone in the subterranean formation. Each geological cell may, for example, correspond to a given geological material or to an empty zone. A geological cell may, for example, be in the form of a three-dimensional parallelepipedal cell (3D) or any other form suitable for this purpose. A slow value can be associated with each geological cell of a slow model Sm and with each energy mode Ej, and stored in a memory, for example in the data repository 440. For each sm slow model, a ray throwing technique can be used to model the wave propagation paths in the multiple geological cells of the subterranean formation. The ray tracing technique is based on ray paths representing the propagation paths of the different types of wave components, and it can be used to predict or determine travel times or arrival times of the components of the beam. wave to the receivers after their propagation along the represented propagation paths. For simplicity, the moment when the emission of the source acoustic wave takes place can be used as a time reference and receive an arbitrary value of zero. Thus, the travel time of a wave component (i.e. the time period corresponding to its movement from a transmitter to a given receiver) is equal to the arrival time (c. ie the time value or the time stamp of the arrival of the wave component) of this wave component. [0067] Figs. 5A-5C illustrate several models of slowness for a given subterranean formation in which a borehole is drilled. The slowness model illustrated in FIG. 5A corresponds to a geologic four-cell slowness model 500-503, the geological cell 500 corresponding to the borehole itself, containing for example a fluid, and the geological cells 501-503 corresponding to different types of rocks or materials. forming the underground formation in which the borehole is drilled. With this first model of slowness, according to a ray tracing technique, a wave emitted by the transmitter T propagates through the geological cell 500, then is reflected by the borehole wall at the level of the geological cell 502 and or propagates along the wall of the borehole at the interface between the geological cell 500 and the geological cell 502, and finally propagates again through the geological cell 500 before reaching one of the RI receptors at R3. The slowness model illustrated in FIG. 5B corresponds to a seven-cell geological 510-516 slowness model, the geological cell 510 corresponding to the borehole itself, for example containing a fluid, and the geological cells 511-516 corresponding to different types of rocks or materials. forming the underground formation in which the borehole is drilled. With this second model of slowness, according to a ray tracing technique, a wave emitted by the transmitter T propagates through the geological cell 510, is refracted by the wall of the borehole at the interface between the geological cell 510 and the geological cell 515, is successively transmitted through one or more geological cells 515, 514, 513 and / or 512, is refracted again by the borehole wall at the interface between the geological cell 510 and one of the cells 512, 513 or 514, and finally propagates again through the geological cell 510 before reaching one of the receivers R1 to R3. The slowness model illustrated in FIG. 5C corresponds to a model of ten-cell geological delay 520-529, the geological cell 520 corresponding to the borehole itself, containing for example a fluid, and the geological cells 521 -529 corresponding to different types of rocks or materials forming the underground formation in which the borehole is drilled. With this third model of slowness, according to a ray tracing technique, a wave emitted by the transmitter T propagates through the geological cell 520, is refracted by the wall of the borehole at the interface between the geological cell 520 and the geological cell 528, is transmitted successively through one or more geological cells 527, 528, 525, 526, 524, 522 and / or 523, is refracted again by the borehole wall at the interface between the geological cell 520 and one of the geological cells 522, 524, 525 or 527, and finally propagates again through the geological cell 520 before reaching one of the R1 receptors at R3. The use of a slowness model comprising geological cells of constant slowness for a given wave component makes it possible to calculate the travel time of a wave component in a given geological cell of the slowness model. the basis of the slowness value associated with the given geological cell and the length of the portion of the propagation path within the given geological cell. A travel time for a given wave component between a transmitter at a given position and a receiver at a given position can then be calculated for each propagation path by calculating the sum of the different travel times in the different geological cells calculated for the different portions of the propagation path. For each model of slowness, a path time can thus be calculated, for a given transmitter position, for a given receiver position and