Method for evaluating fracability of shale oil reservoir, apparatus, device and storage medium

Abstract

A method for evaluating the fracability of a shale oil reservoir, an apparatus, a device and a storage medium. The method comprises: determining target affecting parameters and parameter importances of the target affecting parameters (S101); on the basis of the parameter importances of the target affecting parameters, determining an importance comparison result amongst the target affecting parameters (S102); on the basis of the importance comparison result amongst the target affecting parameters, determining initial weights of the target affecting parameters (S103); on the basis of the parameter values of the target affecting parameters, adjusting the initial weights of the target affecting parameters, thereby obtaining target weights of the target affecting parameters (S104); and, on the basis of the target weights of the target affecting parameters and the parameter values of the target affecting parameters, obtaining a first evaluation result representing the fracability of a target shale oil reservoir (S105). The described method improves the accuracy of fracability evaluation for shale oil reservoirs.

Description

Method, device and equipment for evaluating compressibility of shale oil reservoir and storage medium

[0001] The present application claims priority to the Chinese patent application No. 202411237144.9, filed on September 4, 2024, and entitled "Method, device and equipment for evaluating compressibility of shale oil reservoir and storage medium", the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the technical field of oil and gas stimulation and geotechnical engineering, in particular to a method, device and equipment for evaluating compressibility of shale oil reservoir and storage medium. BACKGROUND

[0003] With the progress of special mining technologies such as horizontal wells and large-scale hydraulic fracturing, China's shale oil exploration and development has made new progress and is becoming an important replacement resource for increasing oil reserves and production. Reservoirs require large-scale hydraulic fracturing modification technology, which is expensive, so before carrying out fracturing modification, the compressibility of the reservoir rock in the target interval needs to be evaluated to select high-quality reservoir intervals that can meet the fracturing requirements and significantly improve oil and gas recovery after fracturing.

[0004] Currently, the compressibility of shale oil reservoirs is mainly evaluated by rock mechanics tests, logging, microseismic monitoring, numerical simulation and model construction, etc. For example, CN116933555A discloses a method and system for evaluating the compressibility of composite reservoir rock, which was published on October 24, 2023, and describes an evaluation method that uses brittleness and viscosity indicators as first-level indicators for the compressibility evaluation model. However, this method only considers the superposition of two indicators and cannot fully reflect the compressibility of complex reservoir rocks.

[0005] It can be seen that the existing data interpretation or model construction methods have deficiencies and cannot accurately evaluate the fracturing capacity of complex lithofacies shale oil reservoirs. SUMMARY

[0006] The embodiments of the present application provide a method, device and equipment for evaluating the compressibility of shale oil reservoirs to accurately evaluate the compressibility of complex lithofacies shale oil reservoirs.

[0007] In a first aspect, the embodiments of the present application provide a method for evaluating the compressibility of shale oil reservoirs, comprising:

[0008] determining a target influence parameter and a parameter importance of the target influence parameter, wherein the target influence parameter is used to represent a hydraulic fracture longitudinal extension capability, a complex fracture network extension capability, or a multi-fracture synchronous extension capability of a target shale oil reservoir, and the parameter importance of the target influence parameter is different when the extension capability represented by the target influence parameter is different;

[0009] determining an importance ratio result between the target influence parameters according to the parameter importance of the target influence parameter;

[0010] determining an initial weight of the target influence parameter according to the importance ratio result between the target influence parameters;

[0011] adjusting the initial weight of the target influence parameter according to a parameter value of the target influence parameter to obtain a target weight of the target influence parameter;

[0012] obtaining a first evaluation result representing the compressibility of the target shale oil reservoir according to the target weight of the target influence parameter and the parameter value of the target influence parameter.

[0013] Optionally, the initial weight of the target influence parameter is determined according to the importance ratio result between the target influence parameters, and the method comprises:

[0014] determining an importance of a first target influence parameter and an importance of a second target influence parameter in the target influence parameters;

[0015] obtaining an importance ratio result of the first target influence parameter and the second target influence parameter according to the importance of the first target influence parameter and the importance of the second target influence parameter;

[0016] determining an element value between the first target influence parameter and the second target influence parameter according to the importance ratio result of the first target influence parameter and the second target influence parameter, wherein the element value between the first target influence parameter and the second target influence parameter is greater when the importance of the first target influence parameter is greater than the importance of the second target influence parameter;

[0017] constructing and obtaining an influence parameter matrix according to the first target influence parameter, the second target influence parameter, and the element value between the first target influence parameter and the second target influence parameter;

[0018] determining the initial weight of the target influence parameter according to the influence parameter matrix.

[0019] Optionally, the initial weight of the target influence parameter is determined according to the influence parameter matrix, and the method comprises:

[0020] determining a maximum eigenvalue of the influence parameter matrix;

[0021] According to the maximum eigenvalue of the influence parameter matrix, a consistency index of the influence parameter matrix is obtained;

[0022] If the consistency index of the influence parameter matrix meets the requirement of the consistency index threshold value, the weight of the target influence parameter in the influence parameter matrix is determined according to the influence parameter matrix;

[0023] If the consistency index of the influence parameter matrix does not meet the requirement of the consistency index threshold value, the element value between the first target influence parameter and the second target influence parameter is adjusted to obtain an adjusted adjustment element value;

[0024] The adjusted adjustment element value is taken as the element value between the first target influence parameter and the second target influence parameter, and the step of constructing and obtaining the influence parameter matrix according to the first target influence parameter, the second target influence parameter, and the element value between the first target influence parameter and the second target influence parameter is re-executed.

