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  • 专利说明
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    • 专利摘要:
    A method (200) for estimating casing wear (52) includes obtaining an input parameter set associated with extending a partially cased borehole (16) and applying from this input parameter set to a physics-based model to obtain an estimated casing wear log (52). The method (200) also includes using a data-based model to generate a log of predicted casing wear (52) based at least in part on the estimated casing wear log (52). . The method (200) also includes storing or displaying information based on the log of wear of the predicted casing (52).
    公开号:FR3027339A1
    申请号:FR1558637
    申请日:2015-09-15
    公开日:2016-04-22
    发明作者:Aniket；Robello Samuel；Serkan Dursun
    申请人:Landmark Graphics Corp；
    IPC主号:
    专利说明:

    [0001] ESTIMATING THE WEAR OF A TUBE FROM PHYSICAL MODELS AND INTEGRATED DATA BACKGROUND In hydrocarbon research and the development of hydrocarbon wells, oil field operators drill boreholes and carry out drilling operations. well completion. An example of well completion operations includes installing casing sections along a borehole, each casing section including multiple casing segments. The drilling team secures the 10 casing segments together to form the casing section when it has descended into the borehole to a desired position. Once the team gets the desired length and position for a given section of casing, it will cement it into place to create a permanent casing segment installation. The team can then extend the borehole by digging through the ring of the installed casing section. The method of installing the casing sections and the extension of a borehole can be repeated as desired. During drilling and / or well completion operations, rotation of the drill string results in frictional wear along the contact surfaces between the drill string and the casing. Over time, such wear reduces the thickness of the sidewall of the casing, degrading the strength and integrity of the casing. Failure of a casing segment due to wear can result in costly well repair operations and / or abandonment of a well. Several wireline logging techniques have been developed to measure casing wear. The available wireline logging techniques involve the lowering or raising of drilling tools along the interior of one or more installed casing sections. Examples of casing wear logging tools utilize acoustic, electromagnetic (EM) technology or multi-finger diarnetry technology. While it is possible to reduce casing failures by frequently and repeatedly deploying casing wear logging tools, such a procedure is not economically feasible since it increases the cost of the casing logging tools. and significantly delays drilling. Other economically undesirable options for reducing casing failure include the use of excessively thick casing segments or the use of high grade or high quality pipe materials.
    [0002] As an alternative to the use of too-conservatively thick casing, expensive high-grade casing materials, or the frequent measurement of casing thickness diameter, some operators rely on physics-based models to estimate the casing thickness. casing wear. Some models of this type often become too complex to set up and / or show great inaccuracies, despite repeated recalibration efforts. BRIEF DESCRIPTION OF THE DRAWINGS Accordingly, methods and systems for predicting casing wear using a physics-based model and a data-based model are disclosed herein. In the drawings: FIG. A is a schematic diagram of an illustrative drilling environment. FIG. 1B is a block diagram of an illustrative cable logging environment. FIG. This is a flowchart of an illustrative directional drilling system. FIG. 2 is a diagram of a casing illustrative of a tension scenario on the drill string showing areas of high wear. FIG. 3A is a sectional view showing a stress scenario for a drill string in casing. FIG. 3B is a sectional view showing a compression scenario for a drill string in a casing. FIG. 3C is a sectional view showing an illustrative casing bore logging tool in a borehole. FIG. 4 is a flowchart of a method of predicting casing wear during the planning phase of a wellbore. FIG. 5 is a flowchart of a method of predicting casing wear during a drilling phase of a wellbore. FIG. 6 is a graph showing an illustrative type of a predicted casing wear log. FIG. 7 is a graph showing another illustrative type of log of predicted casing wear. FIG. 8 is a system for predicting casing wear illustrative.
    [0003] It should also be understood, however, that the specific embodiments given in the figures and their detailed descriptions do not limit the disclosure. Rather, they provide the foundation for a person 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 rapid increase in the number of horizontal wells, long reach, and multi-wells that are currently being dug represents an additional challenge for casing failures caused by drill string-induced casing wear. Methods and systems for predicting casing wear using both a physics-based model and a data-based model are disclosed herein. As used here, a physics-based model describes a model that uses well-understood principles of physics, such as frictional forces, force balancing, energy conservation, and velocities. erosion to formulate a prediction of casing wear in the form of an analytic equation or a table of numerical solutions indexed by parameters. A physics-based model can, eg, calculate casing wear by integrating differential equations derived from Newton's laws and / or other laws describing the effect of a contact between a drill string and a drill string. tubing. Examples of parameters used in the physics-based models include a wear factor, a lateral force (or lateral force-related parameters), drilling parameters that affect the magnitude, location, and severity of the force. contact (eg, rotational speed, bit weight, drilling direction), and / or operating time.