for a given wave component. [0072] FIG. 6 is a flowchart according to one or more embodiments of a method for processing acoustic data acquired for a subterranean formation. Although the various blocks in the flowchart are presented and described sequentially, a person of ordinary technical skill will understand that at least some of the blocks may be executed in different orders, may be combined or omitted, and that at least some of the blocks can be run in parallel. In at least one embodiment, the method may be executed by the computer system 400 (Fig. 4A), one or more X-nodes 422, Y-node 424 (Fig. 4B) of a computer system, or by the surface equipment 350 (Fig. 3) operably connected to a logging tool, for example the logging tool 310, or by any other control device of a field operation. In the exemplary embodiment described with reference to FIG. 6, the underground formation includes a borehole. The method described with reference to FIG. 6 is referred to below as "forward modeling". This prospective modeling can be used for any receiver configuration, i.e. the receivers are uniformly spaced in the logging tool or irregularly spaced in the logging tool. In block 600, the positions of at least one transmitter and one or more receivers of a logging tool placed in the borehole are obtained. The position of a transmitter or receiver can be defined in a one-dimensional space (1D) corresponding to a straight line of the logging tool along which the transmitter and receivers are placed. When the logging tool is placed vertically in the borehole, the position of a transmitter or receiver can be defined as a depth value d in the borehole. For example, a transmitter and Λ / = 4 receivers are used. Assuming that the log includes a single transmitter, the position of the transmitter is noted as T (d) where d is the depth of the transmitter. Similarly, a receiver position is denoted as R (dn) where n is an integer value that varies from 1 to N = 4. The distance TR between a receiver at the position R (dn) and l The emitter at the position T (d) is thus TR = | dn - d |. In block 610, several slowness models are generated for the subterranean formation. For each slowness model, a set of geological cells with a constant slowness value is defined. A slow value associated with the geological cell for each possible energy mode is stored in memory, for example in the data repository 440. In block 620, one or more energy modes and / or wave components are selected from among the energy modes / wave components used in the control models. generated in block 610. For example, three energy modes Ei to Es are selected which correspond respectively to three wave components: the wave component P, the wave component S, and the wave component of Stoneley. An energy mode index j = 1 to 3 is associated with each energy mode Ej. In block 630, several slowness models are selected from the set of slowness models generated. For example, M = 10 slowness models are selected. A model index m = 1 to M is associated with each model of slowness Sm- [0079] At block 640, for each slowness model selected at block 630 and each energy mode selected at block 620, a travel time is calculated for each receiver position received at block 600, taking into account the position of the transmitter. Therefore, for a given slowness model, a set of travel times is calculated, each travel time corresponding to an energy mode and a position of a sink of the logging tool. At block 650, each of the travel times calculated in block 640 is stored in a memory, for example in data repository 440, in association with a model index of slowness m, an energy mode index J , a receiver position R (dn) for a receiver index n, and a transmitter position T (d). A calculated travel time for a slower model index m, a power mode index j, a receiver position R (dn) and a transmitter position T (d) is denoted as: [0081] FIG. 7A represents a flowchart according to one or more embodiments of a method of estimating the slowness for a subterranean formation. Although the various blocks in the flowchart are presented and described sequentially, a person of ordinary technical skill will understand that at least some of the blocks may be executed in different orders, may be combined or omitted, and that at least some of the blocks can be run in parallel. In at least one embodiment, the method may be executed by a device operably connected to a logging tool, for example by the computer system 400 (FIG 4A), by one or more X nodes 422, the Y node 424 (FIG. Fig. 4B) of a computer system, by surface equipment 350 (Fig. 3), or by any other control device of a field operation. At block 700, acoustic waveforms received by receivers after the emission of a source acoustic wave by an emitter through the subterranean formation are obtained. The receptors are located at different positions in the underground formation. Acoustic waveforms can be obtained directly or indirectly from the receivers. For example, acoustic waveforms can be obtained from the sensors and stored