[0025] Optionally, the initial weight of the target influence parameter is adjusted according to the parameter value of the target influence parameter to obtain a target weight of the target influence parameter, including:

[0026] According to the parameter value of the target influence parameter, an entropy value of the target influence parameter is determined;

[0027] According to the entropy value of the target influence parameter, a second weight of the target influence parameter is determined;

[0028] According to the second weight of the target influence parameter, the initial weight of the target influence parameter is adjusted to obtain the target weight of the target influence parameter.

[0029] Optionally, the target influence parameter and the parameter importance of the target influence parameter are determined, including:

[0030] A target influencing factor in the comprehensive logging data of the target shale oil reservoir is determined, the target influencing factor being an influencing factor affecting the target influence parameter;

[0031] According to the factor importance of the target influencing factor, an importance comparison result between the target influencing factors is determined;

[0032] According to the importance comparison result between the target influencing factors, an initial weight of the target influencing factor is determined;

[0033] According to the factor value of the target influencing factor, the initial weight of the target influencing factor is adjusted to obtain a target weight of the target influencing factor;

[0034] According to the target weight of the target influencing factor and the factor value of the target influencing factor, the target influence parameter is obtained.

[0035] Optionally, after obtaining the first evaluation result representing the compressibility of the target shale oil reservoir according to the target weight of the target influence parameter and the parameter value of the target influence parameter, the method further comprises:

[0036] perform principal component analysis processing on the target logging parameters in the comprehensive logging data to determine principal components in the logging parameters and a cumulative contribution rate of the principal components, the target logging parameters at least including two parameters in acoustic travel time, natural gamma, formation density, formation resistivity, porosity, organic matter carbon content, maximum horizontal principal stress, oil saturation, and neutron logging value in the comprehensive logging data;

[0037] determine target principal components and target eigenvalues of the target principal components according to the cumulative contribution rate of the principal components, the cumulative contribution rate of the target principal components meeting a preset cumulative contribution rate requirement;

[0038] obtain a porosity factor, an oil and gas bearing factor, and a shale factor according to the target eigenvalues of the target principal components;

[0039] obtain a geological sweet spot parameter according to the porosity factor, the oil and gas bearing factor, and the shale factor;

[0040] obtain a target evaluation result representing the compressibility of the target shale oil reservoir according to the geological sweet spot parameter and the first evaluation result.

[0041] Optionally, obtaining the geological sweet spot parameter according to the porosity factor, the oil and gas bearing factor, and the shale factor comprises:

[0042] determining weights of the porosity factor, the oil and gas bearing factor, and the shale factor;

[0043] obtaining the geological sweet spot parameter according to the porosity factor, the oil and gas bearing factor, the shale factor, the weight of the porosity factor, the weight of the oil and gas bearing factor, and the weight of the shale factor.

[0044] In a second aspect, an embodiment of the present application provides a shale oil reservoir compressibility evaluation device, comprising:

[0045] a first determination module configured to determine target influence parameters and parameter importance degrees of the target influence parameters, wherein the target influence parameters are used to represent hydraulic fracture longitudinal extension capability, complex fracture network extension capability, or multi-fracture synchronous extension capability of a target shale oil reservoir, the target influence parameters represent different extension capabilities, and the parameter importance degrees of the target influence parameters are different;

[0046] a second determination module configured to determine importance degree comparison results between the target influence parameters according to the parameter importance degrees of the target influence parameters;

[0047] a third determination module configured to determine initial weights of the target influence parameters according to the importance degree comparison results between the target influence parameters.

[0048] The first obtaining module is configured to adjust the initial weight of the target influence parameter according to the parameter value of the target influence parameter, and obtain a target weight of the target influence parameter.

[0049] The second obtaining module is configured to obtain a first evaluation result of the compressibility of the target shale oil reservoir according to the target weight of the target influence parameter and the parameter value of the target influence parameter.

[0050] In a third aspect, an embodiment of the present application provides a shale oil reservoir compressibility evaluation device, including a memory and a processor.

[0051] The memory stores computer execution instructions.

[0052] The processor executes the computer execution instructions stored in the memory, so that the processor executes the first aspect and / or various possible implementation manners of the first aspect.

[0053] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores computer execution instructions, and the computer execution instructions are executed by the processor to realize the first aspect and / or various possible implementation manners of the first aspect.

[0054] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a computer program, and the computer program is executed by the processor to realize the first aspect and / or various possible implementation manners of the first aspect.

[0055] The shale oil reservoir compressibility evaluation method, device, equipment and storage medium provided by the embodiments of the present application can determine the target influence parameter for representing any one of the longitudinal expansion capacity of the hydraulic fracture, the complex fracture network expansion capacity, or the multi-fracture synchronous expansion capacity in the target shale oil reservoir, and determine the parameter importance of the target influence parameter, so as to compare the parameter importance and obtain an importance comparison result, thereby determining the initial weight of the target influence parameter. In order to avoid the deficiency of the initial weight, the initial weight is corrected according to the parameter value of the target influence parameter to obtain the target weight, and then the compressibility of the target shale oil reservoir is evaluated according to the target influence parameter and the corresponding target weight to obtain a first evaluation result. By quantifying the importance of the influence parameter and performing secondary correction of the weight, the multi-dimensional data can be used to evaluate the complex lithofacies shale oil reservoir fracturing capacity, thereby improving the accuracy of the reservoir compressibility evaluation, and the method has high operability. BRIEF DESCRIPTION OF DRAWINGS

[0056] The accompanying drawings, which are incorporated into and form a part of the specification, illustrate one embodiment consistent with the present application and, together with the description, serve to explain the principles of the application.