    [0004] Physical-based model examples include, but are not limited to, the specific energy model, the linear wear efficiency model, the nonlinear casing wear pattern, the Hertzian model, the wear pattern by impact and energy model of the wellbore. The wellbore parameters used by these models may include, but are not limited to, the volume of wear, the internal diameter of the tubing, and the outer diameter of the joint. The specific energy model is the link between casing wear and the amount of energy required to dig a unit volume of material, which is an important parameter used to predict drilling performance and wear rates associated. The linear wear efficiency model is the link between casing wear and the amount of energy dissipated as friction in the wear process. The non-linear casing wear pattern uses wellbore parameters to estimate the extent of the casing wear groove. Once this model is executed, the resulting differential wear factor represents the slope (derivative) of the wear groove volume versus the work function curve. The wireless model involves the solution of two elastic bodies having curved surfaces in contact with each other, which is the case when a drill string is in contact with a casing wall. The impact wear model simulates the phenomenon consistent with downhole vibration and its contribution to the wear of the casing walls. Finally, the wellbore energy model represents a mathematical criterion for quantifying the quality of the borehole and incorporates the parameters of the borehole curvature and the wellbore torsion. The casing wear estimated by the wellbore energy model is an integral function of these two parameters. Thus, a combined model of "wear energy" is used to estimate casing wear in curved sections of the wellbore in which the drill string rests on its lower side. The basic assumption of this model is that the volume of wear of the casing wall is proportional to the work done by the friction on its inner wall by the joints only. As used herein, a data-driven model describes a model that correlates input data with a given output without considering the principles that govern their relationship. The correlations established for a data-driven model may be the result of statistics, adaptive calibration algorithms, and / or other data analysis techniques. In some embodiments, the data-based models combine adaptive and non-adaptive elements. In addition, data-based models can vary in complexity from those with only one or two layers of single-direction logic compared to models using complicated multi-input, multilayer, and multidirectional feedback loops. Where applicable, "weights" can be applied to model parameters, model outputs, or model feedback loops. The correlation learning process for data-based models can be based on measurements based on sensors and / or simulated data. The correlation learning rules may vary and in some embodiments are self-taught and / or dynamic. In at least some embodiments, the data-driven model is a regression-based model. In at least some embodiments, an exemplary method includes obtaining an input parameter set and applying this input parameter set to a physics-based model to obtain a log estimated casing wear. The method also includes using a data-based model to generate a log of predicted casing wear based at least in part on the estimated casing wear log from the physics-based model. The method also includes storing or displaying information based on the predicted casing wear log. Various input parameter options, data-based model calibration options, and options for using predicted casing wear are disclosed here. In at least some embodiments, the data-based model is calibrated based on estimates of casing wear by a physics-based model and actual measurements of casing wear. Once calibrated, the data-driven model is able to predict casing wear based on subsequent estimates of casing wear from a physics-based model. (Actual measurements of casing wear are no longer required, but can be entered to continue calibration of the data-based model). Along with the estimated casing wear from the physics-based model, other input parameters that can be used for calibration of a data-based model or for prediction of casing wear Using a data-driven model include wellbore parameters (eg, temperature, fluid viscosity, pressure), tubing and drill string parameters (flexibility, resistance to wear, diameter, thickness) and / or drilling parameters (the weight on the bit, the rotational speed, the torque). In at least some embodiments, such input parameters may correspond to sensor-based data collected in a well or in multiple wells. Prediction of casing wear from the data-based model may be data points or eroded volume log, channel depth, wall thickness, safety margin, and / or probability a failure of integrity as a function of the position along the casing. In some embodiments, the data-based model predicts casing wear during a wellbore planning phase (prior to commencement of a drilling project). For example, predicted casing wear may be stored or displayed for use by well planners prior to the commencement of a drilling project. With predicted casing wear, well planners can choose or update planned drilling parameters (eg, limits for bit weight, rotational velocity, penetration velocity, or strike direction). drilling), a planned wellbore path and / or a planned casing wall thickness. In other embodiments, predicted casing wear is stored or displayed for use by drilling operations during a wellbore drilling project. For example, drilling operators may use predicted casing wear to update drilling parameters or the trajectory of a borehole. In addition, the drilling operators may decide to stop the drilling, to achieve a diameter of the casing wear or to perform other tasks related to the completion of a drilling project while reducing the probability of drilling. casing failure. In at least some embodiments, the predicted casing wear can be compared to a predetermined threshold that indicates a probability of casing failure. If the predicted casing wear exceeds the predetermined threshold, a warning or other type of information (eg options to reduce the likelihood of a casing failure) may be displayed. FIG. 1A shows an illustrative drilling environment. A drilling platform 2 supports a derrick 4 having a movable muffle 6 for raising and lowering a drill string 8. A kelly rod train 10 supports the rest of the drill string 8 when it is lowered through 12. The turntable 12 rotates the drill string 8, thereby pivoting a drill bit 14. Additionally or alternatively, the rotation of the drill string 14 is controlled by a mud motor or other gear mechanism. rotation. When the drill bit 14 rotates, it digs a borehole 16 (represented by dashed lines) which passes through various formations 18. A pump 20 circulates drilling fluid through a feed pipe 22 to 25 kelly 10, towards the bottom of the hole inside the drill string 8, through holes in the drill bit 14, and back to the surface through the ring 9 around the drill string 8, and in A retention pond 24. The drilling fluid transports the cuttings from the borehole 16 into a retention basin 24 and helps maintain the integrity of the borehole 16. The drill bit 14 is just a piece of the borehole 16. well bottom assembly 25 which includes one or more drill collars 26 and logging tools 28. The drill collars 26 are thick walled steel pipe sections which add weight and rigidity to the drilling process. . The logging tool 28 (which can be mounted on one or more of the drill collars) records measurements of various drilling parameters or formation. Without limit, the logging tool 28 may be integrated into the bottom well assembly near the bit 14 to collect measurements. The collected measurements can be reported and used to orient the drill string 8, monitor drill performance and / or analyze the formation characteristics.
