Fault trap capability evaluation method and device, equipment and storage medium

Abstract

The invention relates to the technical field of oil-gas exploration, and provides a fault trap capability evaluation method, device and equipment and a storage medium, and the method comprises the steps: building a quantitative relation model between a friction coefficient and the shale content based on the logging data of a to-be-evaluated extension stress fault region; the target shale content of any target infinitesimal point location is extracted, and the target friction coefficient of the target infinitesimal point location is calculated based on the quantitative relation model; calculating a first evaluation parameter of the mechanical stability of the target infinitesimal point location based on the target friction coefficient, and calculating a second evaluation parameter of the sealing capacity of the target infinitesimal point location according to the target shale content; and based on the first evaluation parameter and the second evaluation parameter, determining the ultimate loading capacity of the target infinitesimal point location. By constructing the quantitative relation model between the friction coefficient and the shale content, the quantitative relation between the physical property and the mechanical property of the fault is established, the ultimate pressure-bearing capacity of the extension stress fault is comprehensively evaluated, and the evaluation accuracy of the ultimate pressure-bearing capacity of the fault is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and in particular to a fault trapping capacity evaluation method, device, equipment and storage medium. Background Art

[0002] As oil and gas exploration extends into deeper, more complex reservoirs, and unconventional areas, the evaluation of the ultimate pressure bearing capacity of confined geological bodies, crucial for developing oil and gas resources, is crucial for ensuring the safety and efficiency of reservoir development. Within confined geological bodies, a dynamic assessment of the ultimate pressure bearing capacity of boundary faults is often required. However, current evaluation of fault trapping capacity is limited by insufficient evaluation factors, often employing only a single evaluation criterion to assess the ultimate pressure bearing capacity of the fault, resulting in evaluation results that are inconsistent with the actual ultimate pressure bearing capacity of the fault. Summary of the Invention

[0003] The present invention provides a fault trapping capacity evaluation method, device, equipment and storage medium, which are used to solve the defect of the prior art that a single standard is used for evaluation and the ultimate bearing capacity of the fault is not accurately evaluated.

[0004] The present invention provides a method for evaluating fault trapping capacity, comprising the following steps: Based on logging data of an extensional stress fault region to be evaluated, a quantitative relationship model between the friction coefficient and the shale content is constructed; the quantitative relationship model represents a functional relationship between the friction coefficient and the shale content; the extensional stress fault region includes a plurality of micro-element points, and the logging data is obtained by sampling and logging at the plurality of micro-element points; Extracting a target mud content of a target micro-element point, and calculating a target friction coefficient of the target micro-element point based on the quantitative relationship model; the target micro-element point is any one of the multiple micro-element points; Based on the target friction coefficient, a first evaluation parameter of the mechanical stability of the target micro-element point is calculated, and according to the target mud content, a second evaluation parameter of the sealing ability of the target micro-element point is calculated; Based on the first evaluation parameter and the second evaluation parameter, the ultimate pressure bearing capacity of the target micro-element point is determined.

[0005] According to the fault trapping capability evaluation method provided by the present invention, the first evaluation parameter of the mechanical stability of the target micro-element point is calculated based on the target friction coefficient, including: Acquiring stress data of the tensile stress fault region; the stress data includes normal stress and shear stress of the target micro-element point; Based on the Mohr-Coulomb failure criterion, a first evaluation parameter of the mechanical stability of the target micro-element point is calculated through the target friction coefficient, the normal stress and the shear stress.

[0006] According to the fault trapping capacity evaluation method provided by the present invention, the second evaluation parameter of the sealing capacity of the target micro-element point is calculated based on the target shale content, including: Calculating the cross-fault pressure difference of the target micro-element point according to the target mud content; Extracting the formation pressure of the target micro-element point from the target well logging data of the target micro-element point; the target well logging data is obtained by logging the target micro-element point; A second evaluation parameter of the sealing ability of the target micro-element point is calculated based on the cross-fault pressure difference and the formation pressure.

[0007] According to the fault trap capacity evaluation method provided by the present invention, determining the ultimate bearing capacity of the target micro-element point based on the first evaluation parameter and the second evaluation parameter includes: comparing the first evaluation parameter and the second evaluation parameter to determine a smaller value between the first evaluation parameter and the second evaluation parameter; The ultimate pressure bearing capacity of the target micro-element point is determined based on the smaller value.

[0008] According to the fault trapping capacity evaluation method provided by the present invention, the quantitative relationship model is: ; in, is the friction coefficient, represents the mud content in the logging data, 、 and are the model parameters to be fitted.

