Method and apparatus for calculating fault sealing coefficient of supergiant tight sandstone

Abstract

The embodiment of the present application provides a method and equipment for calculating fault sealing coefficient of thick and dense sandstone, and belongs to the field of oil and gas field development. The method comprises the following steps: obtaining fault related data of a target region according to seismic profile and plane structure diagram of the target region; selecting a sealing factor of the fault from the fault related data; determining evaluation grades and membership boundary values of each factor; constructing a membership function based on the evaluation grades and the membership boundary values; taking the factor as a factor set and the evaluation grade as an evaluation set, determining the membership of each factor in each evaluation grade based on the membership function, thereby constructing a fuzzy evaluation matrix; obtaining a comprehensive evaluation vector based on the fuzzy evaluation matrix and a first weight value discrimination matrix established in advance; and obtaining the sealing coefficient based on a second weight discrimination matrix and the comprehensive evaluation vector. The method is used for reducing the defects of low systematicness and comprehensiveness in the prior art.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development, and specifically to a method and equipment for calculating the fault sealing coefficient of thick, dense sandstone. Background Technology

[0002] In existing technologies, methods for evaluating fault sealing performance mainly include lithological matching, mudstone smearing, and opening / closing coefficient methods. These methods can evaluate fault sealing performance to some extent, but when dealing with the interaction of multiple factors, existing methods often rely on linear or single-factor analysis, making it difficult to accurately reflect fault sealing performance. In oil and gas field exploration, faults serve as important channels for oil and gas migration, and their sealing performance directly affects the accumulation and distribution of oil and gas. Traditional fault sealing performance evaluation methods largely rely on the experience and qualitative analysis of geological experts, lacking quantitative and systematic evaluation methods. With the development of theories such as fuzzy mathematics and multi-scale analysis, the need for comprehensive quantitative evaluation of fault sealing performance is increasingly urgent. The northern section of the Xihu Depression slope in the East China Sea Basin, near the source area, features thick, dense sandstone but lacks mudstone, making it impossible to quantitatively calculate fault sealing performance using traditional methods such as SGR. Therefore, a method that does not involve mudstone smearing and is not limited to a single factor is urgently needed to quantitatively calculate the fault sealing coefficient.

[0003] Chinese patent CN114254910A discloses a quantitative evaluation method for fault sealing performance, which uses a machine learning decision tree algorithm to construct a model to evaluate fault sealing performance; Chinese patent CN110632656A discloses a method and apparatus for analyzing the lateral sealing performance of faults, which establishes a lateral geological model perpendicular to the fault and uses this model to obtain the values ​​of various factors affecting the lateral sealing performance of faults; Chinese patent CN108343430A discloses a fuzzy mathematical comprehensive quantitative evaluation method for fault sealing performance.

[0004] In the process of realizing this invention, the inventors of this application discovered that the prior art and the aforementioned patents have the deficiency of low systematicness and comprehensiveness in evaluating the fault sealing degree of thick and dense sandstone. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for calculating the fault sealing coefficient of thick, dense sandstone, thereby reducing the shortcomings of the existing technology in the evaluation of the fault sealing degree of thick, dense sandstone, which has low systematicity and comprehensiveness.

[0006] To achieve the above objectives, in a first aspect, embodiments of the present invention provide a method for calculating the fault sealing coefficient of thick, dense sandstone. The method includes: obtaining fault-related data of the target area based on seismic profiles and planar structural maps of the target area, the fault-related data including fault data and stratigraphic data; selecting characterization factors of fault sealing from the fault-related data; determining the evaluation level and membership boundary value of each characterization factor; constructing a membership function based on the evaluation level and the membership boundary value; using the characterization factors as a factor set and the evaluation level as an evaluation set, determining the membership degree of each characterization factor at each evaluation level based on the membership function, thereby constructing a fuzzy evaluation matrix; calculating a comprehensive evaluation vector based on the fuzzy evaluation matrix and a pre-established first weight discriminant matrix; and calculating the sealing coefficient based on a pre-established second weight discriminant matrix and the comprehensive evaluation vector.