[0057] Fig. 1 is a flowchart of a shale oil reservoir compressibility evaluation method provided by the present application;

[0058] Fig. 2 is a flowchart of another shale oil reservoir compressibility evaluation method provided by the present application;

[0059] Fig. 3 is a schematic diagram of a shale oil reservoir compressibility evaluation method provided by the present application;

[0060] Fig. 4 is a structural schematic diagram of a shale oil reservoir compressibility evaluation device provided by the present application;

[0061] Fig. 5 is a structural schematic diagram of a shale oil reservoir compressibility evaluation device provided by the present application.

[0062] The specific embodiments of the present application have been shown in the above-described drawings, and will be described in more detail hereinafter. These drawings and the written description are not intended to restrict the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0063] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The following description is made with reference to the accompanying drawings in which like reference numerals refer to like elements in the several figures. The following exemplary embodiments described are not meant to represent all embodiments in accordance with the present application. Rather, they are merely examples of apparatus and methods in accordance with some aspects of the present application as detailed in the appended claims.

[0064] In the prior art, the elastic modulus, Poisson's ratio, compressive strength and other parameters of rock are measured by rock mechanics tests, and the compressibility of rock in the process of hydraulic fracturing is evaluated by these geostress parameters. In addition, the stress distribution, microstructure, elastic properties and other parameters of rock are analyzed by geomechanical modeling, microstructure analysis, acoustic testing, numerical simulation and other methods, and the deformation and fracture of rock are inferred or measured. However, there are many parameters affecting the compressibility of rock, the above evaluation methods have different focuses, and there is cross-influence between the parameters affecting the compressibility of rock, which makes it difficult to quantify the importance of each parameter and the influence caused thereby, resulting in the problem that the fracturing capacity of shale oil reservoir cannot be accurately evaluated.

[0065] In order to solve the above problems, the shale oil reservoir compressibility evaluation method provided in the present application is based on comprehensive logging data, combined with natural fracture development, ground stress characteristics and reservoir development characteristics to form a comprehensive evaluation method of multiple target influence parameters of the compressibility of the shale oil reservoir. The initial weight of the target influence parameters is determined by constructing a target influence parameter evaluation system, and the initial weight is modified again, so that the importance of the influence parameters is quantified. The multi-dimensional data is used to evaluate the complex lithofacies shale oil reservoir fracturing capacity, thereby improving the accuracy of the reservoir compressibility evaluation, and having high operability.

[0066] The execution subject of the shale oil reservoir compressibility evaluation method provided in the present application can be a server. The server can be a mobile phone, a computer, a tablet computer or the like. The present application does not particularly limit the implementation mode of the execution subject, as long as the execution subject can determine the target influence parameters and the parameter importance of the target influence parameters. The target influence parameters are used to represent the hydraulic fracture longitudinal expansion capacity, complex fracture network expansion capacity or multi-fracture synchronous expansion capacity of the target shale oil reservoir. The expansion capacities represented by the target influence parameters are different, and the parameter importance of the target influence parameters is also different. According to the parameter importance of the target influence parameters, the importance comparison result between the target influence parameters is determined. According to the importance comparison result between the target influence parameters, the initial weight of the target influence parameters is determined. According to the parameter value of the target influence parameters, the initial weight of the target influence parameters is adjusted to obtain the target weight of the target influence parameters. According to the target weight of the target influence parameters and the parameter value of the target influence parameters, the first evaluation result representing the compressibility of the target shale oil reservoir is obtained.

[0067] Firstly, the terms involved in the present application are explained:

[0068] Compressibility: the ability of a rock reservoir to form and maintain an effective fracture network during hydraulic fracturing. It is a key indicator for measuring the cracking and channel formation of the reservoir under the action of high-pressure fluid, so that oil and gas can flow out smoothly, which directly affects the effect of hydraulic fracturing and the final oil and gas production.

[0069] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail in the specific embodiments below. The specific embodiments below can be combined with each other, and the same or similar concepts or processes can not be described again in some embodiments. The embodiments of the present application will be described below with reference to the drawings.

[0070] Fig. 1 is a flowchart of a shale oil reservoir compressibility evaluation method provided in the present application. As shown in Fig. 1, the method can include:

[0071] S101, determine a target influence parameter and a parameter importance of the target influence parameter, wherein the target influence parameter is used to represent a hydraulic fracture longitudinal extension capability, a complex fracture network extension capability, or a multi-fracture synchronous extension capability of a target shale oil reservoir, and the parameter importance of the target influence parameter is different when the represented extension capabilities are different.

[0072] The hydraulic fracture longitudinal extension capability can refer to the extension capability of the fracture in the longitudinal direction during the hydraulic fracturing process.

[0073] The complex fracture network extension capability can refer to the capability of the fracture to form and extend into a complex network structure in the underground rock formation during the hydraulic fracturing process. The complex fracture network usually includes a main fracture and many branch fractures, forming a three-dimensional fracture network.