[0009] According to the fault trapping capacity evaluation method provided by the present invention, before constructing the quantitative relationship model between the friction coefficient and the shale content based on the well logging data of the extensional stress fault region to be evaluated, the method further includes: Divide the extensional stress fault area to be evaluated into micro-elements to obtain multiple fault micro-elements; Sampling is performed based on the plurality of fault elements to obtain sampling points; Well logging is performed on the sampling points to obtain well logging data.

[0010] According to the fault trapping capacity evaluation method provided by the present invention, the first evaluation parameter is: ; The second evaluation parameter is: ;in, represents the normal stress of the target micro-element point, represents the shear stress at the target micro-element point, is the target friction coefficient of the target micro-element point; represents the formation pressure at the target micro-element point, Indicates the cross-fault pressure difference at the target micro-element point.

[0011] The present invention also provides a fault trapping capacity evaluation device, comprising the following modules: A model building module is configured to build a quantitative relationship model between the friction coefficient and the shale content based on logging data of the extensional stress fault region to be evaluated; the quantitative relationship model represents a functional relationship between the friction coefficient and the shale content, the extensional stress fault region includes a plurality of micro-element points, and the logging data is obtained by sampling and logging the plurality of micro-element points; A model application module is used to extract a target shale content of a target micro-element point and calculate a target friction coefficient of the target micro-element point based on the quantitative relationship model; the target micro-element point is any one of the multiple micro-element points; a first evaluation module, configured to calculate a first evaluation parameter of the mechanical stability of the target micro-element point based on the target friction coefficient, and calculate a second evaluation parameter of the sealing ability of the target micro-element point based on the target mud content; The second evaluation module is used to determine the ultimate pressure bearing capacity of the target micro-element point based on the first evaluation parameter and the second evaluation parameter.

[0012] The present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above-described methods for evaluating the fault trapping capacity when executing the computer program.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for evaluating the fault trapping capacity as described above is implemented.

[0014] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned methods for evaluating the fault trapping capacity.

[0015] The fault trapping capacity evaluation method, device, equipment, and storage medium provided by the present invention construct a quantitative relationship model for the functional relationship between the friction coefficient and the shale content. Based on the shale content at any micro-point in the extensional stress fault region, the friction coefficient of the micro-point can be calculated based on the quantitative relationship model. A first evaluation parameter for the mechanical stability of the micro-point is calculated based on the friction coefficient, and a second evaluation parameter for the sealing capacity of the micro-point is calculated based on the shale content. The ultimate pressure bearing capacity of the extensional stress fault region at the micro-point is comprehensively evaluated based on the first and second evaluation parameters. By constructing a quantitative relationship model between the friction coefficient and the shale content, a quantitative relationship between formation physical properties and mechanical attributes is established, the stability of the extensional stress fault is evaluated, the fault sealing performance is quantified, and the ultimate pressure bearing capacity of the extensional stress fault is comprehensively evaluated, thereby improving the accuracy of the evaluation of the ultimate pressure bearing capacity of the fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 It is a flow chart of the fault trapping capacity evaluation method provided by the present invention.

[0018] Figure 2 It is a scatter plot of the corresponding relationship between the friction coefficient and the mud content provided by the present invention.

[0019] Figure 3 It is a schematic diagram of the evaluation process of the ultimate bearing capacity of a fault provided by the present invention.

[0020] Figure 4 It is a structural schematic diagram of the fault trapping capacity evaluation device provided by the present invention.

[0021] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0022] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0023] An embodiment of the present invention provides a fault trapping capacity evaluation method for evaluating the ultimate compressive bearing capacity of an extensional stress fault within a trapped geological body. By constructing a functional relationship between shale content and the fault friction coefficient, a quantitative relationship between formation physical properties and mechanical attributes is established, which is used as a basis for evaluating the mechanical stability and sealing properties of the fault. The ultimate compressive bearing capacity of an extensional fault is comprehensively evaluated based on mechanical stability and fault sealing properties. This method can significantly improve the accuracy of the evaluation of the ultimate compressive bearing capacity of faults within a trapped geological body, reduce the risk of oil and gas reservoir development, and provide support for the safe and efficient development of oil and gas trapped geological bodies.