[0007] Optionally, the method further includes: comparing the blocking coefficient with a pre-set first threshold; if the blocking coefficient is greater than the first threshold, then the fault is determined to have good blocking performance.

[0008] Optionally, the characterization factors include three aspects: reservoir, structure, and lithology, wherein: the reservoir influencing factors include fault displacement, burial depth, and fault dip angle; the structure influencing factors include regional principal stress and fault strike angle; and the lithology influencing factors include diagenesis of fracture cementation zones.

[0009] Optionally, the fuzzy evaluation matrix is:

[0010]

[0011] Among them, W 1i W represents the membership degree of the fault dip angle at each of the evaluation levels. 2i W represents the membership degree of the principal stress in the region and the fault strike angle at each of the evaluation levels. 3i W represents the membership degree of the discontinuity at each of the evaluation levels. 4i W represents the membership degree of the burial depth at each of the evaluation levels. 5i The degree of membership of the diagenesis of the fracture cementation zone at each of the evaluation levels.

[0012] Optionally, the first weighting discrimination matrix is: Q = {Q1, Q2, Q3, Q4, Q5}, where Q1 is the weighting coefficient of the fault dip angle, Q2 is the weighting coefficient of the angle between the regional principal stress and the fault strike, Q3 is the weighting coefficient of the fault displacement, Q4 is the weighting coefficient of the burial depth, and Q5 is the weighting coefficient of the diagenesis of the fracture cementation zone. The second weighting discrimination matrix is: K = {K1, K2, K3}, where K1 is the weighting coefficient for an excellent evaluation level, K2 is the weighting coefficient for a medium evaluation level, and K3 is the weighting coefficient for a poor evaluation level, with K1 > K2 > K3.

[0013] Optionally, the method for obtaining the fault displacement, burial depth, and fault dip angle includes: reading the depth data of the hanging wall and footwall of the fault at the seismic interface from the fault-related data, and calculating the burial depth and the fault displacement; and measuring the dip angle of the fault using the fault analysis function of the Landmark software.

[0014] Optionally, the method for obtaining the fault strike angle includes: obtaining the fault strike angle on the planar structural diagram, and calculating the angle between the principal stress and the fault strike based on the principal stress direction of the target region, i.e., the fault strike angle.

[0015] Optionally, the method for obtaining the diagenetic activity of the fracture cemented zone includes: fitting a formula for the degree of fracture cementation and calcite content based on regional parameter statistics, and calculating the value of the diagenetic activity of the fracture cemented zone through the calcite content.

[0016] Optionally, the first weight discrimination matrix and the second weight discrimination matrix are obtained by expert survey or analytic hierarchy process.

[0017] On the other hand, the present invention provides an apparatus for calculating the fault sealing coefficient of thick, dense sandstone, the apparatus comprising a memory and a processor, the processor being configured to run a program, wherein the program, when run, is configured to perform the method described in any of the preceding embodiments.

[0018] The technical solution proposed in this invention employs fuzzy mathematical logic, comprehensively considering multiple characterizing factors of fault sealing performance, quantifying the contribution of each characterizing factor to fault sealing performance, and realizing a quantitative evaluation of the fault sealing degree of thick, tight sandstone, thereby improving the systematicness and comprehensiveness of the evaluation. The calculated sealing coefficient can directly guide the development and production of oil and gas fields, providing decision support for oil and gas field development, and has high practical value.

[0019] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0021] Figure 1 This is a flowchart illustrating a method for calculating the fault sealing coefficient of a thick, dense sandstone according to an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of the main fault distribution in the target area provided by an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of a data processing flow provided in an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram illustrating the intersection of the degree of fracture cementation and the calcite content in the target area according to an embodiment of the present invention;

[0025] Figure 5 This is a schematic diagram of the structure of a device provided in an embodiment of the present invention.

[0026] Explanation of reference numerals in the attached figures

[0027] 101 Processor 102 Memory

[0028] 103 bus 10 devices Detailed Implementation

[0029] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0030] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0031] Figure 1 This is a flowchart illustrating a method for calculating the fault sealing coefficient of a thick, dense sandstone formation according to an embodiment of the present invention. Figure 2 This is a schematic diagram showing the distribution of major faults in a target area according to an embodiment of the present invention. (Reference) Figure 1 and Figure 2 The method includes the following steps S1-S7.