in the data repository. The acoustic waveforms can then be obtained from the data repository. In block 710, one or more models of slowness of the underground formation are obtained. A slowness model can be defined by one or more cells of constant slowness for one or more wave energy modes. For example, the slowness models can be obtained from the data repository 440. The slowness models can be defined for one or more acoustic waveform energy modes. At block 720, a set of candidate travel times is calculated for each slowness model obtained at block 710. A candidate travel time of a set of candidate travel times is calculated for an energy mode of wave and a position of a receiver of multiple receivers. In block 730, a relevance indicator is calculated for each set of candidate travel times on the basis of the acoustic waveforms obtained. At block 740, a match between the candidate path time sets and the recorded acoustic waveforms is searched for by looking for a relevance indicator that is optimal. In block 750, one or more estimates of the slowness are calculated for the underground formation from a set of candidate travel times for which the relevance indicator is optimal. [0088] FIG. 7B is a flowchart according to one or more embodiments of a method of estimating slowness for subterranean formation based on waveform data recorded by the receivers of a logging tool. Although the various blocks in the flowchart are presented and described sequentially, a person of ordinary technical skill will understand that at least some of the blocks may be executed in different orders, may be combined or omitted, and that at least some of the blocks can be run in parallel. In at least one embodiment, the method may be executed by a device operatively connected to a logging tool, for example by the computer system 400 (FIG 4A), by one or more X nodes 422, the Y node 424 (FIG. Fig. 4B) of a computer system, by surface equipment 350 (Fig. 3), or by any other control device of a field operation. In the exemplary embodiment described with reference to FIG. 7B, the underground formation is identical to that taken into account for modeling before. In addition, the logging tool is identical as well as the positions of the receivers and transmitter that are used for modeling before. The travel times stored at block 650 are used as the candidate travel time for estimating slowness based on the waveform data recorded by the receivers. The estimation of the slowness is carried out according to a method which is described below with reference to FIG. 7B. For real-time work, the forward modeling can be executed in advance in order to be ready for the processing of the first waveform data recorded by the receivers. According to another example, the forward modeling can be updated based on the result of the slowness estimation performed on the basis of the waveform data recorded by the receivers and the updated candidate path times used. to estimate slowness values for the subsurface formation based on the second waveform data newly recorded by the receivers. At block 800, a source wave is emitted by a transmitter of an acoustic logging tool at a depth d and waves are received by the N receivers of the acoustic logging tool at a depth dn where n is a value as an integer that varies from 1 to N are recorded. The acoustic logs produced by the acoustic logging tool include waveform data representing an acoustic wave received by a receiver. Each receiver n records at least one waveform noted in the form iv / ". The waveform data may include data representing P waves and unipolar S waves, bipolar bending waves and / or unipolar Stoneley waves, for example. Waveform data can be obtained during drilling (Fig. 1) and / or through a cable (Fig. 2) using a multi-mode acoustic tool. Unipolar waveform data, bipolar waveform data, quadrupole waveform data, pseudo-Rayleigh waveform data, and waveform data of the type Stoneley, can be obtained from the acoustic logs. The waveform data representing the recorded acoustic waveforms 452 may be stored in the data repository 440, or they may be directly sent via a communication link to a device functionally. connected to a logging tool 310 or a data repository 440. Multiple acoustic waveforms can thus be obtained for the multiple receivers. For example, the multiple acoustic waveforms may be read from or obtained from the data repository 440, received by a field application 470 operably connected to the data repository or logging tool, received by the computer system. 