[0024] Specifically, Figure 1 FIG. 1 is a flow chart of the fault trapping capacity evaluation method provided by the present invention, as shown in FIG. Figure 1 As shown, the method includes the following steps: Step 100: constructing a quantitative relationship model between the friction coefficient and the shale content based on logging data of the extensional stress fault region to be evaluated; the quantitative relationship model represents the functional relationship between the friction coefficient and the shale content, the extensional stress fault region includes a plurality of micro-element points, and the logging data is obtained by sampling and logging the plurality of micro-element points; Step 200: extracting a target shale content of a target micro-element point, and calculating a target friction coefficient of the target micro-element point based on the quantitative relationship model; the target micro-element point is any one of the multiple micro-element points; Step 300: Calculate a first evaluation parameter of the mechanical stability of the target micro-element point based on the target friction coefficient, and calculate a second evaluation parameter of the sealing ability of the target micro-element point based on the target shale content; Step 400: Determine the ultimate pressure bearing capacity of the target micro-element point based on the first evaluation parameter and the second evaluation parameter.

[0025] Based on the logging data of the extensional stress fault area to be evaluated, a quantitative relationship model between the friction coefficient and the shale content is constructed. This quantitative relationship model represents the functional relationship between the friction coefficient and the shale content in the stress extensional fault area. It can be obtained by fitting the friction coefficient and the shale content at the same point in the logging data.

[0026] The extensional stress fault region includes multiple micro-unit points, and well logging data is obtained by sampling and logging at these micro-unit points. By sampling and logging at these sampling points, the shale content and friction coefficient at the same point in the logging data are fitted to obtain a functional relationship between the friction coefficient and shale content, which serves as a quantitative relationship model between the friction coefficient and shale content in the extensional stress fault region.

[0027] Well logging or simulated logging can be used to obtain logging data for each micro-element point in the extensional stress fault region. The target shale content of the target micro-element point can then be extracted from the data. The target shale content of the target micro-element point can then be calculated based on a quantitative relationship model between the friction coefficient and the shale content. The target micro-element point is any one of the multiple micro-element points. The shale content of the target micro-element point can be obtained through well logging, and the friction coefficient of the target micro-element point is calculated based on the shale content and the quantitative relationship model.

[0028] It should be noted that the mud content can be obtained directly or indirectly through well logging, but the friction coefficient cannot usually be obtained directly through well logging. It needs to be indirectly estimated based on core experiment calibration or mineral composition analysis combined with well logging correlation analysis.

[0029] In view of the difficulty and complexity of obtaining the friction coefficient, in this embodiment, sampling logging is performed on the sampling points to estimate the friction coefficient, and then a quantitative relationship model between the friction coefficient and the mud content is constructed. Subsequently, for any other micro-element points, the friction coefficient can be quickly estimated by the mud content directly obtained by logging and the quantitative relationship model.

[0030] For a target micro-element point, after obtaining the target shale content and estimating the target friction coefficient at that point, a first evaluation parameter for the mechanical stability of the target micro-element point is calculated based on the target friction coefficient. A second evaluation parameter for the sealing capacity of the target micro-element point is calculated based on the obtained shale content. Finally, based on the first and second evaluation parameters, a comprehensive analysis of the trapping capacity of the target micro-element point is performed to determine the ultimate bearing capacity of the extensional stress fault zone at that target micro-element point.

[0031] In this way, the ultimate bearing capacity of each micro-point in the extensional stress fault region can be evaluated, and thus the ultimate bearing capacity of the extensional stress fault region can be evaluated based on the ultimate bearing capacity of each micro-point.

[0032] In this embodiment, by constructing a quantitative relationship model for the functional relationship between the friction coefficient and shale content, the friction coefficient of any micro-point in the extensional stress fault region can be calculated based on the shale content at that micro-point. A first evaluation parameter for the mechanical stability of the micro-point is calculated based on the friction coefficient, and a second evaluation parameter for the sealing capacity of the micro-point is calculated based on the shale content. The ultimate compressive capacity of the extensional stress fault region at the micro-point is comprehensively evaluated based on the first and second evaluation parameters. By constructing a quantitative relationship model between the friction coefficient and shale content, a quantitative relationship between formation physical properties and mechanical attributes is established, the stability of the extensional stress fault is evaluated, the fault sealing performance is quantified, and the ultimate compressive capacity of the extensional stress fault is comprehensively evaluated, thereby improving the accuracy of the evaluation of the ultimate compressive capacity of the fault.

[0033] In one embodiment, the extensional stress fault region is divided into multiple fault elements, and an evaluation point is determined from each element as the element point. A fault element is a tiny unit obtained by discretizing the extensional stress fault region. It is the basic unit used for closure analysis and can achieve spatially refined calculations of extensional stress faults.