[0032] Step S1: Based on the seismic profile and planar structural map of the target area, obtain the fault-related data of the target area, which includes fault data and stratigraphic data.

[0033] Step S2: Select the characterization factor of fault sealing from the fault-related data.

[0034] Step S3: Determine the evaluation level and membership boundary value of each of the characterization factors.

[0035] Step S4: Construct a membership function based on the evaluation level and the membership boundary value.

[0036] Step S5: Using the characterization factors as a factor set and the evaluation levels as an evaluation set, determine the membership degree of each characterization factor on each evaluation level based on the membership function, thereby constructing a fuzzy evaluation matrix.

[0037] Step S6: Calculate the comprehensive evaluation vector based on the fuzzy evaluation matrix and the pre-established first weight value discrimination matrix.

[0038] Step S7: Calculate the blocking coefficient based on the pre-established second weight discrimination matrix and the comprehensive evaluation vector.

[0039] Among them, super-thick dense sandstone refers to dense sandstone with a thickness greater than 200m. The fault sealing performance of this type of fault cannot be quantitatively calculated using traditional methods such as SGR. Through the above steps proposed in this invention, fuzzy mathematical logic is used to comprehensively consider multiple characterizing factors of fault sealing performance, quantify the contribution of each characterizing factor to fault sealing performance, realize the quantitative evaluation of the fault sealing performance of super-thick dense sandstone, and improve the systematicness and comprehensiveness of the evaluation.

[0040] It should be noted that in step S3, when determining the evaluation level and membership boundary value of each characterization factor, the evaluation level is divided into three categories: Excellent (L1), Medium (L2), and Poor (L3). The membership boundary value is determined based on the characteristics of each characterization factor after analyzing the data. The membership boundary value indicates the boundary of the characterization factor's value on the membership function of a certain evaluation level. For example, if the value x of a characterization factor is in the range [A, B], and the evaluation level is Excellent, then the membership boundary value is [C, D], where C < A, D > B. The specific values ​​can be adjusted according to the actual situation. Generally, the distance between C and A, and the distance between B and D, are smaller than the distance between A and B. When a characterization factor takes a value in the range [C, D], this value can be substituted into the membership function to calculate the membership degree of the characterization factor on that evaluation level. Since [A, B] is a range, a trapezoidal membership function can be constructed to calculate the value of the characterization factor on the corresponding evaluation level. The membership function for the Excellent evaluation level can be:

[0041]

[0042] In step S4, when constructing the membership function based on the evaluation level and membership boundary value, a Gaussian membership function, a trapezoidal membership function, a triangular membership function, or other types of membership functions can be constructed. The function can be adjusted according to different actual production conditions. This invention does not limit the selection of membership functions.

[0043] Furthermore, the method also includes: comparing the blocking coefficient with a pre-set first threshold; if the blocking coefficient is greater than the first threshold, then the fault is determined to have good blocking performance.

[0044] In actual production, a first threshold can be set based on the geological conditions of the target area to quantitatively evaluate the fault sealing performance of that area. For example, by statistically analyzing the regional empirical parameters of lateral sealing by the thick, dense sandstone in the Xihu Depression of the East China Sea Basin, the first threshold for that area can be set to 0.7. When the calculated sealing coefficient is greater than 0.7, the fault is considered to have good sealing performance.

[0045] Figure 3 This is a schematic diagram of a data processing flow provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the characterization factors include three aspects: reservoir, structure, and lithology. Among them, the factors affecting the reservoir include fault displacement, burial depth, and fault dip angle; the factors affecting the structure include the regional principal stress and the fault strike angle; and the factors affecting the lithology include diagenesis of the fracture cementation zone.

[0046] When selecting characterization factors, the existing data are first summarized and organized, and then the selection is made using expert experience. In this embodiment, five influencing factors were selected: fault displacement, burial depth, fault dip angle, angle between regional principal stress and fault strike, and diagenesis of the fracture cementation zone, to analyze the lateral sealing properties of the fault. In other embodiments of the present invention, other influencing factors can also be selected as characterization factors based on experience, but the present invention is not limited thereto.