[0074] The multi-fracture synchronous extension capability can refer to the capability of multiple fractures to form and extend simultaneously during the hydraulic fracturing process.

[0075] In the shale oil reservoir, the hydraulic fracture longitudinal extension capability, the complex fracture network extension capability, and the multi-fracture synchronous extension capability are all affected by factors such as the mechanical properties of the formation, the stress state of the ground, the properties of the fracturing fluid, and the injection parameters.

[0076] In the present embodiment, the method for determining the target influence parameter and the parameter importance of the target influence parameter can include:

[0077] Determining a target influence factor in the comprehensive logging data of the target shale oil reservoir, the target influence factor being an influence factor affecting the target influence parameter;

[0078] According to the factor importance of the target influence factor, determining an importance comparison result between the target influence factors;

[0079] According to the importance comparison result between the target influence factors, determining an initial weight of the target influence factor;

[0080] According to the factor value of the target influence factor, adjusting the initial weight of the target influence factor to obtain a target weight of the target influence factor;

[0081] According to the target weight of the target influence factor and the factor value of the target influence factor, obtaining the target influence parameter.

[0082] The integrated logging data refers to various geological and physical parameter data obtained based on logging data, well logging data, and rock mechanics experimental results. This includes data such as well logging interpretation results, rock mechanics characteristics, mineral characteristics, and rock fracture morphology characteristics, such as formation resistivity, sonic velocity, density, natural gamma ray intensity, and porosity. In this embodiment, the integrated logging data can be core data, rock mechanics experimental data, single-well logging data, and well logging data.

[0083] In this embodiment, by processing and interpreting the comprehensive logging data, the target influencing factors can be obtained. The target influencing factors can refer to factors affecting the development of natural fractures, geostress, and reservoir development. For example, the target influencing factors can refer to the interfacial shear strength, interlayer stress difference, tensile strength difference, and vertical stress difference that affect the longitudinal propagation capacity of hydraulic fractures. They can also refer to the development of natural fractures, horizontal stress difference, and brittle parameters that affect the propagation capacity of complex fracture networks. Furthermore, they can refer to the stress heterogeneity, fracture toughness heterogeneity, and Young's modulus that affect the synchronous propagation capacity of multiple fractures. Since the synchronous propagation of multiple fractures is largely affected by heterogeneity, heterogeneity is taken as a consideration indicator.

[0084] Furthermore, the interfacial shear strength can be obtained by the following formula (1):

[0085] Where τ is the shear strength; C is the cohesion; and σ is the normal stress. It represents the internal friction angle of the rock.

[0086] The interlaminar stress difference can be obtained by the following formula (2): Δσ in =σ h2 -σ h1 (2)

[0087] Where, Δσ in The interlayer stress difference; σ h1 The minimum horizontal principal stress of the interlayer; σ h2 This represents the minimum horizontal principal stress of the fracturing layer.

[0088] The vertical stress difference can be obtained by the following formula (3): Δσ v =σ v -σ h (3)

[0089] Where, Δσ v The vertical stress difference; σ v For vertical stress; σ h This represents the minimum horizontal principal stress.

[0090] The parameters in the above formulas (2) and (3) can be obtained by the following formulas (4) to (8):

[0091] wherein σ v is the vertical stress; σ H is the maximum horizontal principal stress; σ h is the minimum horizontal principal stress; E d is the dynamic Young's modulus; μ d is the dynamic Poisson's ratio; Δt s is the shear wave time difference; α is the Biot coefficient; Δσ v is the vertical stress difference; Δσ in is the interlayer stress difference; σ h1 is the minimum horizontal principal stress of the barrier layer; σ h2 is the minimum horizontal principal stress of the fractured layer.

[0092] The horizontal stress difference can be obtained by calculating the difference between the maximum horizontal principal stress and the minimum horizontal principal stress in the above formula (4).

[0093] The stress heterogeneity can be determined according to the above formula (4) by the difference between the minimum horizontal principal stresses of different fractures.

[0094] The tensile strength difference can be obtained by the following formulas (9) to (11): S c = 10 3 E d (0.00816V sh + 0.00459(1.0-V sh )) (9)

[0095] wherein S c is the compressive strength; V sh is the estimated shale content; I sh is the shale index; GR max is the maximum natural gamma log value; Gr min is the minimum natural gamma log value; GR is the estimated natural gamma log value of the well section; GCUR is an empirical coefficient for calculating the shale volume, which is generally 2 for old formations and 3.7 for Tertiary formations. On this basis, since the tensile strength S t of the rock is about 1 / 10-1 / 3 of the compressive strength, the tensile strength difference is the difference between the maximum tensile strength and the minimum tensile strength.

[0096] The natural fracture development condition can be obtained by interpreting the core observation results and the imaging logging results in the comprehensive logging data.

[0097] The brittleness parameter can be obtained by the following equations (12) to (13):

[0098] where YM BRIT is the normalized Young's modulus; YM is the Young's modulus at the perforation; YM cmax is the maximum Young's modulus of the evaluation section; YM cmin is the minimum Young's modulus of the evaluation section; PR is the Poisson's ratio at the perforation; PR BRIT is the normalized Poisson's ratio; PR cmax is the maximum Poisson's ratio of the evaluation section; PR cmin is the minimum Poisson's ratio of the evaluation section; B rit is the brittleness parameter.