[0034] Based on this, before step 100, the following steps may also be included: Step 001, dividing the tensile stress fault region to be evaluated into micro-elements to obtain a plurality of fault micro-elements; Step 002, sampling is performed based on the plurality of fault elements to obtain sampling points; Step 003: perform well logging on the sampling points to obtain logging data.

[0035] The extensional stress fault region to be evaluated is divided into micro-elements to obtain multiple fault micro-elements. Sampling is performed based on these multiple fault micro-elements, including determining the sampling micro-elements and the logging points within the sampling micro-elements to obtain the sampling points. Well logging is performed at the sampling points to obtain logging data. Based on this logging data, a quantitative relationship model between the friction coefficient and the shale content is constructed.

[0036] In one embodiment, the measuring points are calibrated with depth, and a scatter plot of the corresponding relationship between the logging curves of the mud content and the friction coefficient of the exploration wells passing through the fault or the area adjacent to the fault is established. The corresponding relationship between the mud content and the friction coefficient of the sampling points in the fault area is fitted to establish a functional relationship between the two.

[0037] The quantitative relationship model between the friction coefficient and the mud content is shown in the following formula 1: ; (1) in, is the friction coefficient, Indicates the mud content in the logging data, 、 and are the model parameters to be fitted.

[0038] For example, the depth of the measuring point is used to calibrate the measuring point, and a scatter plot of the corresponding relationship between the mud content and friction coefficient logging curves of the exploration wells in the X region and the Y well passing through the fault or the area adjacent to the fault is established, such as Figure 2 As shown in the figure, the corresponding relationship between the mud content and friction coefficient in the fault area is fitted, and the functional relationship between the two is established as follows: ; (2) In formula 2, we can get 、 and The values ​​of 、 and .

[0039] The first evaluation parameter of the mechanical stability of the target micro-element point is calculated based on the friction coefficient and stress data of the target micro-element point. Based on this, in step 300, the first evaluation parameter of the mechanical stability of the target micro-element point is calculated based on the friction coefficient of the target micro-element point, including: Step 301: Acquire stress data of the tensile stress fault region; the stress data includes normal stress and shear stress of the target micro-element point; Step 302 : Based on the Mohr-Coulomb failure criterion, a first evaluation parameter of the mechanical stability of the target micro-element point is calculated using the target friction coefficient, the normal stress, and the shear stress.

[0040] Stress data of the tensile stress fault area is obtained, and the stress data includes at least the normal stress and shear stress of the target micro-element point. Based on the Mohr-Coulomb failure criterion, the first evaluation parameter of the mechanical stability of the target micro-element point is calculated through the target friction coefficient, normal stress and shear stress of the target micro-element point.

[0041] The second evaluation parameter of the sealing ability of the target micro-element point is calculated based on the cross-fault pressure difference and the formation pressure. The cross-fault pressure difference is calculated based on the shale content. Based on this, in step 300, the second evaluation parameter of the sealing ability of the target micro-element point is calculated based on the shale content, including: Step 303, calculating the cross-fault pressure difference of the target micro-element point according to the target shale content; Step 304: extracting the formation pressure of the target micro-element point from the target well logging data of the target micro-element point; the target well logging data is obtained by logging the target micro-element point; Step 305: Calculate a second evaluation parameter of the sealing ability of the target micro-element point based on the cross-fault pressure difference and the formation pressure.

[0042] The cross-fault pressure difference at the target micro-element point is calculated based on the shale content, and the formation pressure at the target micro-element point is extracted from the well logging data at the target micro-element point. Then, based on the cross-fault pressure difference and the formation pressure at the target micro-element point, a second evaluation parameter of the sealing capacity at the target micro-element point is calculated. The target well logging data is obtained by logging at the target micro-element point.

[0043] The cross-fault pressure difference of the target micro-element point is calculated according to the mud content. Specifically, the mudstone layer is divided according to the mud content, the continuity of the fault mud smear is determined, and the fault mudstone ratio is calculated. Then, the cross-fault pressure difference is calculated based on the fault mudstone ratio.

[0044] In one embodiment, the first evaluation parameter can be expressed as: ; The second evaluation parameter can be expressed as: ,in, represents the normal stress at the fault element point, represents the shear stress at the fault element point, is the friction coefficient of the fault element point; Indicates the formation pressure at the fault micro-element point, Indicates the cross-fault pressure difference at the fault element point.