[0047] Furthermore, the fuzzy evaluation matrix is:

[0048]

[0049] Among them, W 1i W represents the membership degree of the fault dip angle at each of the evaluation levels. 2i W represents the membership degree of the principal stress in the region and the fault strike angle at each of the evaluation levels. 3i W represents the membership degree of the discontinuity at each of the evaluation levels. 4i W represents the membership degree of the burial depth at each of the evaluation levels. 5i The membership degree of the diagenesis of the fracture cementation zone at each of the evaluation levels is represented by the value of i, which ranges from 1 to 3.

[0050] It is understandable that each row of the fuzzy evaluation matrix corresponds to a characterization factor, and each column corresponds to an evaluation level, where W j1 The membership degree of each characterization factor on L1, W j2 The membership degree of each characterization factor on L1 and W j3 , where j represents the membership degree of each characterization factor on L3, with j ranging from 1 to 5, and each membership degree is calculated using the membership function.

[0051] Furthermore, the first weight value discrimination matrix is:

[0052] Q = {Q1, Q2, Q3, Q4, Q5}, where Q1 is the weighting coefficient of the fault dip angle, Q2 is the weighting coefficient of the angle between the regional principal stress and the fault strike, Q3 is the weighting coefficient of the fault displacement, Q4 is the weighting coefficient of the burial depth, and Q5 is the weighting coefficient of the diagenesis of the fracture cementation zone. The second weighting discrimination matrix is:

[0053] K = {K1, K2, K3}

[0054] Where K1 is the weight coefficient for an excellent rating, K2 is the weight coefficient for a medium rating, and K3 is the weight coefficient for a poor rating, with K1 > K2 > K3.

[0055] After obtaining the fuzzy evaluation matrix R and the first weight value discrimination matrix Q, the comprehensive evaluation vector A can be calculated based on R and Q:

[0056] A = Q × R = {Q1 × W} 11 +Q2×W 21 +Q3×W 31 +Q4×W 41 +Q5×

[0057] W51,Q1×W12+Q2×W22+Q3×W32+Q4×W42+Q5×W52, Q1×W13+Q2×W23+Q3×W33+Q4×W43+Q5×W53=A1,A2,A3,

[0058] After calculating the comprehensive evaluation vector A, the blocking coefficient S is then calculated using the second weight discrimination matrix K.

[0059] S={A1,A2,A3}·{K1,K2,K3}=A1×K1+A2×K2+A3×K3,

[0060] This yields the desired blocking coefficient.

[0061] Each characterization factor contributes differently to the fault sealing effect, therefore different characterization factors correspond to different weight coefficients. The method for establishing the first weight value discrimination matrix is ​​as follows: based on the characteristics of the study area itself, comprehensively considering the characteristics of each characterization factor, judging the relative importance of each characterization factor, and using the analytic hierarchy process (AHP) to establish a weight value discrimination table, thus obtaining the first weight value discrimination matrix. In some embodiments, the five characterization factors are divided into three levels: the angle between the regional principal stress and the fault strike and the diagenesis of the fault cementation zone are level one, with a weight coefficient of 0.3; the fault displacement is level two, with a weight coefficient of 0.2; and the fault dip angle is level three, with a weight coefficient of 0.1. Thus, the value of the first weight value discrimination matrix is ​​Q = {Q1, Q2, Q3, Q4, Q5} = {0.1, 0.3, 0.2, 0.1, 0.3}.

[0062] Each value in the discrimination matrix K corresponds to a weight of an evaluation level. When calculating the blocking coefficient, the result with an excellent evaluation level is prioritized. Therefore, K1 is the maximum value and should have the largest weight, while K3 is the minimum value and is only used as reference data. For example, the second weight discrimination matrix can be set to K = {K1, K2, K3} = {0.6, 0.3, 0.1}, or K1 can have a larger weight, and the second weight discrimination matrix can be set to K = {K1, K2, K3} = {0.7, 0.2, 0.1}.