[0099] The fracture toughness heterogeneity can be obtained by the following equations (14) to (15): K IC = 0.2176P c + 0.0059S t 3 + 0.0923S t 2 + 0.517S t - 0.3322 (14)

[0100] where K IC is the fracture toughness; P c is the confining pressure.

[0101] In this embodiment, the target influence parameter can refer to a parameter result obtained by weighted summation calculation of the factor values of each influence factor in the target influence factor matrix and the target weight. The target influence factor can refer to at least one of the factors that affect the longitudinal extension capability of the hydraulic fracture, the complex fracture network extension capability, and the multi-fracture synchronous extension capability in the target shale oil reservoir. The method of determining the weight of the target influence factor and the target weight of the target influence parameter can be the same, which can be determined by the analytic hierarchy process and the entropy weight method, which is specifically described in the following embodiments and will not be repeated here.

[0102] S102, according to the parameter importance of the target influence parameter, determine the importance ratio result between the target influence parameters.

[0103] where the parameter importance can refer to subjective judgment of the importance of the target influence parameter based on the analytic hierarchy process, and an influence parameter matrix is constructed according to the judgment result to obtain the importance ratio result. The importance ratio result can refer to the importance size obtained by comparing the target influence parameters with each other, and the influence parameter matrix obtained by assigning values to the importance size.

[0104] Further, the influence parameter matrix can refer to a matrix obtained by comparing each two of the target influence parameters with each other to determine the importance of the two target influence parameters and assigning values to the target influence parameters according to the importance. In the influence parameter matrix, the row elements and the column elements are target influence parameters. Thus, when there are Q target influence parameters, the influence parameter matrix can be a Q*Q matrix. The elements in the influence parameter matrix can be determined according to the importance of the target influence parameters, which can be determined according to the data types represented by the target influence parameters. In some embodiments, the importance of the target influence parameters can be set by a user before the influence parameter matrix is constructed. For example, the target influence parameters can be divided into levels such as absolutely important, relatively important, generally important, and unimportant according to the importance. The importance ratios of the different levels are different. For example, the comparison result of the absolutely important and the generally important is different from the comparison result of the relatively important and the generally important.

[0105] In the embodiments of the present application, the 1-9 scale method of Santy can be used to quantify the target influence parameters, and the influence parameter matrix A=(a ij ) m×n wherein a ij represents the importance ratio of the target influence parameter i and the target influence parameter j, as shown in Table 1.

[0106] Table 1

[0107] S103, determining the initial weight of the target influence parameter according to the comparison result of the importance of the target influence parameters.

[0108] The initial weight of the target influence parameter can refer to the importance of each target influence parameter in all influence parameters obtained by the analytic hierarchy process. Further, in some embodiments, the initial weight of the target influence parameter can be calculated by the square root method or the sum method. For example, when the square root method is used to calculate the initial weight of the target influence parameter, the initial weight of the target influence parameter satisfies the formula:

[0109] wherein w i is the initial weight of the target influence parameter, a ij represents the importance ratio of the target influence parameter i and the target influence parameter j, and n is the number of target influence parameters.

[0110] It should be noted that when the square root method or the sum method is used to calculate the weight of the target influence parameter, the weight of the target influence parameter also needs to satisfy the condition AW=λ max W, wherein A in the condition can be the influence parameter matrix, and λ maxW is the normalized eigenvector of the largest eigenvalue λ max of the normalized eigenvector of the largest eigenvalue λ i of the normalized eigenvector of the largest eigenvalue λ

[0111] In the embodiment, the method of determining the initial weights of the target influence parameters according to the importance ratio between the target influence parameters can include:

[0112] determining the importance of the first target influence parameter and the importance of the second target influence parameter;

[0113] obtaining the importance ratio of the first target influence parameter and the second target influence parameter according to the importance of the first target influence parameter and the importance of the second target influence parameter;

[0114] determining the element value between the first target influence parameter and the second target influence parameter according to the importance ratio of the first target influence parameter and the second target influence parameter, wherein the element value between the first target influence parameter and the second target influence parameter is greater when the importance of the first target influence parameter is greater compared to the importance of the second target influence parameter;

[0115] constructing and obtaining the influence parameter matrix according to the first target influence parameter, the second target influence parameter, and the element value between the first target influence parameter and the second target influence parameter;

[0116] determining the initial weights of the target influence parameters according to the influence parameter matrix.

[0117] The first target influence parameter and the second target influence parameter are any two target influence parameters selected from the target influence parameters, and the first target influence parameter and the second target influence parameter can be the same or different. After determining the element value between the first target influence parameter and the second target influence parameter, the steps of determining the importance of the first target influence parameter and the importance of the second target influence parameter can be repeatedly performed until the step of determining the element value between the first target influence parameter and the second target influence parameter according to the importance ratio of the first target influence parameter and the second target influence parameter, thereby obtaining the element values between all target influence parameters and constructing the influence parameter matrix.

[0118] Further, the method of determining the initial weights of the target influence parameters according to the influence parameter matrix can include:

[0119] determining the largest eigenvalue of the influence parameter matrix;

[0120] obtaining the consistency index of the influence parameter matrix according to the largest eigenvalue of the influence parameter matrix;

[0121] If the consistency index of the influence parameter matrix meets the requirement of the consistency index threshold value, the weight of the target influence parameter in the influence parameter matrix is determined according to the influence parameter matrix.