400 (Fig. 4A), by one or more X-nodes 422, the Y-node 424 (Fig. 4B) of a computer system, or by the surface equipment 350 (Fig. 3). At block 810, one or more energy modes are selected. The number of selected energy modes is noted as P, for example P = 3 and three energy modes Ei, E2 and E3 are selected; they correspond respectively to P waves, S waves and Stoneley waves. The selected energy modes and the corresponding wave components that are used for slowness estimation may include the energy modes / wave components selected in block 620 in the forward modeling. subset of these / them. The selection can be done manually by a user, or automatically from the set of energy modes selected in block 620 in the forward modeling. According to another example, P = 1 for the estimation of the slowness on unipolar data of P waves or for the estimation of the slowness on bipolar data of S waves. In block 820, pretreatment is performed on the waveform data. This preprocessing operation is optional. It improves the performance of the slowness estimation method. For each energy mode selected in block 810, a specific pretreatment of this energy mode is applied to each recorded waveform. Each waveform component corresponding to a wave component may have specific characteristics that other waveform components do not have; a specific amplitude, a specific frequency spectrum, or another specific waveform attribute. Thus, the pretreatment performed for a given energy mode on a recorded waveform can be performed based on one or more criteria (amplitude, amplitude standard deviation, frequency band, or other criteria which can be extracted from a waveform), so as to extract a given wave component from the processed waveforms, and thus to attenuate (eg reduce or eliminate) the other waveform components. which do not have the specific characteristic or characteristics of the given energy mode. A pretreated waveform, resulting from pretreatment of the waveform w / "received by the receiver n which extracts the waveform component corresponding to the energy mode Ej, will be noted as wf ^ ^. For example, as illustrated in FIG. 5D, the Stoneley wave component has an amplitude that is greater than that of the other waveform components and a lower frequency. Therefore, by filtering a recorded waveform to extract - by means of, for example, a low-pass filter - the Stoneley wave component, the other waveform components having a lower frequency are attenuated. lower amplitude and / or a higher frequency. According to another example illustrated in FIG. 5D, the Rayleigh wave component has a frequency that is greater than that of the other waveform components. Therefore, by filtering a recorded waveform to extract - by means of, for example, a high-pass filter - the Rayleigh wave component, the other waveform components having a lower frequency are attenuated. lower frequency. According to another example illustrated in FIG. 5D, the P wave component has an amplitude which is much smaller than that of the other waveform components. Therefore, by filtering a recorded waveform to extract - by means of, for example, an amplitude filter - the P-wave component, the other waveform components having an amplitude are attenuated. higher. In block 830, one or more slowness models are selected. Selected slowness models used for slowness estimation may include the slowness models selected in block 630 in the forward modeling, or a subset of them. The selection can be done manually by a user, or automatically from the set of slowness models selected in block 630 in the forward modeling. In block 840, the calculated travel times for the slowness models selected in block 830 and / or the energy modes selected in block 810 are obtained, for example, from data repository 440 in which the Travel times were stored at block 650 during the modeling before. These travel times are used as candidate travel times for estimating slowness. For each sm slow model, a set of N * P candidate path times is thus obtained, where N is the number of receivers for which waveform data are available and P is the number of selected energy modes. in block 810. A calculated candidate travel time for a slow model Sm, an energy mode Ej, a receiver position R (Dn) and a transmitter position T (d) is written as: where j varies from 1 to P, n varies from 1 to N and m varies from 1 to M. In block 850, an objective function is selected. The selection can be done manually by a user, or automatically from a set of available objective functions. An objective function is a function that is applied to a set of candidate path times of a given slowness model and to a set of waveforms (with preprocessing according to block 820, or without preprocessing) recorded by a or multiple receivers to generate a relevance indicator for the given slowness model. The relevance indicator of a slowness model is also referred to in this document as the "model relevance indicator". The objective function aims to provide a digital tool for automatically identifying the slowest model that best corresponds to a set of recorded waveforms. A model relevance indicator is generated based on the calculated candidate run times for that given slowness model and by comparison with the recorded waveforms (whether or not they have been preprocessed) . The model relevance indicator is a numerical value that is globally assigned to the given slowness model as a whole, and represents a level of relevance of this slowness model. In one or more embodiments, the objective function is configured to take