[0063] In other embodiments of the present invention, the values ​​of the first weight value discrimination matrix and the second weight value discrimination matrix can be adjusted according to the actual production situation. It is worth noting that the weight coefficients must satisfy Q1+Q2+Q3+Q4+Q5=1 and K1+K2+K3=1.

[0064] In some embodiments, the first weight discrimination matrix and the second weight discrimination matrix can also be obtained by expert survey method.

[0065] Furthermore, the method for obtaining the fault displacement, burial depth, and fault dip angle includes: reading the depth data of the hanging wall and footwall of the fault at the seismic interface from the fault-related data, and calculating the burial depth and the fault displacement; and measuring the dip angle of the fault using the fault analysis function of the Landmark software.

[0066] Furthermore, the method for obtaining the fault strike angle includes: obtaining the fault strike angle on the planar structural diagram, and calculating the angle between the principal stress and the fault strike based on the principal stress direction of the target region, i.e., the fault strike angle.

[0067] Furthermore, the method for obtaining the diagenetic activity of the fracture cemented zone includes: fitting a formula for the degree of fracture cementation and calcite content based on regional parameter statistics, and calculating the value of the diagenetic activity of the fracture cemented zone through the calcite content.

[0068] Figure 4 This is a schematic diagram illustrating the intersection of the degree of fracture cementation and calcite content in the target area according to an embodiment of the present invention, as shown below. Figure 4 As shown, when the calcite content in the fault zone and fault filling is ≥20%, the fracture permeability is <0.1mD, and the fault zone and fault begin to be sealed; when the calcite content in the fault zone and fault filling is ≥55.48%, the fault zone and fault are completely sealed, that is, the diagenesis of the fracture cemented zone is 1. By statistically analyzing multiple thick and dense sandstone lateral sealing faults, the formula for fitting the degree of fracture cementation and calcite content is: y=0.0237x-0.3149,x∈[20,55.48]. After obtaining the calcite content, the value of the diagenesis of the fracture cemented zone is calculated using this formula.

[0069] When determining the membership boundary values ​​for each characterization factor, first determine the values ​​for each factor, categorized as excellent, moderate, and poor, in that order. For fault displacement, these can be divided into 80m-150m, 150m-220m, and 30m-80m; for burial depth, they can be divided into less than 4000m-4500m, greater than 4500m, and less than 4000m; for fault dip angle, they can be divided into 52°-56°, greater than 56°, and less than 52°; for the angle between the regional principal stress and the fault strike, they can be divided into 70°-90°, 50°-70°, and 30°-50°; and for diagenesis of the fracture cementation zone, they can be divided into 0.7-1, 0.3-0.7, and less than 0.3. Based on actual conditions, a certain proportion can be added to the left and right ends of the above values ​​to obtain the membership boundary values. After defining the boundary values, for each acquired characterization factor, first determine which interval it belongs to, and then substitute it into the membership function to calculate the corresponding membership degree. In some embodiments, a break distance of 4000m-4500m is also considered excellent. In this case, when the break distance is between 80m-150m and 4000m-4500m, the membership degree for an excellent rating can be set to 1.

[0070] The method proposed in this invention can simultaneously reflect the overall sealing performance of the target stratigraphic segment and is applicable to fault sealing performance evaluation under complex conditions. Comparative analysis of practical application cases demonstrates that the evaluation results of this method are consistent with the distribution patterns of oil and gas. When the sealing coefficient in the Xihu Depression of the East China Sea Basin is greater than 0.7, the fault can seal oil and gas in both thick, tight sandstone blocks, more accurately reflecting the actual situation, thereby reducing exploration risks and improving the success rate of oil and gas exploration.

[0071] This invention also provides an apparatus for calculating the fault sealing coefficient of thick, dense sandstone, for example, such as... Figure 5 As shown, the device includes a memory and a processor, the processor being used to run a program, wherein the program is run to perform the method for calculating the fault sealing coefficient of a thick, dense sandstone.

[0072] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0073] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and the fault sealing coefficient of thick, dense sandstone can be calculated by adjusting the kernel parameters.

[0074] This invention also provides a computer program product that, when executed on a data processing device, is adapted to execute a program for initializing the calculation of the fault sealing coefficient of the thick, dense sandstone.