[0122] If the consistency index of the influence parameter matrix does not meet the requirement of the consistency index threshold value, the element value between the first target influence parameter and the second target influence parameter is adjusted to obtain an adjusted adjustment element value.

[0123] The adjusted adjustment element value is taken as the element value between the first target influence parameter and the second target influence parameter, and the step of constructing and obtaining the influence parameter matrix according to the first target influence parameter, the second target influence parameter, and the element value between the first target influence parameter and the second target influence parameter is re-executed.

[0124] Wherein, the maximum eigenvalue can be calculated by the constructed influence parameter matrix A, the columns of the influence parameter matrix A are summed, each unit is normalized to obtain a standard matrix The standard matrix is added by row to obtain a vector w, and w is normalized to obtain a matrix W, and finally, the maximum eigenvalue λ of the judgment matrix is derived. max Wherein, the maximum eigenvalue λ max satisfies:

[0125] Wherein, n is the order of the matrix, that is, the number of influence parameters.

[0126] The consistency index can be an index for detecting whether the constructed influence parameter matrix has logical problems, wherein the consistency index CI can satisfy:

[0127] It should be noted that the consistency index is associated with the order of the matrix n, wherein when n<3, the judgment matrix can be determined to have complete consistency due to the small order of the matrix; when n≥3, the consistency index can be processed to obtain a new consistency index calculation result, wherein the new consistency index calculation result CR satisfies:

[0128] Wherein, RI can be a parameter determined according to the order of the matrix n, in the embodiments of the present application, the relationship between RI and the order of the matrix n can be shown in Table 2 as follows:

[0129] Table 2

[0130] After obtaining the consistency index calculation result CR, the consistency index calculation result CR is compared with the consistency index threshold, and a consistency test result is obtained according to the comparison result. For example, when the consistency index threshold is 0.1, when CR < 0.1, the consistency test is passed, otherwise it indicates that there is a logical problem in the influence parameter matrix, and the element value between the first target influence parameter and the second target influence parameter needs to be adjusted, thereby correcting the influence parameter matrix to obtain a logically correct influence parameter matrix, and recalculating the initial weight of the target influence parameter according to the logically correct influence parameter matrix.

[0131] S104, adjusting the initial weight of the target influence parameter according to the parameter value of the target influence parameter to obtain the target weight of the target influence parameter.

[0132] The parameter value of the target influence parameter can refer to the parameter result obtained by weighting and summing the weight of the target influence factor and the influence factor value of the target influence factor.

[0133] Since the initial weight of the target influence parameter depends on subjective judgment, different decision makers may give different comparison results, resulting in inconsistent results, therefore, the objective weighting method is used to adjust the initial weight to obtain the target weight, thereby balancing subjectivity and objectivity, and improving the reliability of comprehensive evaluation.

[0134] In the embodiment, the method of adjusting the initial weight of the target influence parameter according to the parameter value of the target influence parameter to obtain the target weight of the target influence parameter can include:

[0135] According to the parameter value of the target influence parameter, the entropy value of the target influence parameter is determined;

[0136] According to the entropy value of the target influence parameter, the second weight of the target influence parameter is determined;

[0137] According to the second weight of the target influence parameter, the initial weight of the target influence parameter is adjusted to obtain the target weight of the target influence parameter.

[0138] The initial weight can be corrected by using the entropy weight method:

[0139] First, the parameter value of the target influence parameter obtained by weighting and summing the target influence factor is standardized:

[0140] Where i is the segment number corresponding to a certain well depth range in the target shale oil reservoir; j is the target influence parameter of the target shale oil reservoir; x j is the parameter value of the target influence parameter; x ij ' is the standardized parameter value.

[0141] and the standardized parameter value is translated: x' ij = H + x' ij ;

[0142] wherein the value of H is set to 1.

[0143] Then, the data is quantified by using the specific gravity method to calculate the proportion of the target influence parameter:

[0144] Then, the jth entropy value is calculated:

[0145] Further, the difference coefficient of the jth target influence parameter, i.e., the redundancy z is determined: j = 1 - e j ;

[0146] Finally, the second weight of the jth target influence parameter is obtained:

[0147] Accordingly, the initial weight h j is corrected according to the second weight to obtain the target weight w j . w j = 50% * h j + 50% * V j .

[0148] S105, according to the target weight of the target influence parameter and the parameter value of the target influence parameter, a first evaluation result representing the compressibility of the target shale oil reservoir is obtained.

[0149] The first evaluation result can refer to the weighted sum of the weight of the target influence parameter and the parameter value of the target influence parameter, and the compressibility of the shale oil reservoir is quantitatively evaluated according to the engineering sweet spot parameter, wherein the engineering sweet spot parameter can refer to the engineering characteristics and parameters used to identify and evaluate regions with high economic value and production potential during the development of oil and gas fields.

[0150] Since the evaluation of the compressibility of the target shale oil reservoir only by the engineering sweet spot parameter is inaccurate, in order to more accurately obtain the evaluation result of the compressibility of the target shale oil reservoir, the method after obtaining the first evaluation result representing the compressibility of the target shale oil reservoir according to the target weight of the target influence parameter and the parameter value of the target influence parameter in the embodiments of the present application can further include:

[0151] The target logging parameters in the comprehensive logging data are subjected to principal component analysis processing to determine principal components in the logging parameters and cumulative contribution rates of the principal components, and the target logging parameters at least include two parameters in acoustic travel time, natural gamma, formation density, formation resistivity, porosity, organic matter carbon content, maximum horizontal principal stress, oil saturation, and neutron logging value in the comprehensive logging data.