    [0005] The measurements from the logging tool 28 can be acquired by a telemetry submarine (eg, integrated with the logging tool 28) to be stored on an internal memory and / or communicated to the surface through a communication link. Sludge pulse transmission telemetry is a common technique constituting a communication link for transferring log measurements to a surface receiver for receiving commands from the surface, but other telemetry techniques can also be used. used. The telemetry signals are provided via a communication link 36 whether or not wired to a computer 38 or some other form of a data processing device. A computer 38 operates in accordance with software (which may be stored on an information storage medium 40) and user input through an input device 42 for processing and decoding the received signals. The telemetry data thus obtained can be further analyzed and processed by a computer 38 to generate a display of useful information on a computer monitor 44 or other form of display device, including a computer tablet. For example, an operator can use this system to obtain and monitor drill parameters or training characteristics. In at least some embodiments, the measurements collected by the logging tool 28 and / or other sensors (at the bottom of the well or at the surface) of the drilling environment of FIG. 1 A are used as input parameters in a physics-based model that estimates casing wear. As described herein, the estimation of casing measurements from a physics-based model can be provided to a data-based model that predicts casing wear. In some embodiments, such a physics-based model and a data-based model are used by a computer system such as a computer 38. The prediction of casing wear from the data-driven model (or related data such as a warning) may be displayed, eg, on a monitor 44. In addition, the computer system using the physics-based model and the data-based model may include a user interface for viewing , selection and adjustment of physics-based model options, data-based model options, learning options, warning options, and / or prediction validation options. The computer 38 or other computer may also allow a drilling operator to adjust drilling operations based on the prediction of casing wear from a data-driven model (or related data such as than a warning). In the drilling environment of FIG. 1A, some well completion operations, including the installation of a casing 52 representing at least one section of casing, were performed. The installation of each casing section involves mating the modular casing segments until a desired casing section length is obtained and / or lowering the casing section to a desired position in the hole Once a desired length and position for a given casing section is obtained, concreting operations are performed, creating a permanent casing section installation. If necessary, the borehole 16 is extended by digging through the cement at an installed end of the casing section. The method of installing the casing sections and extension of the borehole 16 may be repeated as desired. During drilling and / or well completion operations, drill string 8 is routinely removed from borehole 16, possibly reconfigured, and returned to borehole 16 to continue the drilling process. drilling.
    [0006] In the drilling environment of FIG. 1A, the casing wear occurs due to the contact between the drill string 8 and the casing 52. Such contact occurs, for example, during drill string operations, resulting in wear every time the drill string 8 pivots. The contact between the drill string 8 and the casing 52 is extended in the curved, sloping or horizontal portions of the casing 52. In addition, it should also be understood that the modification of the drill parameters and / or the rotation of the drill string Stems can change the speed of casing wear as well as the contact points. Over time, the contact between the drill string 8 and the casing 52 reduces the thickness of the sidewall of the casing 52, degrading the strength and integrity of the casing.
    [0007] FIG. 1B shows an illustrative cable logging environment that may represent the environment of FIG. 1A with the drill string 8 removed from the borehole 16 or other similar environment. In FIG. 1B, a first casing section 113 and a second casing section 140 were installed in a borehole 112. A cable 142 suspends a wireline logging tool 144 in the borehole 112 and couples the tool 144 to a unit or a logging vehicle 146, which may contain one or more computer systems. A pulley 148 (shown as part of a wired crane truck, but otherwise attached to a platform 102 with a rig 104) allows the wired logging tool 144 to be lowered and reassembled. along the borehole 112 at a controlled rate. The cable line 142 includes electrical and / or optical conductors for transporting the measurement data to the logging unit or vehicle 146 and possibly conveying electrical power to the tool 144. In some embodiments, The wired logging tool 144 may include blocks and / or other centralizing elements to maintain the tool centered in the borehole 112 during the logging operations. The wireline logging tool 144 may acquire different types of data related to the characteristics of the formation and the conditions at the bottom of the well. In accordance with at least some embodiments, the wired logging tool 144 corresponds to a casing wear logging tool that collects acoustic, electromagnetic (EM) measurements, or multi-parametric measurements that can, be analyzed for a log of casing wear as a function of the position along the first section of casing 113 or the second section of casing 140. The logging unit or vehicle 146 receives measurements collected by the casing wired logging tool 144 (e.g., through a wired link or not) and a related computer system stores, processes and / or displays the related measurements or information. In at least some embodiments, the casing wear measurements collected by the wireline logging tool 144 are used as input parameters for learning a data-based model, such as is described here. In addition, other measurements collected by the wired logging tool 144 and / or other sensors / tools can be used as input parameters for a physics-based model that estimates casing wear. Again, estimates of casing wear from a physics-based model can be provided to a data-based model that predicts casing wear. In some embodiments, such a physics-based model and a data-based model are used by a computer system associated with the logging unit or vehicle 146. Predicting casing wear from the model based on the data (or related data such as a warning) can be displayed, eg on a computer monitor. In addition, a computer system using the physics-based model and the data-based model may include a user interface for viewing, selecting. 0 and adjustment of physics-based model options, data-based model options, learning options, warning options, and / or prediction validation options. In at least some embodiments, a computer associated with the logging unit or vehicle 146 or other computer allows a drilling operator to adjust the drilling operations based on the prediction of casing wear. from a data-based model (or related data such as a warning). The adjusted drill parameters may apply after the well completion operations add another section of additional casing into the borehole 112 and / or after drill string operations place a drill string in the hole 112. In at least some embodiments, a log of casing wear estimated from a physics-based model and a log of measured casing wear associated with a first borehole segment. are used for the calibration of a data-driven model. Then, a log of estimated casing wear is obtained from a physics-based model. The calibrated data model uses the estimated casing wear log to predict casing wear for a second segment of boreholes. If applicable, at least one drilling component may be oriented according to the wear of the predicted casing. Such an orientation can be manual or automated. In addition, the magnitude of the adjustments to the drilling parameters may vary depending on the rate of wear or other calculations made using predicted casing wear. FIG. 1C is a flow diagram of an illustrative directional drilling system (e.g., as in FIG 1A), although the illustrated modules are also highly representative of a wired logging system (e.g., such as FIG. B). In FIG. 1C, one or more downhole tool commands 202 (e.g., processors) collects measurements from a downhole sensor set 204. Examples of sensors 204 include navigational sensors and sensors training parameters. The output of the sensors 204 is digitized and stored in an optional downhole processing for compressing the data, improving the signal-to-noise ratio and / or obtaining parameters of interest from the measurements. In at least some embodiments, the downhole sensors 204 measure casing wear using electromagnetic coils, acoustic sensors, and / or multi-finger logs.