into account several energy modes for the waveforms recorded by the various receivers of the logging tool. The objective function is an analysis tool that is much more robust to false detection of an arrival time compared to a waveform operator applied to a single waveform and to a single mode of energy. In one or more embodiments, the objective function is based on applying a nonlinear Radon transformation to waveform attributes computed by one or more waveform operators. In one or more embodiments, the objective function is based on one or more waveform operators that operate on the basis of a single waveform. A waveform operator may be applied to a single recorded waveform, to calculate an operator output value representing a suitability indicator of a given candidate travel time calculated for a given receiver and a mode of operation. given energy. The relevance indicator of a candidate travel time is also referred to in this document as the "travel time relevance indicator". [00105] The waveform operator is selected such that the path time relevance indicator or the operator output value is optimal for a candidate path time which, for a waveform given, is the best candidate path time according to a given criterion represented by the waveform operator itself. An operator output value for a candidate path time is optimal, for example, if the operator output value reaches a maximum, a minimum, or satisfies an optimality criterion for the candidate path time. [00106] The objective function defines how the relevance indicators of the journey times are digitally combined to generate the model relevance indicator. The model relevance indicator of a given slowness model is a numerical combination of the travel time relevance indicators of the calculated candidate travel times for the given slowness model. The numerical combination defined by the objective function may be based on a sum, a weighted sum, a multiplication, a weighted multiplication or any mathematical function that combines the indicators of relevance of travel time in such a way that the output of the objective function ( ie the model relevance indicator) increases when any of the relevance indicators of travel time increases. Examples of objective functions and waveform operators are described below. The waveform operator may be the "Short Term Average / Long Term Average" operator (hereinafter referred to as STALTA), the "Akaike Information Criterion" operator (hereinafter referred to as AlC), the 'Bayes Information Criterion' operator (hereinafter referred to as BIC) or higher order statistical operators. The operator STALTA can be defined on the basis of a positive function g (t) applicable to a waveform. For example, the function g (t) can extract the Hilbert envelope from the waveform or a squared waveform amplitude. In the equation below (eq1), t is a candidate path time for which the operator output value / the travel time relevance indicator is calculated, sw and Iw define a time window. around the candidate path time t, and ε is a small real number used for stabilization purposes of the division process: (eql) where U is a variable that represents time. The time stamp or the time value t for which the STALTA function reaches a maximum over a time zone of interest is often considered as the arrival time of the energy mode of interest. The STALTA function has a peak around this time value. The STALTA operator is widely used in the processing of global data of seismology, for the computation of the arrival times of the P wave and the S wave. According to the equation (eq1) above, the value of Operator output is calculated from a portion of the recorded acoustic waveform corresponding to a time window [t-1w, t + sw] defined with respect to the given candidate travel time t. [00109] The AlC operator is defined, for example, in the document entitled "A new look at the statistical model identification", by Akaike H., 1974, IEEE Transactions on Automatic Control, 19 (6), pp 716-723 . The AlC operator aims to detect changes in a recorded waveform and the output of the AlC operator visibly increases when a change is detected. The most apparent change observed in the output of the operator AlC is often associated with the arrival time of the first energy mode, for example the arrival of the P wave component. [00110] The Bayes Information Criterion is defined, for example, in the document entitled "Estimating the Dimension of a Model", by Schwarz G. E., 1978, Annals of Statistics 6. In one or more embodiments, the objective function is called the CSM (Combined Sonic Mapping) function and is defined by the equation (eq2) below: (eq2) where: - Kn is a scaling factor for the n-index receiver; by default, this scaling factor can be set to a value of 1; and Op is a waveform operator applicable to an input waveform (eg, AlC, BIC, STALTA. [00112] With regard to the above ratings, is the operator output for the pretreated waveform ^ and for the candidate path time is the path time relevance indicator, based on the waveform