• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    A method embodiment includes: obtaining a set of drilling parameters, possibly from a drill plane; applying the drilling parameter set to a physics-based model to obtain an estimated register of a downhole parameter such as temperature; and refinement of the estimated register with a data-driven model with a set of exogenous parameters. The temperature cycle and cumulative fatigue (or other measures of the probability of failure or the remaining life) can be obtained to predict tool failures, identify the main causes of poor drilling performance and determine the corrective actions.
    公开号:FR3027049A1
    申请号:FR1558622
    申请日:2015-09-15
    公开日:2016-04-15
    发明作者:Robello Samuel；Aniket；Serkan Dursun
    申请人:Landmark Graphics Corp；
    IPC主号:
    专利说明:

    [0001] PREDICTING HISTORICAL TEMPERATURE CYCLE-INDUCED DOWNHOLE TOOL FAULT Oil field operators require a large amount of information related to the parameters and conditions at the bottom of the well. Such information generally includes the characteristics of the earth formations traversed by the wellbore, and data concerning the size and configuration of the wellbore itself. Gathering of information on downhole conditions, commonly referred to as "logging," can be done in real time during drilling using "Logging While Drilling" ("LWD") tools which are integrated into the drill string. For various reasons, these tools are preferably positioned near the bit where the drilling operation makes the downhole environment particularly hostile to electronic instruments and sensor operations. Tool failures, whether partial or complete, are far too frequent. The interface of the platform's data acquisition and control systems communicates with LWD tools using one or more telemetry channels. The most frequently used telemetry channels support data rates that are greatly limited, forcing operators to choose between available sensor measurements. Often only the highest priority measurements are communicated in "real time" (in compressed form) and the others are sent from time to time or stored for later retrieval, which may be during breaks in the drilling process or may be delayed until the drill module is physically recovered from the wellbore. Often, many of these data are thrown out due to lack of bandwidth at the telemetry channel and lack of adequate space in the well-bottom memory. Thus, many parameters of the well-bottom environment, at any given time, are unknown or poorly monitored. The imminent detection of tool failure 3027049 - and the diagnosis of the root cause are problems that have not been adequately investigated, which means that tool failures at the bottom of the well are still unexpected and "Inexplicable". BRIEF DESCRIPTION OF THE FIGURES Thus, there is disclosed in the figures and in the following description, systems and methods for monitoring and predicting downhole tool failure events induced by the current temperature cycle. drilling. In the figures: FIG. 1 illustrates a logging environment while drilling (LWD).
    [0002] FIG. 2 is a flowchart of an illustrative LWD system. Fig. 3 is a graph showing an illustrative drilling position as a function of time. Fig. 4 is a graph showing an illustrative dependence of the temperature on the position.
    [0003] FIG. 5 is a graph comparing an estimated and measured dependence of the tool temperature as a function of time. Fig. 6 is an illustrative attribute table. Fig. 7 is a flowchart of an illustrative embodiment of a drilling method.
    [0004] Figs. 8a-8b are graphs showing the predicted temperature cycle and fatigue as a function of time. It should be understood, however, that the specific embodiments presented in the figures and the detailed description thereof do not limit the disclosure. Rather, they provide the foundation for one skilled in the art to discern alternative forms, equivalents, and modifications that are encompassed by one or more of the embodiments within the scope of the appended claims. DETAILED DESCRIPTION The disclosed methods and systems are better understood in the context of the larger systems in which they operate. Thus, FIG. 1 illustrates a logging environment while drilling (LWD). A drilling platform 102 supports a derrick 104 having a movable muffle 106 for raising and lowering the drill string 108. A top driven motor 110 supports and pivots the drill string 108 as it is lowered into a wellbore 112. The rotation of the wellbore 108 and / or a downhole motor 114 drives the drill bit 114. As the drill bit 116 rotates, it extends the wellbore 112 to through various underground formations. The downhole motor 114 may include a rotational orientation system (RSS) that allows the drilling team to orient the wellbore along a desired path. A pump 118 circulates drilling fluid through a feed pipe to the top drive 110, into the well through the interior of the drill string 108 through orifices in a drill bit 116. and returning to the surface through the ring around the drill string 108 and into the retention well 120. The drilling fluid carries cuttings into the retention well 120 and helps maintain the integrity of the wellbore .