    [0008] A telemetry system 208 sends at least some of the measurements or parameters deduced to a processing system 210 at the surface. Processing system 210 may also collect, record and process measurements from sensors 212 on and around a drilling platform (eg, platform 2 of FIG 1A) in addition to well bottom. The processing system 210 sends information for display on an interactive user interface 214. Examples of information that can be displayed include, for example, measurement logs, a drill hole trajectory, a trajectory casing, a log of predicted casing wear and drilling parameters recommended to reduce the risk of casing failure below a threshold. The processing system 210 may also accept user inputs and commands and control operations in response to such inputs to, eg, transmit commands and configuration information via telemetry system 208 to the control commands. Such controls may change the settings of an orientation mechanism 206 or other controllable drilling parameters. As illustrated in FIG. 2, a casing 52 may have multiple curves giving multiple contact regions 17A-17E between the drill string 8 and the casing 52 as the drill string 8 moves upward in a stress scenario. The extent of casing wear that occurs as a result of the contact between the drill string 8 and the casing 52 is affected by the number of curves along the casing 52, the angle of the curves along the casing 52, the flexibility of the casing material, the flexibility of the drill string material, the diameter of the casing and the diameter of the drill string. As a general rule, a casing path with a more "bent" path will have more casing wear. In at least some embodiments, the operator can reduce casing wear by modifying drilling parameters including, but not limited to, changing the well profile, changing the parameters to reduce the contact force, changing the parameters of the well. material properties of the drill string, bit or casing for a higher quality material, or the addition of drill string protection equipment (not shown). The drill pipe protection equipment includes a cover that wraps the circumference of the drill string in a plastic sleeve, and the outer surface of the sleeve does not rotate when the drill string is rotated, thereby reducing the contact force and the resulting casing wear. FIG. 3A illustrates a voltage scenario 50 as shown in FIG. 2 with more details for a drill string in a casing. In the tension scenario 50, the drill string 8 is removed or pulled in an upward direction 62 in connection with the borehole 16. The drill string 8, under tension, creates a normal force 54A which causes the contact a hinge 56 along the drill string 8 with an inner wall 60 of the casing 52, contributing to the wear of the casing. Importantly, the drill bit of the drill string 14 (shown in Fig. 1A) contributes most of the wear of the casing 52 during drilling operations. Over time, the resulting casing wear gruge casing material and may exceed a threshold 58 corresponding to the casing integrity threshold. Such casing wear can be continuous or occur in isolated areas along casing 52. In both cases, casing wear can eventually cause casing 52 to fail so that fluids can enter or escape. casing 52. FIG. 3B is a sectional view of a compression scenario 75. In the compression scenario 75, the drill string 8 is inserted or pushed in a downward direction 66 in relation to the borehole 16. The drill string 8 , under tension, creates a normal force 54B which causes the contact of a hinge 56 along the drill string 8 with an inner wall 64 of the casing 52, contributing to the wear of the casing. Over time, the resulting casing wear gruge casing material and may exceed a threshold 58 corresponding to the casing integrity threshold. Such casing wear can be continuous or occur in isolated areas along casing 52. In both cases, casing wear can eventually cause casing 52 to fail so that fluids can enter or escape. As the repair of a casing such as casing 52 is difficult, the disclosed methods and systems for predicting casing wear are used to minimize or prevent casing failure. In at least some embodiments, prediction of casing wear involves obtaining an estimate of casing wear of a physics-based model and applying wear estimation. casing as an entry into a data-driven model. The data-based model generates a log of predicted casing wear based at least in part on estimating casing wear.