[0045] Reference Figure 3 The illustrated process for evaluating the ultimate bearing capacity of an extensional stress fault region involves sampling multiple fault elements in the extensional stress fault region, logging the sampled points, and obtaining logging data. Based on this logging data, a quantitative relationship model between the shale content and friction coefficient in the extensional stress fault region is constructed. Furthermore, physical property data and stress data are obtained for the extensional stress fault region. The physical property data can be logging data or data determined based on the logging data. The physical property data includes the shale content and formation pressure at each element of the fault element. Based on this physical property data, the trans-fault differential pressure of the fault element is determined. The friction coefficient at the element is calculated using the quantitative relationship model between friction coefficient and shale content. Based on the friction coefficient and stress data at the element, a first evaluation parameter for the mechanical stability of the element is calculated. Based on the trans-fault differential pressure and formation pressure, a second evaluation parameter for the sealing capacity of the element is calculated. Based on the first and second evaluation parameters, a comprehensive evaluation of the ultimate bearing capacity of the element is performed to determine the ultimate bearing capacity of the element.

[0046] In one embodiment, the first evaluation parameter corresponds to the first ultimate pressure bearing capacity value of the micro-element point, and the second evaluation parameter corresponds to the second ultimate pressure bearing capacity value of the micro-element point. The comprehensive evaluation of the micro-element point is to select the smaller value from the first evaluation parameter and the second evaluation parameter, and use the smaller value as the ultimate pressure bearing capacity of the micro-element point, thereby achieving a comprehensive evaluation of the ultimate pressure bearing capacity of the micro-element point. Based on this, step 400 includes: Step 401: compare the first evaluation parameter and the second evaluation parameter to determine the smaller value of the first evaluation parameter and the second evaluation parameter; Step 402: Determine the ultimate pressure bearing capacity of the target micro-element point based on the smaller value.

[0047] The first evaluation parameter and the second evaluation parameter are compared to determine the smaller value between the first evaluation parameter and the second evaluation parameter, and the ultimate pressure bearing capacity of the target micro-element point is determined based on the smaller value, wherein the smaller value between the first evaluation parameter and the second evaluation parameter characterizes the ultimate pressure bearing capacity of the target micro-element point.

[0048] In one embodiment, the quantitative relationship model between the friction coefficient and the mud content of the well Y in the X region established by calibrating the measurement points with depth is used as a basis to extract the mud content data of each fault micro-element point. , and based on the functional relationship represented by the established quantitative relationship model, calculate the friction coefficient of the corresponding fault micro-element point Based on the friction coefficient of the fault micro-element point and the Mohr-Coulomb failure criterion, the first evaluation parameter of the fault's ultimate bearing capacity based on the fault mechanical stability is calculated. : ; (3) In formula 3, Fault micro-element point The first evaluation parameter of the ultimate bearing capacity; Fault micro-element point The normal stress, Fault micro-element point The shear stress, The first of the multiple fault elements representing the extensional stress fault region Rank A micro-point.

[0049] Furthermore, based on the positive correlation between the fault sealing capacity and the mud content of the fault zone, the fault sealing capacity of the micro-element point is quantified by the cross-fault pressure difference AFPD, and the second evaluation parameter of the fault ultimate bearing capacity is calculated in combination with the actual formation pressure. : ; (4) In formula 4, Fault micro-element point The second evaluation parameter of the ultimate bearing capacity, Fault micro-element point The formation pressure, Fault micro-element point The cross-fault pressure difference.

[0050] Among them, the fault pressure difference AFPD can be used to measure the fault mudstone ratio. The calculation formula is as follows: ; (5) The fault mudstone ratio is calculated by dividing the fault mudstone thickness by the overall fault thickness. It is a dimensionless parameter and is obtained as a constant through empirical methods or calibration. For example, the value is 0.25~0.5 depending on the burial depth.

[0051] For any micro-point, compare the first evaluation parameter and the second evaluation parameter of the ultimate pressure bearing capacity of the micro-point, and select the smaller value of the two as the ultimate pressure bearing capacity of the micro-point: . (6) For example, two micro-element points D1 and D2 of the F1 fault in region X are selected, and the mud content data of D1 and D2 are extracted. Based on the functional relationship between the friction coefficient and the mud content shown in Formula 2, the friction coefficient of the corresponding points is calculated, as shown in Table 1. Table 1:

[0052] Based on the calculation results of the friction coefficient at points D1 and D2, combined with the stress data at the corresponding positions, the stress parameters of the cross section are calculated. The stress parameters include vertical principal stress, horizontal maximum principal stress, horizontal minimum principal stress, normal stress and shear stress. Then, the first evaluation parameter of the ultimate bearing capacity based on fault mechanical stability is calculated based on the friction coefficient and stress parameters. (Unit: MPa), the calculation results of stress parameters and the first evaluation parameters are shown in Table 2: Table 2:

[0053] Based on the positive correlation between fault sealing capacity and mud content, the cross-fault pressure difference at points D1 and D2 is calculated. Combined with the formation pressure at the corresponding positions, the second evaluation parameter of the ultimate bearing capacity of points D1 and D2 is calculated, as shown in Table 3: Table 3:

[0054] Comparing the first and second evaluation parameters of the ultimate bearing capacity at points D1 and D2 yields the ultimate bearing capacity of the fault at each location. As shown in Formulas 7-8 below, the ultimate bearing capacity of the fault at point D1 on the F1 fault is 11.44 MPa, the second evaluation parameter, and the ultimate bearing capacity of the fault at point D2 on the F1 fault is 19.31 MPa, the first evaluation parameter.

[0055] ; (7) . (8) The above method can be used to calculate the ultimate bearing capacity of each micro-element point in the extensional stress fault region. This capacity is then used to evaluate the ultimate bearing capacity of the extensional stress fault region based on the ultimate bearing capacity of each fault micro-element. Optionally, the minimum value of the ultimate bearing capacity of each micro-element point is used as the ultimate bearing capacity of the extensional stress fault region to ensure the safety of oil and gas development.

[0056] In this example, to address the overestimation of the ultimate compressive bearing capacity of a fault due to a single evaluation criterion, a comprehensive evaluation method for the ultimate compressive bearing capacity of a fault is proposed. Using well logging data on friction coefficient and shale content, a functional relationship between shale content and friction coefficient is established. Combined with stress field data and the Mohr-Coulomb failure criterion, the fault mechanical stability parameter (the first evaluation parameter) is calculated. The cross-fault pressure differential is then calculated based on fault sealing theory, and the second evaluation parameter for sealing capacity is calculated in combination with formation pressure. Ultimately, the smaller of the first and second evaluation parameters is selected as the ultimate compressive bearing capacity of the fault microelement. By establishing a quantitative relationship model between friction coefficient and shale content, the method overcomes the data isolation and one-sided evaluation limitations of traditional evaluation methods, significantly improving the accuracy and reliability of evaluation results and reducing the risk of oil and gas reservoir development. The method is applicable to a variety of complex geological environments, providing support for the efficient development of oil and gas resources.

[0057] Furthermore, for the extensional stress fault area, the minimum value of the ultimate bearing capacity in each micro-element point is selected as the ultimate bearing capacity of the extensional stress fault area. Based on the micro-element point, a high-precision evaluation of the extensional stress fault area is achieved, providing a more fine-grained evaluation result for the development of oil and gas resources and improving the accuracy of the evaluation results.

[0058] The fault trap capacity evaluation device provided by the present invention is described below. The fault trap capacity evaluation device described below and the fault trap capacity evaluation method described above can be referenced to each other.

[0059] Reference Figure 4 The embodiment of the present invention provides a fault trapping capacity evaluation device, comprising: A model building module 10 is configured to build a quantitative relationship model between the friction coefficient and the shale content based on the well logging data of the extensional stress fault region to be evaluated; the quantitative relationship model represents the functional relationship between the friction coefficient and the shale content, the extensional stress fault region includes a plurality of micro-element points, and the well logging data is obtained by sampling and logging the plurality of micro-element points; The model application module 20 is used to extract the target mud content of the target micro-element point and calculate the target friction coefficient of the target micro-element point based on the quantitative relationship model; the target micro-element point is any one of the multiple micro-element points; A first evaluation module 30 is configured to calculate a first evaluation parameter of the mechanical stability of the target micro-element point based on the target friction coefficient, and calculate a second evaluation parameter of the sealing ability of the target micro-element point based on the target mud content; The second evaluation module 40 is configured to determine the ultimate pressure bearing capacity of the target micro-element point based on the first evaluation parameter and the second evaluation parameter.

[0060] In one embodiment, the first evaluation module 30 is further configured to: Acquiring stress data of the tensile stress fault region; the stress data includes normal stress and shear stress of the target micro-element point; Based on the Mohr-Coulomb failure criterion, a first evaluation parameter of the mechanical stability of the target micro-element point is calculated through the target friction coefficient, the normal stress and the shear stress.