    [0005] The drill bit 116 and the downhole motor 114 form only a portion of a downhole module (BHA) which includes one or more drill collars (i.e. thick-walled steel) to provide weight and rigidity to assist the drilling process. Some of these drill collars include integrated logging tools for harvesting various drilling parameters such as position, orientation, bit weight, rotational speed, torque, vibration, well diameter. drilling, temperature and pressure at the bottom of the well, etc. The orientation of the tool can be specified in terms of a tool face angle (rotational orientation), an angle of inclination (the slope), and the direction of the compass, each of which can be obtained measurements from magnetometers, inclinometers 3027049 and / or accelerometers, although other types of sensors, such as gyroscopes, may moreover be used. In a specific embodiment, the tool comprises a three-axis gap magnetometer and a three-axis accelerometer. As is known in the art, the combination of these two sensor systems makes it possible to measure the angle of the face of the tool, the angle of inclination and the direction of the compass. Such orientation measurements can be combined with gyro or inertia measurements to precisely track the tool position. One or more LWD tools 122 can also be integrated into the BHA for measuring the parameters of the formations that are drilled. When the drill bit 116 113 extends into the wellbore 112 through the subterranean formations, the LWD tools 122 turn and collect measurements such as resistivity parameters, density, porosity, acoustic wave velocity, radioactivity, gamma or neutron beam attenuation, magnetic resonance decomposition rates, and of course any physical parameters for which a measurement tool exists. A downhole control associates the measurements with time and the position and orientation of the tool to map out the time and space dependence of the measurements. Measurements can be stored in an internal memory and / or communicated to the surface, even if, as explained above, limits exist as to the speed with which such communications can occur. A telemetry submarine 124 may be included in the downhole module to maintain the communication link with the surface. Mud phase telemetry is a frequent telemetry technique for transferring measurements from the tool to a surface interface 126 and receiving commands from the surface interface, but other telemetry techniques may also be used. Typical telemetry data rates may vary from less than 1 bit per minute to several bits per second, generally well below the bandwidth required to communicate all raw measurement data to the surface in a reasonable amount of time. The surface interface 126 is also coupled to various sensors on and around the drilling platform to obtain measurements of drilling parameters 3027049 from the surface equipment. Examples of drilling parameters include riser pressure and temperature, annulus pressure and temperature, flow rate of drilling fluid to and from the well, density and / or heat capacity of the wellbore. drilling fluid, the hook load, the rotations per minute, the torque, the extended length of the drill string 108 and the rate of penetration. A processing unit, illustrated in FIG. 1 in the form of a computer tablet 128, communicates with the surface interface 126 through a wired or non-wired communication link 130 and provides a graphical user interface (GUI) 10 and other forms of interactive interfaces which allow a user to send commands and to receive (and possibly interact with) a visual representation of the acquired measurements. The measurements can be in the form of a register, eg a graph of the parameters measured as a function of time and / or the position along the wellbore.
    [0006] The processing unit may take alternate forms, including a desktop computer, a laptop, an integrated processor, a computing cloud, an internet-based central processing center, and combinations thereof. In addition, the drilling parameters at the top of the well and at the bottom of the well and the measured formation parameters, the surface interface 126 or the processing unit 128 can also be programmed with additional parameters relating to the drilling method. , which can be manually entered or retrieved from a configuration file. Such additional parameters may include, for example, technical data for drill string tubes, including material and wall thickness as well as lengths of the column; the type and configuration of the drill bit; the tools the LWD; and the configuration of the BHA. Additional information may also include a desired wellbore trajectory, an estimated geothermal gradient, typical break lengths for connection assembly, offset well registers, pressure limits, rate of flow limits, and any limitations on other drilling parameters.
    [0007] Thus, the term "parameter" as used herein is a generic term for various species of parameters: well-borehole parameters, downhole drilling parameters, formation parameters and additional parameters. Synonyms include "attributes" and "characteristics", and each parameter 5 has a value that can be defined (eg, tubular wall material) or that can be measured (eg, a rate), and in In both cases, it can be expected to vary or not, eg, depending on time and position. Fig. 2 is a function flow diagram of an illustrative LWD system. A downhole sensor set 202, preferably including, but not necessarily both, sensors of the drilling parameters and formation parameter sensors, sends signals to a sampling block 204. The block Sampling 204 digitizes the sensor signals for a downhole processor 206 which collects and stores the signal samples, either as raw data or as derived values obtained by the processor from the raw data.