    [0009] In accordance with at least various embodiments, the physics-based model that estimates casing wear may explain the different casing paths (see FIG 2). Such casing paths can be simulated by software and / or can be estimated from position / orientation data collected with measuring tools during drilling (MWD) or borehole logging (LWD). ) during a drilling process and / or with wireline logging tools. In addition, the physics-based model that estimates casing wear may explain different contact / force scenarios (see FIGs. 3A and 3B). For example, a lateral force (e.g., normal forces 54A or 54B in FIGS. 3A and 3B) can be estimated as a function of the bending stiffness and the various forces acting on a drill string at the same time. inside a casing such as viscous drag, torque, gravity, flotation, compression and vibration. Without limitation, a physics-based model may also explain other parameters including a wear factor, a rotational speed, and a measurement of the duration of drilling. The rotational speed and the drilling time can be measured by sensors at the surface or at the bottom of the well. In the meantime, the wear factor can be based on tubing materials and rod trains and / or can be inferred from laboratory tests. In various embodiments, the estimation of casing wear from a physics-based model can be expressed in various formats such as eroded volume, groove depth, casing wall thickness, margin of safety or a probability of a failure of integrity. The estimation of casing wear from a data-based model can similarly be expressed in various formats and may or may not have the same format as the estimated casing wear provided as input to the data-driven model. FIG. 3C illustrates a casing wear diameter tool 154 deployed along a casing 113 in a borehole 112 for measuring casing wear. In at least some embodiments, the casing wear diameter tool 154 may be deployed as a wired logging tool (see, eg, Fig. 113). In the case of the wireline logging embodiments, the power supply, telemetry and positioning of the casing wear diameter tool 154 may be supported by a cable line housing 142. By Moreover, some casing wear diameter tools 154 may correspond to logging tools while drilling (LWD). For embodiments of the LWD tool, the casing wear diameter tool 154 may be part of a bottom well assembly (BHA) (eg, BHA 25 as in FIG. 1A). The casing wear diameter tool 154 utilizes sensors 158 and possibly a signal source 156 for directly or indirectly measuring a tubing thickness 160 as a function of the position along the casing 113. the casing wear, the signal source 156 emits an acoustic signal and the sensors 158 receive corresponding acoustic reflections. The timing of the reflections with respect to the transmitted acoustic signals can be used to derive the position of the reflective surface (the casing wall). The position of the reflective surface varies with the extent of casing wear. For electromagnetic measurement of casing wear, the signal source 156 emits an electromagnetic signal and sensors 158 detect a corresponding magnetic field. Since the power of the magnetic field detected by the sensors 158 is affected by the thickness of the casing 113, the thickness of the casing can be deduced from the measurements of the magnetic field. With respect to the multi-finger compass measurement of casing wear, a signal source 156 is not required and the sensors 158 measure small movements or the tension of the compass (tip) fingers which are drawn on the along the surface of the casing 113. The thickness of the casing can then be deduced from the movement or the variation voltage of the compass fingers. In at least some embodiments, one or more measured casing wear logs from the casing wear diameter tool 154 are used to calibrate the data-based model. Although exceptions are possible, the availability of measured casing wear logs for calibrating a data-based model is generally limited because of cost. Once calibrated, the data-driven model is used to predict casing wear without the use of measured casing wear logs. Of course, if other logs of measured casing wear are available, the calibration of the data-based model can be updated accordingly. FIG. 4 presents an illustrative process 200 for predicting casing wear during a wellbore planning phase. the process 200 may be implemented by a computer 38 (FIG 1A) and / or another computer. At block 202, input parameters and a log of measured casing wear from an adjacent borehole are obtained. At block 204, the input attributes and the measured casing wear log are used to calibrate the data-based model. The output at block 204 is a model for predicting calibrated casing wear. At block 206, information regarding the parameters of the borehole design and the borehole profile is obtained. At block 208, the information on drilling design parameters and drill hole profile obtained at block 206 is applied to a physics-based model to obtain an estimated log of casing wear. The physics-based model can be one of several available models, eg, a specific energy model, a linear wear efficiency model, a nonlinear casing wear model, a model Hertzian, a model of impact wear or energy model of the wellbore. At block 210, the calibrated data model obtained at block 204 is used to generate a log of predicted casing wear based at least in part on the estimated casing wear log obtained at the level of block 204. of block 208. At block 212, the casing wear log is predicted and displayed or stored for use by a well planner before drilling begins. FIG. 5 shows an illustrative method 220 for predicting casing wear during a drilling phase of the wellbore. The method 220 may be implemented by a computer (FIG 1A) and / or another computer. The input parameters are obtained at block 222. The input parameters may correspond to drilling parameters, wellbore trajectory parameters, downhole condition parameters, casing attributes. , stem train attributes and / or other parameters used by physics-based models. At block 224, a physics-based model is applied to obtain an estimated casing wear log based on the input parameters obtained at block 222. Once again, the model based on physics can be one of several available models, eg, a specific energy model, a linear wear efficiency model, a nonlinear casing wear model, a Hertzian model, a model impact wear or energy model of the wellbore. The casing wear is measured at block 226. For example, a measured casing wear log can be obtained using a casing wear diameter tool 154 deployed as a tool. LWD or cable line tool. At block 228, the data-based prediction model is calibrated with the measured casing wear obtained at block 226 and the input parameters obtained at block 222. At block 230, the model Based on the data is applied to generate a log of predicted casing wear based on subsequent input parameters obtained during drilling. At block 232, the predicted casing wear log is stored or displayed for use by a well operator during drilling operations. The drilling operator may, for example, choose to adjust the controllable drilling parameters to reduce the likelihood of casing failure. In some embodiments, a drill control and / or or drilling components (for controlling bit weight, rotational speed, penetration rate, and / or drilling fluid parameters) can be automatically controlled. in accordance with the log of predicted casing wear or related output values from the data-based model. On the other hand, drill suggestions (eg, reduce rotation speed by 10%, reduce bit weight by 5%, change trajectory to reduce angl angle by 8%) can be presented to an operator in compliance with the predicted casing wear log. FIG. 6 is an illustrative map of a predicted tubing wear output log type from the data model of block 230. In FIG. 6, wear volume (in cubic inches) is reported as a function of measured depth. The wear volume shown in FIG. 6 can be an estimate of the existing casing wear, based on a set of input parameters collected, or a prediction of the amount of materials that, based on a