operator Op, the candidate path time TT Bj {T {d), R (dj ^ ')) corresponding to the receiver R (dn) at a position dn. [00113] Similarly, is the model relevance indicator, based on the Op waveform operator, of the Sm slow model for energy modes Ei to Ep. By choosing STALTA as a waveform operator, we obtain the expression CSMstalta as indicated by the equation (eq3): (eq3) [00115] Similarly, by choosing AlC as the waveform operator for CSM, we obtain the expression CSMaic as indicated by the equation (eq4): (eq4) [00116] In one or more embodiments, the objective function is called the GSTC (GSTC) function and is defined by the equation (eq5) below: (eq5) where: - Tw is a window length used to extract a portion of the waveforms. As it leaves the equation (éq6) below, the GSTC function would be equivalent to the STC operator under the additional conditions below: P is equal to one; - All receivers are spaced uniformly in the acoustic tool: - A refraction model is used in the ray tracing of the modeling before; - It is assumed that a constant slow model value exists for the portion of the borehole where the selected receivers are positioned. (eq6) [00118] With the objective functions defined in this document, it is no longer necessary to have uniform spacing of the receivers of a logging tool. The propagation mode of the waves is not limited either, because different propagation modes can be taken into account simultaneously. In addition, the objective function also operates with any type of slowness model, and therefore even in the absence of constant slowness in the portion of the borehole where the selected receivers are positioned. In addition, the different arrival times of different energy modes can be taken into account simultaneously in order to reduce the risk of calculating erroneous travel times. For example, taking into account the arrival of P waves, S waves and Stoneley waves reduces the risk of erroneous detection of P wave arrival. The objective function can also operate on an overall analysis of the P waves. waveforms recorded by the receivers, rather than on the basis of a single waveform. [00119] Referring again to FIG. 7B, in block 860, a model relevance indicator is calculated for each slowness model from the set of waveforms (whether or not these have been pretreated) recorded by a or more receivers of the logging tool. In block 870, a search for the optimal relevance indicator is performed based on the model relevance indicators calculated in block 860. In block 880, the set of candidate travel times corresponding to the slowness model for which the objective function is optimal (for example the slowness model for which the model relevance indicator is maximum) is obtained from from the search executed in block 870. The objective function is optimal, for example, when the output value of the objective function reaches a maximum, a minimum, or satisfies a given criterion of optimality for a set of candidate travel times corresponding to a slowness model. The candidate travel times corresponding to the slowness model for which the objective function is optimal are referred to herein as "corresponding travel times". At block 885, a time picking algorithm can be used to compare the corresponding path times with determined path times on the basis of a single waveform with an operator. of given waveform applied directly to a recorded waveform (without modeling before). Fig. Figure 8 illustrates the principles of time adjustment using the STALTA operator. [00123] FIG. 8 shows examples of input waveforms as well as the associated output curves of the STALTA operator. The output of the STALTA waveform operator may include peaks (maximum values) corresponding to the best candidate path times for P (PP) and S (SP) waves, respectively. A set of candidate arrival times obtained at block 650 following the modeling before is also represented, for the P waves (PC circles) as well as for the S waves (SC circles). A maximum of the objective function is obtained for the most relevant or optimal slowness model and the corresponding travel times, in this example, correspond to those produced by the STALTA operator. Fig. 8 illustrates the situation of non-matching travel times for illustrative purposes. By locally applying a time-adjustment refinement approach based on a waveform operator (STALTA, AlC or BIC) that operates on the basis of a single waveform, the results obtained at the block 880 can thus be evaluated. In some embodiments, timing techniques may be used to refine the times. The before modeling and the operations of the blocks 830 to 880 or 830 to 885 can be executed several times before the execution of the operations of the block 890. In block 890, one or more estimates of slowness are obtained for the underground formation based on the corresponding travel times. An estimate of slowness is one of the slowness values of the geologic cells of the most relevant slowness model, or a combination of these slowness values. The estimate of slowness can be calculated for