    [0008] The derived values can, for example, include representations of the raw values, possibly in the form of statistics (e.g., means and variances), function coefficients (e.g., amplitude and velocity). an acoustic waveform), the parameters of interest (eg, weight on the bit rather than the voltage of the extensometer) or compressed representations of the data.
    [0009] A telemetry system 208 transmits at least some of the measured parameters to a treatment system 210 at the surface, the well top system 210 collecting, recording and processing the downhole parameters as well as a set of sensors 212 on the platform and around it. The processing system 210 may display the registered and processed registry settings on an interactive user interface 214. The processing system 210 may also accept user inputs and commands and operate in response to such entries, eg, control transmission and configuration information through the telemetry system 208 to the downhole processor 206. Such controls may change the operation of the downhole tool 3027049, e.g. , by adjusting the power to selected components to reduce power dissipation or to adjust fluid flows for cooling. Although the various parameters that operate on the well top treatment system represent different characteristics of drilling formation and operation, it must be recognized that they are not, strictly speaking, linearly independent. For example, the temperature measured by downhole tools may correlate with: the length of the drill string extended (depending on the geothermal gradient); with rotational speed, hook load and torque (friction dependent); and with the penetration rate and fluid flow rates (dependent on the heat transfer phenomenon). Additional correlations with other parameters, whether attributable to known or unknown causes, can be researched and exploited. Particularly, when associated with geothermal trends or more sophisticated engineering models to predict temperature dependence along the desired trajectory of the wellbore, the information that is expected to be expected is expected. such correlations can be derived from well-borehole parameters being sufficient for accurate and real-time monitoring of bottomhole temperature. Let's take Fig. 3, which is a graph showing an illustrative drilling position versus time. This parameter may be measured at the top of the well as an extended length of the drill string, but may also be or otherwise based on parameters measured by the navigation instruments incorporated in the BHA and transmitted to the upper well treatment system 126, 210. (Although not apparent on this scale, there are periodic breaks for the addition of new columns to extend the drill string). At any given depth, the temperature profile for fluids in the wellbore can be simulated or modeled analytically, based on physical principles. Fig. 4 shows an illustrative example of a temperature profile modeled analytically with the final position drill string in FIG. 3. Chart 7 3027049 402 illustrates the geothermal gradient of the formation, which comes from other sources and influences the temperature profile of the wellbore. Due to the moving fluid, however, the temperature profile of the wellbore deviates from this geothermal gradient. Charts 404 and 406 respectively illustrate the temperature profiles of the fluid in the drill string (elsewhere referred to as the temperature inside the pipe) and the fluid in the ring, according to the physics-based analysis model described. by Kumar and Samuel, "Analytical Model to Predict the Effect of Pipe Friction on Downhole Fluid Temperatures", SPE 165934, Drilling & Completion, Sept 2013. Based on the measured position (Fig. 3) and flow rate, temperature time-patterned BHA 10 is shown as a graph 502 in FIG. 5. By comparison, the temperature of the measured BHA is shown in Figure 504. Although some of the errors are due to quantification effects, most are attributable to other phenomena that are not included in the model and that should be correlate with other measured parameters, eg, rotational speed, torque, measured flow, ROP, each of which may represent pauses in drilling activity and excessive friction during drilling. Fig. 6 is a table of illustrative parameters that can be acquired as a function of time or position of the BHA, each line corresponding to a different sampling time or at a different position along the wellbore. (As indicated by the labels on the right of the figure, certain implementations may group multiple lines together to form sets that are associated with different position-based or time-based segments of the wellbore or drilling process in general). The columns of the table represent two sets of parameters, the first set is called "target attributes" and the second set is called "exogenous attributes". The target attributes are the parameters that are predicted by the physics-based model from the available set of measurements of the surface and sink parameter. In this case, the target attributes are the annular temperature (Ta) and the fluid temperature in the pipe (Tp) at position BHA. Exogenous attributes are the parameters, measured by surface sensors or recovered from well bottom sensors, which are available for use in conjunction with the physics-based model predictions. These may include some or all of the measurements used by the physics-based model to predict the target attributes, and may also include additional measures that are potentially correlated to the desired information and are available for study. In this particular example, the exogenous attributes include the penetration velocity (ROP), the revolutions per minute (RPM), and the weight on the bit (WOB). Hook load, riser pressure, and fluid flow are also specifically contemplated, as are the expected variables and registers of formation characteristics such as gamma radiation, sound velocity, and temperature. Based on the above observations and principles, FIG. 7 presents a flow diagram of a first illustrative logging method that could be implemented by the surface interface 126 or the wellhead processing unit 128, 210. In block 702, the system collects the parameters. and the available drilling characteristics of the drilling fluid. These parameters can be obtained from the sensors during a drilling operation in progress, or can also be obtained from the plans of a drilling operation. The drilling plan can be based on a volumetric model of the underground formations of interest, with a planned trajectory for the wellbore, an anticipated geothermal gradient, expected rock facies along the path, the configuration of the modulus of downhole (including bit type and dimensions), the nominal characteristics of the drilling fluid including flow rates, and the desired drilling rate, with downtimes and intervals.