proposed set of input parameters, will be removed from the internal wall of the casing due to the contact between the drill string and the casing. FIG. 7 is a similar illustrative map of another type of predicted casing wear as a function of depth, the casing wear being expressed in terms of the depth of the groove rather than the wear volume of the casing. Fig. 6. In 15 different newspapers, the depth of the groove may, moreover, be represented as a percentage of the thickness of the used wall, a percentage of the thickness of the remaining wall or a percentage of deteriorated wall integrity. In accordance with at least some embodiments, disclosed methods and systems related to predicted casing wear may be implemented in a digital electronic circuit, or in computer software, firmware or hardware, including the disclosed structures. in this specification and their structural equivalents, or in combinations of one or more of these. Computer software may include, for example, one or more instruction modules, encoded on a computer readable storage medium to be executed by, or to control the operation of, a data processing device. Examples of a computer-readable storage device include read-only memory (ROM) devices, random access memory (RAM) devices, optical devices (eg, CD or DVD), and read-only readers. floppy disks. The term "data processing device" encompasses all types of devices, devices and machines for data processing, including, as an example, a programmable processor, a computer, a system on a chip, or multiple , or combinations thereof. The devices may include a dedicated logic circuit, eg, a FPGA (programmable gate IC) or an ASIC (application specific integrated circuit). The devices may also include, in addition to the hardware, code that creates a runtime environment for the computer program in question, eg, a code that constitutes the processor firmware, a protocol stack, a management system for databases, a database management system, an operating system, a multiplatform execution time environment, a virtual machine, or a combination of one or more of these. The device and the runtime environment can realize different IT model infrastructures, such as web services, and distributed and grid computing infrastructures. A computer program (also called program, software, software application, script or code) may be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages. A computer program may, but not necessarily, correspond to a file in a file system. A program may be stored in a part of a file that contains other programs or data (eg, one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in several coordinated files (eg files that contain one or more modules, subroutines or portions of code). A computer program can be deployed to run on a computer or multiple computers that are located at one site or at multiple sites dispersed and interconnected by a communication network. Some of the processes and logic flows described in this specification may be performed by one or more programmable processors running one or more computer programs to generate actions by processing input data and generating an output. Processes and logical flows can also be performed by, and the device can also be implemented as, eg, an FPGA (programmable gate IC) or an ASIC (application specific integrated circuit). Suitable processors for running a computer program include, as an example, both microprocessors and versatile and specialized processors of any type of digital computer. In general, a processor will receive instructions and data from a RAM or ROM, or both. A computer includes a processor for executing actions in accordance with the instructions and one or more memory devices for storing instructions and data. A computer may also include, or may be operably linked to receive data from and to transfer data to, or both, to one or more mass storage devices for storing data, eg, a disk Magnetic, magneto-optical, or optical disk. However, a computer does not have to have these devices. Suitable devices for storing instructions and data from a computer program include all forms of memory, media and nonvolatile devices, including, for example, semiconductor memory devices (e.g., EPROM, EEPROM, Flash memory device, and others), magnetic discs (eg, internal hard disk, removable disk, and others), magneto-optical discs, and CD-ROMs and DVD-ROMs. The processor and the memory may be supplemented by, or incorporated into, a dedicated logic circuit. In order to interact with a user, the operations may be implemented on a computer having a display device (eg, a monitor, or other type of display device) to display information to the user. and a keyboard and pointing device (eg, mouse, trackball, tablet, touch screen, or other type of pointing device) through which the user can input data into the computer. 'computer. Other types of devices may also be used to interact with a user; eg, feedback from the user may be in the form of any sensory feedback, e.g., visual feedback, acoustic feedback, or tactile feedback, a user input may be received under any form, including hearing, voice or tactile input. In addition, a computer may interact with a user by sending documents to and receiving documents from a device that is used by the user; eg, sending web pages to a browser or client device of the user in response to requests from the web browser. An information system may comprise a single computing device, or multiple computers that operate near or generally at a distance from one another and usually communicate through a communication network. Examples of communication networks include a local area network ("LAN") and a wide area network ("WAN"), an inter-network (eg, the Internet), a network including a satellite link, and networks peer-to-peer (eg, ad-hoc peer-to-peer networks). A client and server relationship can be generated by computer programs that are run on the respective computers and that have a server client relationship to each other. FIG. 8 shows an illustrative system 300. The prediction system 100 may correspond to the computer system 38 mentioned in FIG. 1A and / or other computer system involved in obtaining input parameters, obtaining logs of casing wear, obtaining logs of estimated casing wear from physics-based models , calibration of a data-based model, use of a calibrated data-based model to predict casing wear and / or use of casing wear prediction from a data-based model for planning future wells, for adjusting drilling operations in real time or for performing other tasks described herein. The system 300 includes a processor 310, a memory 320, a storage device 330, and an input / output device 340. Each of the components 310, 320, 330, and 340 may be interconnected, e.g., with a bus system 350. The processor 310 may process instructions to be executed within the system 300. In some embodiments, the processor 310 is a wired processor, a multi-threaded processor, or other type of processor. The processor 310 may process information stored in the memory 320, or on the storage device 330. The memory 320 and the storage device 330 may store information within the computer system 300. The input / output device output 340 provides input / output operations for the system 300. In some embodiments, the input / output device 340 may include one or more network interface devices, e.g. an Ethernet card; a serial communication device; an RS-232 port and / or a wireless interface device, e.g., an 802.11 card, a 3G wireless modem or a 4G wireless modem. In some embodiments, the input / output device may include driver devices configured to receive input data and to send output data to other input / output devices, e.g. In some embodiments, mobile computing devices, mobile communication devices, and other devices may be used. The disclosed options for the prediction of casing wear should not be interpreted as limitations on the scope of what might be claimed, but rather as descriptions of the characteristics specific to the particular examples.