[0152] According to the cumulative contribution rates of the principal components, target principal components that meet preset cumulative contribution rate requirements and target eigenvalues of the target principal components are determined according to the cumulative contribution rates of the principal components.

[0153] According to the target eigenvalues of the target principal components, a porosity factor, an oil and gas bearing factor, and a shale factor are obtained.

[0154] According to the porosity factor, the oil and gas bearing factor, and the shale factor, a geology sweet spot parameter is obtained.

[0155] According to the geology sweet spot parameter and the first evaluation result, a target evaluation result representing the compressibility of the target shale oil reservoir is obtained.

[0156] The target logging parameters are parameters that affect the geology sweet spot, such as most of the geological parameters such as mineral composition, shale thickness, gas content, Young's modulus, and Poisson's ratio. Since there are many target logging parameters, dimension reduction processing is performed on the target logging parameters by principal component analysis to transform multiple indexes into a few comprehensive indexes, and the comprehensive indexes are the principal components. Each principal component is a linear combination of original variables, is independent of each other, and retains most of the information of the original variables.

[0157] In the specific implementation process, the target logging parameters in the comprehensive logging data are subjected to standardization processing, and a covariance matrix R is calculated according to the standardized parameters, wherein the covariance matrix R satisfies:

[0158] wherein r ij is the covariance between the ith variable and the jth variable.

[0159] Further, eigenvalues λ are obtained by performing eigenvalue decomposition on the covariance matrix R, and eigenvectors u corresponding to the eigenvalues are calculated, wherein the eigenvalues satisfy λ1≥λ2≥…≥λ n ≥0, and the eigenvectors u j =(u 1j , u 2j …, u nj ), u nj represents the nth component of the jth eigenvector.

[0160] n principal components are calculated from the eigenvectors, and the n principal components satisfy: n principal components are calculated from the eigenvectors, and the n principal components satisfy:

[0161] wherein y1 is the first principal component, y2 is the second principal component, and so on, y n is the nth principal component.

[0162] Further, the contribution rate and the cumulative contribution rate are calculated through the above characteristic value and the corresponding characteristic vector, wherein:

[0163] The contribution rate b j satisfies:

[0164] The cumulative contribution rate a p satisfies:

[0165] In the embodiments of the present application, the characteristic values of the first, second, and third principal components with the highest cumulative contribution rates are selected as the porosity factor, the oil and gas bearing factor, and the shale factor, and the physical meanings of the public factors are determined according to the score coefficients of the extracted sensitivity parameters of the public factors, wherein the porosity factor is mainly affected by the porosity, the acoustic time difference, the neutron logging value, the formation density, and the like; the oil and gas bearing factor is mainly affected by the organic matter carbon content, the oil saturation, the formation resistivity, and the like; and the shale factor is mainly affected by the natural gamma, the formation density, the neutron logging value, the maximum horizontal principal stress, and the like. Finally, all the target logging parameters are reflected through the porosity factor, the oil and gas bearing factor, and the shale factor.

[0166] Further, the method for obtaining the geological sweet spot parameter can include the following steps:

[0167] determining the weight of the porosity factor, the weight of the oil and gas bearing factor, and the weight of the shale factor;

[0168] obtaining the geological sweet spot parameter according to the porosity factor, the oil and gas bearing factor, the shale factor, the weight of the porosity factor, the weight of the oil and gas bearing factor, and the weight of the shale factor.

[0169] The geological sweet spot parameter can refer to the geological features and parameters used to identify and evaluate the regions with high economic value and production potential in the reservoir in oil and gas exploration and development. The geological sweet spot parameter can be obtained by weighted summation of the porosity factor, the oil and gas bearing factor, and the shale factor.

[0170] The weight of the porosity factor, the weight of the oil and gas bearing factor, and the weight of the shale factor can be obtained by the calculation formula of the ternary linear regression fitting established by the aforementioned sensitivity parameters.

[0171] The target evaluation result can refer to a comprehensive compressibility evaluation of the shale oil reservoir in combination with the geological dessert parameter and the first evaluation result obtained according to the engineering dessert parameter, and specifically, the greater the geological dessert parameter and the engineering dessert parameter, the better the compressibility of the shale oil reservoir.

[0172] The shale oil reservoir compressibility evaluation method provided by the embodiment of the present application can determine the most reasonable target influence parameter in the case of multiple influence factors, and then determine the first evaluation result in the case of multiple target influence parameters, so as to realize the effect of comprehensively and accurately evaluating the compressibility of the shale oil reservoir.

[0173] FIG. 2 is a flowchart of another shale oil reservoir compressibility evaluation method provided by the present application, as shown in FIG. 2, the method can include:

[0174] S201, after normalizing the standard deviation of the logging basic parameters, the processed data is reduced in dimension to extract the porosity factor, the oil and gas bearing factor and the argillaceous factor by using the principal component analysis method, all logging basic data are reflected through the three common factors, and the geological dessert parameter is established by calculating the weight of each factor.

[0175] The logging basic parameters can refer to logging interpretation results, rock mechanics characteristics, mineral characteristics, rock fracture morphology characteristics and the like.

[0176] The porosity factor, the oil and gas bearing factor, the argillaceous factor and the geological dessert calculation results of a certain well section are shown in Table 3.