    [0010] In block 704, the system uses the drill parameters collected in a physics-based model to provide an estimated register of the target parameter (s), such as the annular temperature and the temperature inside the pipe as a function of time. and depth. (Refer to Kumar and Samuel for details on an illustrative, physics-based model). In block 706, the system takes the estimated registers of the target parameters and increases the data with the exogenous parameter registers. Such parameters may, but not necessarily, include some or all of the parameters that operate on the physics-based model. Fig. 6 gives an example of the set of parameter registers thus obtained.
    [0011] It should be noted that the data collected in block 706 may, in some cases, include the actual measurements of the target parameters, e.g., if performed in real time during the drilling operation. Thus, the system can obtain downhole temperature measurements by telemetry from the downhole module. If such real measurements are available, then in an optional block 708, the system can reverse the estimated registers by subtracting the measured register from the target parameters. In block 710, the system exerts a data driven model to operate on the estimated registers of the target parameters and any register of exogenous parameters to produce a predicted target parameter register which is more sophisticated than the estimated registers. Such refinement may be possible because the data-driven model can account for the omissions and approximations employed by the physics-based model. The training performed in block 710 is based on a comparison of the predictions of the target parameter with the measurements of the target parameter. This comparison can be done segment by segment, the model being derived from measurements of a previous drill segment that are used to predict values of the target parameter in the next drill segment. Moreover, the comparison can be done dynamically to allow a faster adaptation of the model. In block 711, the system uses the data-driven model to make improved predictions of target parameters as a function of time or position along the wellbore trajectory. The system can extend predictions up to a forecast horizon, which can be expressed in the same way, in terms of time or position. The data-driven model used in blocks 710711 can be implemented in a variety of ways, the objective being, in each case, to automatically extract and use correlations or other forms of information that can be hidden in the parameter set. Among the appropriate modeling techniques that can be implemented by the system are regression-based or autoregressive prediction models such as 5 AR (self-regression only), ARX (self-regression exogenous), ARMA (self-regression motion) medium) and ARMAX (exogenous self-regression motion), and their nonlinear counterpart NAR, NARX, NARMA, and NARMAX; and regression based on prediction models such as support vector machines (MVS) and neural networks. Regardless of the implementation model, their prediction performance can be evaluated against the target parameter measurements based on the mean absolute error (EAM), the relative absolute error (EAR), the error percentage mean absolute error (PEMA), mean squared error (MSE), root mean squared error (RMSE), mean relative squared error (EQRM), directional accuracy (PDD) - a net count of the fact that predictions are above or below measures), the Akaike Information Criterion (CIA), or the Bayesian Information Criterion (CIB), possibly combined with a penalty based on complexity to prevent over-smoothing of the data. If the eventual overturning operation represented by block 708 is used, block 711 gives improvements to the estimated registers rather than the improved predictions themselves, and thus, in block 712 the system will combine its enhancements with estimated registers. to produce the predicted registers of the target parameters. Such a reversal may allow the data-driven model to better account for the inaccuracies of the physics-based model. In block 714, the system displays the expected target parameters for the future segments of the wellbore, up to a selected forecast horizon. In block 716, the system can compare previously generated forecasts with actual measurements of the target parameter registers and, if it is determined that the performance is inadequate, can initiate a new selection of the model implementation driven by the data and / or new training to improve the performance of the model. In addition to improving the accuracy of prediction, models driven by the data potentially reveal hidden relationships, allowing engineers to, eg, determine the impacts of specific exogenous parameters on the target parameter, possibly indicating causes. previously unrecognized tool failure.