    [0010] Some features that are described in this specification, in the context of separate embodiments, may also be combined. Conversely, various features that are described in the context of a single embodiment may also be implemented in multiple embodiments or in any suitable combination. Many other modifications, equivalent and alternative, will be apparent to those skilled in the art once the aforementioned disclosure is fully understood. It is envisaged that the following claims will be interpreted to encompass all modifications, equivalents or alternatives of this type where applicable. Embodiments disclosed herein include A: A method of estimating casing wear which includes obtaining an input parameter set associated with the extension of a partially cased borehole, the application of from the parameter set to a physics-based model to obtain an estimated log of casing wear, the use of a data-driven model to generate a log of casing wear based at least on part on the log of estimated casing wear, and the storage or display of information based on the log of predicted casing wear. B: A casing wear estimation system which comprises at least one processor, a memory in communication with at least one processing and instruction storage which, when executed, allows at least one parameter to obtain a set of input parameters, the application of the input parameter set associated with the extension of a partially cased borehole to a physics-based model in order to obtain a log of the estimated casing wear, the use of a data-based model to generate a log of predicted casing wear based at least in part on said estimated casing wear log, and storage or display information based on the log of estimated casing wear. C: A drilling system that includes a processor configured to obtain a log of casing wear estimated from a physics-based model and a log of measured casing wear associated with a first well segment of the casing. drilling, calibrating a data-based model using the estimated casing wear log and the measured casing wear log, acquiring an estimated log of the next casing wear from a physics-based model, use the calibrated data-based model and the next estimated casing wear log to predict casing wear for a second segment of the wellbore, and at least one component of drilling in communication with the processor, wherein the processor controls an operation of the at least one drilling component according to the log of predicted casing wear. Each of Embodiments A, B and C may have one or more of the following additional elements, in any combination: Element 1: Also including model calibration based on casing wear measurement data associated with a Drill hole previously drilled. Element 2: Also includes the calibration of the model based on the casing wear measurement data associated with a wellbore that is being drilled. Element 3: 10 also comprising calibrating the model based on at least one parameter of the wellbore. Element 4: also comprising calibrating the model based on at least one drilling parameter. Element 5: The data-driven model is based on regression. Element 6: Also including comparing at least some of the predicted casing wear logs to a predetermined threshold and displaying a comparison based warning. Element 7: wherein said use of the data-based model is during wellbore planning and wherein the method also includes modifying a drilling plan based at least in part on the log of a wellbore the wear of the predicted casing. Element 8: wherein the modified drill plane comprises at least one modified limit based on at least one drill parameter selected from the list composed of bit weight, rotational speed, penetration rate and torque. . Element 9: wherein the modified drill plane comprises at least one modified parameter including the modification of the well profile, the modification of the drilling parameters in order to reduce the contact force, the modification of the properties of the material and the addition of the equipment protection of the drill string. Element 10: wherein said use of the data-based model is during the drilling phase of a wellbore and wherein the method also includes modifying a drilling parameter for a well being drilled based at least in part on the predicted casing wear log. Element II: in which the physics-based model corresponds to at least one specific energy model, a linear wear efficiency model, a non-linear casing wear model, a Hertzian model, a model of impact wear or energy model of the wellbore. Element 12: wherein the predicted casing wear log is a function of at least one casing wear value selected from a list consisting of an eroded volume, a groove depth, a wall thickness, safety margin or probability of integrity failure. Element 13: wherein the instructions direct the at least one processor to calibrate the model based on the casing wear measurement data associated with a previously drilled borehole. Element 14: wherein the instructions direct the at least one processor to calibrate the model based on the casing wear measurement data associated with a borehole being drilled. Element 15: In which the instructions also instruct the processor to calibrate the model based on at least one of a wellbore parameter or a drilling parameter. Element 16: wherein the instructions also instruct the at least one processor to compare at least a portion of the predicted casing wear log to a predetermined threshold and display a comparison based comparison. Element 17: wherein the instructions also instruct the at least one processor to modify a drilling plan for a future wellbore based at least in part on the predicted casing wear log. Element 18: wherein the instructions also instruct the at least one processor to modify a drill plan for a wellbore being drilled based at least in part on the predicted casing wear log. Many modifications and variations will be apparent to those skilled in the art once the aforementioned disclosure is fully understood. It is contemplated that the following claims be interpreted to encompass all such modifications and variations.