[0061] In one embodiment, the first evaluation module 30 is further configured to: Calculating the cross-fault pressure difference of the target micro-element point according to the target mud content; Extracting the formation pressure of the target micro-element point from the target well logging data of the target micro-element point; the target well logging data is obtained by logging the target micro-element point; A second evaluation parameter of the sealing ability of the target micro-element point is calculated based on the cross-fault pressure difference and the formation pressure.

[0062] In one embodiment, the second evaluation module 40 is further configured to: comparing the first evaluation parameter and the second evaluation parameter to determine a smaller value between the first evaluation parameter and the second evaluation parameter; The ultimate pressure bearing capacity of the target micro-element point is determined based on the smaller value.

[0063] In one embodiment, the quantitative relationship model is: ; in, is the friction coefficient, represents the mud content in the logging data, 、 and are the model parameters to be fitted.

[0064] In one embodiment, the fault trapping capability evaluation device further includes a microelement division and well logging module, which is used to: Divide the extensional stress fault area to be evaluated into micro-elements to obtain multiple fault micro-elements; Sampling is performed based on the plurality of fault elements to obtain sampling points; Well logging is performed on the sampling points to obtain well logging data.

[0065] In one embodiment, the first evaluation parameter is: ; The second evaluation parameter is: ;in, represents the normal stress of the target micro-element point, represents the shear stress at the target micro-element point, is the target friction coefficient of the target micro-element point; represents the formation pressure at the target micro-element point, Indicates the cross-fault pressure difference at the target micro-element point.

[0066] Figure 5 An example of a physical structure diagram of an electronic device is shown below. Figure 5 As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530, and a communication bus 540. The processor 510, the communications interface 520, and the memory 530 communicate with each other via the communication bus 540. The processor 510 may call logic instructions in the memory 530 to execute the steps of the fault trap capability evaluation method, for example, including: Based on logging data of an extensional stress fault region to be evaluated, a quantitative relationship model between the friction coefficient and the shale content is constructed; the quantitative relationship model represents a functional relationship between the friction coefficient and the shale content; the extensional stress fault region includes a plurality of micro-element points, and the logging data is obtained by sampling and logging at the plurality of micro-element points; Extracting a target mud content of a target micro-element point, and calculating a target friction coefficient of the target micro-element point based on the quantitative relationship model; the target micro-element point is any one of the multiple micro-element points; Based on the target friction coefficient, a first evaluation parameter of the mechanical stability of the target micro-element point is calculated, and according to the target mud content, a second evaluation parameter of the sealing ability of the target micro-element point is calculated; Based on the first evaluation parameter and the second evaluation parameter, the ultimate pressure bearing capacity of the target micro-element point is determined.

[0067] Furthermore, the logic instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0068] In another aspect, the present invention further provides a computer program product, comprising a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the steps of the fault trap capability evaluation method provided by the above methods, for example, including: Based on logging data of an extensional stress fault region to be evaluated, a quantitative relationship model between the friction coefficient and the shale content is constructed; the quantitative relationship model represents a functional relationship between the friction coefficient and the shale content; the extensional stress fault region includes a plurality of micro-element points, and the logging data is obtained by sampling and logging at the plurality of micro-element points; Extracting a target mud content of a target micro-element point, and calculating a target friction coefficient of the target micro-element point based on the quantitative relationship model; the target micro-element point is any one of the multiple micro-element points; Based on the target friction coefficient, a first evaluation parameter of the mechanical stability of the target micro-element point is calculated, and according to the target mud content, a second evaluation parameter of the sealing ability of the target micro-element point is calculated; Based on the first evaluation parameter and the second evaluation parameter, the ultimate pressure bearing capacity of the target micro-element point is determined.

[0069] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the fault trap capacity evaluation method provided by the above methods are implemented, for example, including: Based on logging data of an extensional stress fault region to be evaluated, a quantitative relationship model between the friction coefficient and the shale content is constructed; the quantitative relationship model represents a functional relationship between the friction coefficient and the shale content; the extensional stress fault region includes a plurality of micro-element points, and the logging data is obtained by sampling and logging at the plurality of micro-element points; Extracting a target mud content of a target micro-element point, and calculating a target friction coefficient of the target micro-element point based on the quantitative relationship model; the target micro-element point is any one of the multiple micro-element points; Based on the target friction coefficient, a first evaluation parameter of the mechanical stability of the target micro-element point is calculated, and according to the target mud content, a second evaluation parameter of the sealing ability of the target micro-element point is calculated; Based on the first evaluation parameter and the second evaluation parameter, the ultimate pressure bearing capacity of the target micro-element point is determined.