one or more energy modes. A slowness map can be generated and displayed on a screen of a device. A slowness map represents indicators of relevance of slowness values calculated for a given energy mode at different depths with a given function. The given function can be an objective function described in this document or a waveform operator. [00127] Figs. 9 and 10 show examples of slowness maps resulting from the processing of waveforms acquired by an acoustic tool with a set of 13 receivers in two different situations. In order to be able to compare the output of different functions, the processing was performed under the following conditions: it is assumed that the slowness is constant over the area covered by the logging tool; the receivers are uniformly spaced such that the STC method can be used as a reference method; the travel time between the transmitter and the nearest receiver is measured. These two figures 9 and 10 consist of four panels. Panel 1, on the left, represents the waveforms recorded by the receivers, with the estimated arrival times associated with them. On this panel, a PL or SL line can be drawn between the arrival times of the P wave or S waves, respectively, and this line intersects the horizontal axis. This line illustrates the linear relationship that exists between the depth (or position of the receiver) and the corresponding travel time when the slowness is uniform in the borehole. The panel 2 represents a slowness map according to the output of the operator STC for the P waves. The panel 3 represents a slow map according to the output of CSMstalta for the P waves. The panel 4, on the right, shows a map of slowness according to the CSMaic output for P waves. The gray level of a point in these slowness maps is representative of the relevance of the slowness value to the corresponding depth / time value. In each of panels 2 to 4, a dark circle SV1, SV2, SV3 or SV4, SV5, SV6 respectively is shown; it corresponds to the highest relevance indicator corresponding to the value of slowness most relevant to the corresponding depth. [00129] FIG. 9 shows that CSMstalta and CSMaic estimate the slowness of adequate P waves (SV2, SV3). On the other hand, STC is subject to a cycle skip effect and produces the slow wave value S (SV1). [00130] FIG. 10 shows that the three methods tested produce comparable estimates of slow P waves (SV4, SV5, SV6). However, it is clear that the STC card is much more prone to false alarms (much more noise and much more irregular) than the other two cards, including the CSMstalta card or the CSMaic- [00131] card. the slowness has been described. The described algorithm can be used to process acoustic logging tool data regardless of the configuration of their receivers, i.e. acoustic receivers do not necessarily need to be evenly spaced. [00132] The acoustic slowness estimation method described herein can be used for unipolar data, bipolar data acquired with acoustic tools used in cable work, or with acoustic logging tools during drilling (LWD). The flexibility of the described method allows to apply it to acoustic tools regardless of the configuration of their receivers. Specifically, the receivers of the data acquisition tool do not need to be evenly spaced. Thanks to the non-linear transformation of Radon, the method of estimating the slowness described in this document combines the use of information criteria such as STALTA, AlC or BIC with a forward modeling based on the launch of radius, in order to obtain the best match between the model and the data observed following a joint fire collection. In addition, the method makes it possible to jointly detect several energy modes. The method produces travel times from several slowness models for the underground formation, thereby guaranteeing the coherence of the calculated travel times with a slowness model, and therefore with each other. This provides a more robust approach for estimating acoustic slowness, with fewer false detections of arrival time. The method can reduce user intervention on calculated travel times or delays. [00135] The slowness estimation method described in this document applies both to the processing of acoustic logging data in the field and to the post-processing of these data. Although the description presented above has relied on particular means, materials and embodiments, it is not limited to the particular means, materials and embodiments disclosed herein. As a further example, embodiments may be used in conjunction with a hand-held system (ie, a telephone, a computer attached to the wrist or forearm, a tablet, or any other hand-held device), a portable system (ie a laptop computer or a portable computer system), a stationary computer system (ie, a desktop computer system, a server , a group, or a high-performance computer system), or across a network (ie, a cloud computing system). As such, the embodiments extend to structures, methods, uses, program products, and functionally equivalent compositions that fall within the scope of the appended claims.