    [0012] In block 718, the system obtains predictions on a tool event from the predicted registers of the target parameters. The invention specifically contemplates a temperature cycle derivation and cumulative stress fatigue, although other measures of remaining tool life or probability of failure would also be appropriate. Fig. 8a is a graph illustrating an illustrative temperature cycle register for a given downhole tool, which can be extended over a period of time that includes the history of the tool since the last maintenance. The graph shows two periods 802, 804 of active temperature cycle that can be predicted for a given tool in accordance with a drilling plan. Such a temperature cycle can be measured as a mean time derivative (absolute value of) of a predicted well bottom temperature register. Such a temperature cycle contributes to the cumulative stress fatigue 806 shown in FIG. 8b. As indicated, the cumulative fatigue generally changes in a nondecreasing manner, possibly reaching and exceeding a threshold 808. The threshold may represent a level indicating when the tool is to be repaired or replaced to minimize risks or associated costs. to a failure of the tool. On the other hand, exceeding such a threshold may instead be used as an indication of the likely main cause if poor drilling performance is observed, allowing corrective actions or mitigation measures to be taken up to date. that the main cause is fixed.
    [0013] In block 720 (Fig. 7), predicted tool events or estimated event probabilities can be displayed and accompanied by corrective actions or feasible recommendations. For example, if the stress fatigue provided by the temperature cycle exceeds a threshold, the system may recommend replacing or repairing a tool before drilling the next segment of the wellbore. On the other hand, if allowed by other drilling considerations, the system 3027049 may recommend tighter limits on the flow rate of the drilling fluid to reduce the temperature site. The process of FIG. 7 considers applying the model during the actual drilling process (ie, in "real time"). However, derived models based on the data obtained from one or more dug wells can be used during the planning process for drilling new boreholes in that area. In such cases, the predicted target parameters are based on drilling parameters that are, in themselves, estimates rather than measured values. Nevertheless, such predictions may be particularly useful for obtaining the availability of repair equipment and replacement tools in situations where the risk of tool failure suggests that such precautions are desirable. Among the embodiments disclosed herein, there are: A: A drilling method which comprises: obtaining a set of drilling parameters; Applying the drilling parameter set to a physics-based model to obtain an estimated register of a downhole parameter; and using a data-driven model to produce a predicted register of said well-bottom parameter based at least in part on said estimated register. B: A drilling system that includes: one or more downhole tools to be used with a drill string to extend a wellbore in accordance with a drilling plan; and a processing unit that obtains a temperature cycle prediction for each of the one or more downhole tools based at least in part on a drill plane. Each of these embodiments may include one or more of the following features in any combination. Feature 1: Comparing the predicted register to the measurements of a downhole parameter and updating, accordingly, the data driven model. Feature 2: The set of drill parameters is associated with a drill plan that is changed based on at least part of the predicted register. The modified drill plan may include at least one modified boundary on at least one drill parameter in said set. Feature 3: the downhole parameter includes a downhole temperature. Feature 4: The set of drill parameters includes at least bit weight, rotational speed, penetration rate, and rate of flow. Feature 5: The drill parameter set includes drilling fluid properties. Feature 6: The downhole parameter includes a temperature cycle of a downhole tool. Feature 7: Obtaining a forecast of a tool event from a predicted register. The prediction of a tool event may include the fact that cumulative stress fatigue exceeds a threshold and / or may include a probability of tool failure exceeding a threshold. Feature 8: The data driven model includes an autoregressive filter component. Feature 9: The data driven model includes an exogenous input filter component. Exogenous inputs may comprise at least one of the drilling parameters. Feature 10: The data driven model is based on regression. Feature 11: As part of obtaining one or more temperature cycle predictions, the processing unit applies a physics-based model to a set of parameters associated with the drill plan to obtain an estimated register of a downhole temperature, and work on the estimated register using a data driven model to produce a temperature cycle output. Feature 12: Based at least in part on the prediction of the temperature cycle for a given tool among one or more of the downhole tools, the processing unit recommends the repair or replacement of the given tool. Many modifications and other variations will be apparent to those skilled in the art once the above disclosure is well understood. It is intended that the following claims be interpreted to encompass all such variations and modifications where applicable. 14
    权利要求:
    Claims (5)
    [0001]
    REVENDICATIONS1. A drilling system comprising: one or more downhole tools for use with a drill string to extend a wellbore in accordance with a drilling plan; and a processing unit that obtains a temperature cycle forecast for each of the one or more downhole tools based at least in part on a drill plane.