[0075] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0076] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0077] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0078] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0079] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0080] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0081] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0082] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0083] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0084] The acquisition, transmission, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0085] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

Claims

1. A method for calculating the fault sealing coefficient of thick, dense sandstone, characterized in that, The method includes: Based on the seismic profile and planar structural map of the target area, fault-related data of the target area are obtained, including fault data and stratigraphic data. Characteristic factors for fault sealing are selected from the fault-related data; Determine the evaluation level and membership boundary value of each of the aforementioned characterization factors; Construct a membership function based on the evaluation level and the membership boundary value; The characterization factors are used as a factor set, the evaluation levels are used as an evaluation set, and the membership degree of each characterization factor on each evaluation level is determined based on the membership function, thereby constructing a fuzzy evaluation matrix. Based on the fuzzy evaluation matrix and the pre-established first weight value discrimination matrix, the comprehensive evaluation vector is calculated; and The blocking coefficient is calculated based on the pre-established second weight discrimination matrix and the comprehensive evaluation vector.

2. The method according to claim 1, characterized in that, The method further includes: The blocking coefficient is compared with a pre-set first threshold. If the blocking coefficient is greater than the first threshold, the fault is determined to have good blocking performance.

3. The method according to claim 1, characterized in that, The characterization factors include three aspects: reservoir, structure, and lithology, among which: The factors influencing the reservoir include fault displacement, burial depth, and fault dip angle; The influencing factors of the structure include the regional principal stress and the angle between the fault strike; The factors influencing the lithology include diagenesis of fracture cementation zones.

4. The method according to claim 3, characterized in that, The fuzzy evaluation matrix is: Among them, W 1i W represents the membership degree of the fault dip angle at each of the evaluation levels. 2i W represents the membership degree of the principal stress in the region and the fault strike angle at each of the evaluation levels. 3i W represents the membership degree of the discontinuity at each of the evaluation levels. 4i W represents the membership degree of the burial depth at each of the evaluation levels. 5i The degree of membership of the diagenesis of the fracture cementation zone at each of the evaluation levels.

5. The method according to claim 4, characterized in that, The first weight value discrimination matrix is: Q = {Q1, Q2, Q3, Q4, Q5} Wherein, Q1 is the weighting coefficient of the fault dip angle, Q2 is the weighting coefficient of the angle between the regional principal stress and the fault strike, Q3 is the weighting coefficient of the fault displacement, Q4 is the weighting coefficient of the burial depth, and Q5 is the weighting coefficient of the diagenesis of the fracture cementation zone. The second weighted discrimination matrix is: K = {K1, K2, K3} Where K1 is the weight coefficient for an excellent rating, K2 is the weight coefficient for a medium rating, and K3 is the weight coefficient for a poor rating, with K1 > K2 > K3.

6. The method according to claim 3, characterized in that, The methods for obtaining the fault displacement, burial depth, and fault dip angle include: The depth data of the hanging wall and footwall of the fault at the seismic interface are read from the fault-related data to determine the burial depth and the fault displacement; and The dip angle of the fault was measured using the fault analysis function of the Landmark software.

7. The method according to claim 3, characterized in that, The method for obtaining the fault strike angle includes: The strike angle of the fault is obtained on the planar structural diagram. Based on the principal stress direction of the target region, the angle between the principal stress and the fault strike is calculated, i.e., the fault strike angle.

8. The method according to claim 3, characterized in that, The methods for obtaining the diagenetic process of the fracture cemented zone include: Based on the method of regional parameter statistics, a formula is fitted to the relationship between the degree of fracture cementation and calcite content, and the value of diagenesis of the fracture cementation zone is obtained by calculating the calcite content.

9. The method according to claim 1, characterized in that, The first weight discrimination matrix and the second weight discrimination matrix are obtained through expert survey or analytic hierarchy process.

10. An apparatus for calculating the fault sealing coefficient of thick, dense sandstone, the apparatus comprising a memory and a processor, characterized in that, The processor is used to run a program, wherein the program is run to perform: the method as described in any one of claims 1-9.

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