权利要求:
Claims (11) [1" id="c-fr-0001] A method for estimating acoustic slowness in a subterranean formation, comprising: obtaining (700) a plurality of acoustic waveforms received by a plurality of receivers after the transmission of a source acoustic wave by an emitter through the subterranean formation to obtain a plurality of recorded acoustic waveforms, the plurality of receivers being located at different positions in the subterranean formation, obtaining (710) at least two slow models of the subterranean formation, a a slowness model being defined by at least one constant slowness cell for at least one wave energy mode, a calculation (720), for each slowness model, of a set of candidate path times, each time candidate path of a set of candidate path times being calculated for a said wave energy mode and a position of a receiver of the plurality of receivers, a calculation (730) of an indicative relevance for each set of candidate travel times, based on recorded acoustic waveforms; searching (740) for a correspondence between the candidate travel time sets and the recorded acoustic waveforms by searching for an indicator of relevance that is optimal; and calculating (750) an estimate of the acoustic slowness for the subterranean formation from a set of candidate travel times for which the relevance indicator is optimal. [2" id="c-fr-0002] The method of claim 1, further comprising: calculating, for each candidate path time, at least one operator output value by applying at least one waveform operator to a waveform recorded acoustic signal received by the receiver at a position corresponding to the candidate path time, the operator output value being indicative of a relevance of the candidate path time, the relevance indicator of a set of candidate path times being calculated by numerically combining the calculated operator output values for each candidate path time of the candidate path time set. [3" id="c-fr-0003] The method of claim 2, further comprising: pretreating each recorded acoustic waveform to extract at least one wave component corresponding to a wave energy mode, calculating an output value an operator for each candidate path time corresponding to a given energy mode by applying the waveform operator to the extracted wave component corresponding to the given energy mode. [4" id="c-fr-0004] The method of claim 3, wherein a set of candidate path times comprises, for each position of a receiver in the subterranean formation, at least two candidate path times corresponding to two respective distinct wave energy modes. . [5" id="c-fr-0005] 5. The method according to claim 4, wherein the pretreatment of each recorded acoustic waveform is performed to extract, from each recorded acoustic waveform, at least two wave components respectively corresponding to at least two modes. of wave energy, and wherein the relevance indicator of a set of candidate path times is computed by digitally combining the operator output values corresponding to at least two wave energy modes. [6" id="c-fr-0006] The method according to any one of claims 2 to 5, wherein the operator output value is calculated from a portion of the recorded acoustic waveform corresponding to a time window defined with respect to the time of operation. given candidate path. [7" id="c-fr-0007] The method according to any one of claims 1 to 6, wherein a set of candidate path times comprises, for each wave energy mode, at least two candidate path times corresponding to at least two respective distinct positions. of a receiver in the underground formation. [8" id="c-fr-0008] The method of any one of claims 1 to 7, wherein the waveform operator is based on a criterion of the group consisting of AlC, BIC and STALTA criteria. [9" id="c-fr-0009] The method of any one of claims 1 to 8, wherein a wave energy mode represents an energy mode corresponding to a wave component of the group consisting of a compression wave component, a shear wave component, a Stoneley wave component, a Rayleigh wave component, and a slurry wave component. [10" id="c-fr-0010] A computer system comprising: one or more processors for processing data; a memory operatively coupled to said one or more processors, which includes program instructions for causing said one or more processors to execute a method of estimating acoustic slowness according to any one of claims 1 to 9. [11" id="c-fr-0011] A computer program product comprising computer-executable instructions which, when executed by a processor, cause said processor to execute a method for estimating acoustic slowness according to any one of claims 1 to 9. .
类似技术:
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同族专利:
公开号 | 公开日 US20190113639A1|2019-04-18| EP3433643A2|2019-01-30| US11112513B2|2021-09-07| WO2017165341A3|2018-08-23| FR3049355B1|2020-06-12| EP3433643A4|2020-01-22| WO2017165341A2|2017-09-28|
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申请号 | 申请日 | 专利标题 FR1652596A|FR3049355B1|2016-03-25|2016-03-25|METHOD AND DEVICE FOR ESTIMATING ACOUSTIC SLOWNESS IN A SUBTERRANEAN FORMATION| FR1652596|2016-03-25|FR1652596A| FR3049355B1|2016-03-25|2016-03-25|METHOD AND DEVICE FOR ESTIMATING ACOUSTIC SLOWNESS IN A SUBTERRANEAN FORMATION| US16/086,596| US11112513B2|2016-03-25|2017-03-21|Method and device for estimating sonic slowness in a subterranean formation| PCT/US2017/023296| WO2017165341A2|2016-03-25|2017-03-21|Method and device for estimating sonic slowness in a subterranean formation| EP17770937.5A| EP3433643A4|2016-03-25|2017-03-21|Method and device for estimating sonic slowness in a subterranean formation| 相关专利
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