[0070] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0071] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for evaluating fault trapping capacity, characterized in that: include: Based on the well logging data of the extensional stress fault area to be evaluated, a quantitative relationship model between the friction coefficient and the shale content is constructed; The quantitative relationship model represents the functional relationship between the friction coefficient and the shale content, the extensional stress fault region includes a plurality of micro-element points, and the well logging data is obtained by sampling and logging the plurality of micro-element points; Extracting the target mud content of the target micro-element point, and calculating the target friction coefficient of the target micro-element point based on the quantitative relationship model; The target micro-element point is any one of the multiple micro-element points; Based on the target friction coefficient, a first evaluation parameter of the mechanical stability of the target micro-element point is calculated, and according to the target mud content, a second evaluation parameter of the sealing ability of the target micro-element point is calculated; Based on the first evaluation parameter and the second evaluation parameter, the ultimate pressure bearing capacity of the target micro-element point is determined.

2. The fault trapping capacity evaluation method according to claim 1, characterized in that: The first evaluation parameter of the mechanical stability of the target micro-element point is calculated based on the target friction coefficient, including: Acquiring stress data of the tensile stress fault region; the stress data includes normal stress and shear stress of the target micro-element point; Based on the Mohr-Coulomb failure criterion, a first evaluation parameter of the mechanical stability of the target micro-element point is calculated through the target friction coefficient, the normal stress and the shear stress.

3. The fault trapping capacity evaluation method according to claim 1, characterized in that: The second evaluation parameter of the sealing ability of the target micro-element point is calculated according to the target mud content, including: Calculating the cross-fault pressure difference of the target micro-element point according to the target mud content; Extracting the formation pressure of the target micro-element point from the target well logging data of the target micro-element point; the target well logging data is obtained by logging the target micro-element point; A second evaluation parameter of the sealing ability of the target micro-element point is calculated based on the cross-fault pressure difference and the formation pressure.

4. The fault trapping capacity evaluation method according to claim 1, characterized in that: The determining of the ultimate pressure bearing capacity of the target micro-element point based on the first evaluation parameter and the second evaluation parameter includes: comparing the first evaluation parameter and the second evaluation parameter to determine a smaller value between the first evaluation parameter and the second evaluation parameter; The ultimate pressure bearing capacity of the target micro-element point is determined based on the smaller value.

5. The fault trapping capacity evaluation method according to claim 1, characterized in that: The quantitative relationship model is: ； in, is the friction coefficient, represents the mud content in the logging data, 、 and are the model parameters to be fitted.

6. The fault trapping capacity evaluation method according to claim 1, characterized in that: Before constructing the quantitative relationship model between the friction coefficient and the shale content based on the well logging data of the extensional stress fault region to be evaluated, the method further includes: Divide the extensional stress fault area to be evaluated into micro-elements to obtain multiple fault micro-elements; Sampling is performed based on the plurality of fault elements to obtain sampling points; Well logging is performed on the sampling points to obtain well logging data.

7. The fault trapping capacity evaluation method according to claim 1, characterized in that: The first evaluation parameter is: ; The second evaluation parameter is: ;in, represents the normal stress of the target micro-element point, represents the shear stress at the target micro-element point, is the target friction coefficient of the target micro-element point; represents the formation pressure at the target micro-element point, Indicates the cross-fault pressure difference at the target micro-element point.

8. A fault trapping capacity evaluation device, characterized in that: include: A model building module is used to construct a quantitative relationship model between friction coefficient and shale content based on the well logging data of the extensional stress fault area to be evaluated; The quantitative relationship model represents the functional relationship between the friction coefficient and the shale content, the extensional stress fault region includes a plurality of micro-element points, and the well logging data is obtained by sampling and logging the plurality of micro-element points; A model application module is used to extract the target mud content of the target micro-element point and calculate the target friction coefficient of the target micro-element point based on the quantitative relationship model; The target micro-element point is any one of the multiple micro-element points; a first evaluation module, configured to calculate a first evaluation parameter of the mechanical stability of the target micro-element point based on the target friction coefficient, and calculate a second evaluation parameter of the sealing ability of the target micro-element point based on the target mud content; The second evaluation module is used to determine the ultimate pressure bearing capacity of the target micro-element point based on the first evaluation parameter and the second evaluation parameter.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the fault trap capability evaluation method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the fault trapping capacity evaluation method according to any one of claims 1 to 7 is implemented.

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