    权利要求:
    Claims (21)
    [0001]
    REVENDICATIONS1. A method of estimating casing wear (52) comprising: obtaining by a logging tool (28) and / or other sensors a set of input parameters associated with the extension of a casing (52) a borehole (16) partially cased .; applying the input parameter set to a physics-based model to obtain an estimated casing wear log (52); and using the data-based model to generate a log of predicted casing wear (52) based at least in part on the estimated casing wear log (52); and storing or displaying information based on the predicted casing wear log (52).
    [0002]
    The method of claim 1, further comprising calibrating the model based on the casing wear measurement data (52) associated with a previously drilled borehole (16).
    [0003]
    The method of claim 1, further comprising calibrating the model based on the casing wear measurement data (52) associated with a borehole (16) being drilled.
    [0004]
    The method of claim 1, further comprising calibrating the model based on at least one parameter of the wellbore.
    [0005]
    The method of claim 1, further comprising calibrating the model based on at least one drilling parameter.
    [0006]
    The method of claim 1, wherein the data-based model is based on regression. 25
    [0007]
    The method of any one of claims 1 to 6, further comprising comparing at least some of the casing wear logs (52) predicted to a predetermined threshold and displaying a warning based on the comparison.
    [0008]
    The method of any one of claims 1 to 6, wherein said use of the data-based model is during planning of a wellbore, and wherein the method further comprises modifying a drilling plan based at least in part on the predicted casing wear log (52).
    [0009]
    9. The method of claim 8, wherein the modified drill plane comprises at least one modified limit on at least one drilling parameter selected from the list of the composition of the weight on the bit, the rotational speed, the speed of penetration and of the couple.
    [0010]
    The method of claim 8, wherein the modified drill plane comprises at least one modified parameter comprising the modification of the well profile, the modification of the drilling parameters in order to reduce the contact force, the modification of the properties of the material. and the addition of drill string protection equipment.
    [0011]
    The method of any one of claims 1 to 6, wherein said use of the data-based model is during the drilling phase of a wellbore and wherein the method also includes modifying the a drilling parameter 10 for a well being drilled based at least in part on the predicted casing wear log (52), the modification of the drilling materials and the addition of drill string protection equipment .
    [0012]
    The method of any one of claims 1 to 6, wherein the physics-based model corresponds to at least one specific energy model, a model of linear wear efficiency, a model of the non-linear casing wear (52), a radio model, an impact wear model or a wellbore energy model.
    [0013]
    The method of any one of claims 1 to 6, wherein the predicted casing wear log (52) is a function of at least one casing wear value (52) selected from a a list composed of an eroded volume, a groove depth, a wall thickness, a safety margin or a probability of a integrity failure.
    [0014]
    A system for estimating casing wear (52) comprising: at least one processor; a memory in communication with at least one processor and storage instructions which, when executed, enable the processor to: obtain a set of input parameters associated with the extension of a borehole (16) partially cased; applying a set of input parameters to a physics-based model to obtain an estimated casing wear log (52); and using a data-based model to generate a log of predicted casing wear (52) based at least in part on the log of casing wear (estimated 52i) and to store or display information based on the predicted casing wear log (52).
    [0015]
    The system of claim 14, wherein the instructions also instruct the at least one processor to calibrate the data-based model of the casing wear measurements (52) associated with a borehole (16) previously drilled.
    [0016]
    The system of claim 14, wherein the instructions direct the at least one processor to calibrate the model based on the casing wear measurement data (52) associated with a borehole (16). during drilling.
    [0017]
    The system of claim 14, wherein the instructions also instruct the processor to calibrate the model based on at least one of a wellbore parameter and a drilling parameter. 10
    [0018]
    The system of any one of claims 14 to 17, wherein the instructions also instruct the at least one processor to compare at least a portion of the casing wear log (52) predicted to a predetermined threshold and to display a warning based on the comparison.
    [0019]
    19. The system of any one of claims 14 to 17, wherein the instructions also instruct the at least one processor to modify a drill plan for a future wellbore based at least in part on the logbook. casing wear (52) predicted.
    [0020]
    The system of any one of claims 14 to 17, wherein the instructions also instruct the at least one processor to modify a drilling parameter for a wellbore being drilled based at least in part on the casing wear log (52) predicted.
    [0021]
    A drilling system, comprising: a processor configured to obtain a log of casing wear (52) estimated from a physics-based model and a log of casing wear (52) measured 25 associated to a first borehole segment (16), calibrating a data-based model using the estimated casing wear log (52) and the measured casing wear log (52), for acquire a next estimated casing wear log (52) from a physics-based model, and use the calibrated data-based model and the estimated casing wear log (52) to predicting casing wear (52) for a second segment of the wellbore; and at least one drilling component in communication with the processor, wherein the processor controls an operation of the at least one drilling component based on the log of the predicted casing wear (52).
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    公开号 | 公开日
    GB2544696B|2020-09-23|
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    AU2014409112A1|2017-03-23|
    CA2961145C|2021-05-18|
    CA2961145A1|2016-04-21|
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    AR101975A1|2017-01-25|
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    AU2014409112B2|2019-09-26|
    NO20170350A1|2017-03-09|
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    法律状态:
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    2018-07-06| PLSC| Search report ready|Effective date: 20180706 |
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    优先权:
    申请号 | 申请日 | 专利标题
    PCT/US2014/061128|WO2016060684A1|2014-10-17|2014-10-17|Casing wear prediction using integrated physics-driven and data-driven models|
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