    [0002]
    The system of claim 1, wherein in the context of obtaining one or more temperature cycle predictions, the processing unit is configured to apply a physics-based model to a set of parameters. associated with the drill plan to obtain an estimated register of a downhole temperature, and work on the estimated register using a data-driven model to produce a temperature cycle output.
    [0003]
    The system of claim 2, wherein the processing unit is configured to work on the estimated register using the data driven model that also works on the set of parameters associated with the drill plane.
    [0004]
    The system of claim 1, wherein based, at least in part, on the temperature cycle prediction for a given tool from one or more of the downhole tools, the processing unit is configured to recommend the repair or replacement of the given tool.
    [0005]
    The system of claim 1, wherein based, at least in part, on the temperature cycle prediction for a given tool from one or more of the downhole tools, the processing unit is configured to recommend the limitation or modification of at least one parameter associated with the drilling plan.
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    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US6968909B2|2002-03-06|2005-11-29|Schlumberger Technology Corporation|Realtime control of a drilling system using the output from combination of an earth model and a drilling process model|
    BRPI0706804A2|2006-01-31|2011-04-05|Landmark Graphics Corp|computer-readable methods, systems, and media for optimizing real-time production of oil and gas fields using a proxy simulator|
    US7766101B2|2007-06-25|2010-08-03|Schlumberger Technology Corporation|System and method for making drilling parameter and or formation evaluation measurements during casing drilling|
    US8928488B2|2008-12-03|2015-01-06|Halliburton Energy Services, Inc.|Signal propagation across gaps|
    EA201391266A1|2011-03-11|2014-02-28|Лэндмарк Графикс Корпорейшн|METHOD AND SYSTEM FOR DETERMINING THE PARAMETERS OF FORMATION|
    AU2012370482B2|2012-02-24|2016-06-30|Landmark Graphics Corporation|Determining optimal parameters for a downhole operation|AU2014396852B2|2014-06-09|2018-05-10|Landmark Graphics Corporation|Employing a target risk attribute predictor while drilling|
    US10983507B2|2016-05-09|2021-04-20|Strong Force Iot Portfolio 2016, Llc|Method for data collection and frequency analysis with self-organization functionality|
    US11237546B2|2016-06-15|2022-02-01|Strong Force loT Portfolio 2016, LLC|Method and system of modifying a data collection trajectory for vehicles|
    US20190064791A1|2016-05-09|2019-02-28|Strong Force Iot Portfolio 2016, Llc|Methods and systems for detection in an industrial internet of things data collection environment with intelligent management of data selection in high data volume data streams|
    CN110073301A|2017-08-02|2019-07-30|强力物联网投资组合2016有限公司|The detection method and system under data collection environment in industrial Internet of Things with large data sets|
    US10921801B2|2017-08-02|2021-02-16|Strong Force loT Portfolio 2016, LLC|Data collection systems and methods for updating sensed parameter groups based on pattern recognition|
    US11156998B2|2016-05-09|2021-10-26|Strong Force Iot Portfolio 2016, Llc|Methods and systems for process adjustments in an internet of things chemical production process|
    US20190323323A1|2016-12-07|2019-10-24|Landmark Graphics Corporation|Automated mutual improvement of oilfield models|
    FR3059705A1|2016-12-07|2018-06-08|Landmark Graphics Corporation|AUTOMATED MUTUAL IMPROVEMENT OF PETROLEUM FIELD MODELS|
    US11066917B2|2018-05-10|2021-07-20|Baker Hughes Holdings Llc|Earth-boring tool rate of penetration and wear prediction system and related methods|
    US20200232893A1|2019-01-23|2020-07-23|Halliburton Energy Services, Inc.|System and method to determine fatigue life of drilling components|
    法律状态:
    2016-07-25| PLFP| Fee payment|Year of fee payment: 2 |
    2017-07-26| PLFP| Fee payment|Year of fee payment: 3 |
    2018-07-18| PLFP| Fee payment|Year of fee payment: 4 |
    2019-09-26| PLFP| Fee payment|Year of fee payment: 5 |
    2021-09-03| RX| Complete rejection|Effective date: 20210726 |
    优先权:
    申请号 | 申请日 | 专利标题
    PCT/US2014/059681|WO2016057030A1|2014-10-08|2014-10-08|Predicting temperature-cycling-induced downhole tool failure|
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