[0177] Table 3

[0178] S202, according to the core data, the rock mechanics experimental data, the single well logging data and the well logging data of the target reservoir, the influence factors of the hydraulic fracture longitudinal extension capacity of the target reservoir are obtained: the interface shear strength, the interlayer stress difference, the tensile strength difference and the vertical stress difference.

[0179] S203, the hydraulic fracture longitudinal extension capacity parameter is calculated by using the analytic hierarchy process and the entropy weight method.

[0180] The influence factor judgment matrix of the hydraulic fracture longitudinal extension capacity in the analytic hierarchy process is shown in Table 4, the weight is shown in Table 5, and the calculation results of the hydraulic fracture longitudinal extension capacity parameter are shown in Table 6.

[0181] Table 4

[0182] Table 5

[0183] Table 6

[0184] S204, obtaining influencing factors of complex fracture network extension capacity according to core data, rock mechanics experiment data and single well logging data and logging data of the target reservoir: natural fracture development, horizontal stress difference, brittleness parameter;

[0185] S205, calculating complex fracture network extension capacity parameters by using analytic hierarchy process and entropy weight method.

[0186] Among them, the judgment matrix of complex fracture network extension capacity influencing factors in the analytic hierarchy process is shown in Table 7, the weight is shown in Table 8, and the calculation results of complex fracture network extension capacity parameters are shown in Table 9.

[0187] Table 7

[0188] Table 8

[0189] Table 9

[0190] S206, obtaining influencing factors of multi-fracture synchronous extension capacity according to core data, rock mechanics experiment data and single well logging data and logging data of the target reservoir: stress heterogeneity, fracture toughness heterogeneity, Young's modulus;

[0191] S207, calculating multi-fracture synchronous extension capacity parameters by using analytic hierarchy process and entropy weight method.

[0192] Among them, the judgment matrix of multi-fracture synchronous extension capacity influencing factors in the analytic hierarchy process is shown in Table 10, the weight is shown in Table 11, and the calculation results of multi-fracture synchronous extension capacity parameters are shown in Table 12.

[0193] Table 10

[0194] Table 11

[0195] Table 12

[0196] S208, establishing complex lithofacies shale three-dimensional comprehensive engineering sweet spot evaluation calculation parameters by using analytic hierarchy process and entropy weight method according to the established hydraulic fracture longitudinal extension capacity parameters, complex fracture network extension capacity parameters and multi-fracture synchronous extension capacity parameters.

[0197] The judgment matrix of the calculation parameters of the project compressibility evaluation in the analytic hierarchy process is shown in Table 13, the weight is shown in Table 14, and the calculation result of the project compressibility evaluation is shown in Table 15.

[0198] Table 13

[0199] Table 14

[0200] Table 15

[0201] S209, the compressibility comprehensive evaluation parameter is calculated in combination with the geological dessert and the project dessert.

[0202] In the embodiment, the compressibility comprehensive evaluation parameter can be obtained by the following formula:

[0203] Wherein, F G is the geological dessert, F E is the project dessert, and F is the compressibility comprehensive evaluation parameter.

[0204] The calculation result of the compressibility comprehensive evaluation of a well section is shown in Table 16.

[0205] Table 16

[0206] The compressibility evaluation method of the shale oil reservoir provided in the embodiment can calculate the geological dessert parameter according to the logging basic data, calculate a plurality of extension capacity parameters according to other geological data, thereby obtaining the project dessert parameter, and further establish an evaluation model according to the project dessert parameter and the address dessert parameter, thereby evaluating the compressibility of the shale oil reservoir. The influence degree of each evaluation index is quantified, the geological dessert prediction and the project dessert prediction are fused, and the evaluation accuracy is improved.

[0207] FIG. 3 is a schematic diagram of the compressibility evaluation method of the shale oil reservoir provided in the application, as shown in FIG. 3, the method can include:

[0208] The geological dessert parameter is established by using the principal component analysis method through the pore factor, the oil and gas content factor and the argillaceous factor;

[0209] The hydraulic fracture longitudinal extension capacity parameter is calculated by using the analytic hierarchy process and the entropy weight method through the interface shear strength, the interlayer stress difference, the tensile strength difference and the vertical stress difference; the complex fracture network extension capacity parameter is calculated through the natural fracture development, the horizontal stress difference and the brittleness parameter; the multi-fracture synchronous extension capacity parameter is calculated through the stress heterogeneity, the fracture toughness heterogeneity and the Young's modulus; and then the project dessert parameter is established through the hydraulic fracture longitudinal extension capacity parameter, the complex fracture network extension capacity parameter and the multi-fracture synchronous extension capacity parameter;

[0210] A complex lithofacies shale oil reservoir compressibility comprehensive evaluation model is established by the geological dessert parameters and the engineering dessert parameters.

[0211] Fig. 4 is a structural schematic diagram of the shale oil reservoir compressibility evaluation device provided by the present application. As shown in Fig. 4, the shale oil reservoir compressibility evaluation device 40 provided by the present embodiment comprises a first determining module 401, a second determining module 402, a third determining module 403, a first obtaining module 404 and a second obtaining module 405. Wherein:

[0212] The first determining module 401 is configured to determine target influence parameters and parameter importance of the target influence parameters, wherein the target influence parameters are used to represent the hydraulic fracture longitudinal expansion capability, the complex fracture network expansion capability or the multi-fracture synchronous expansion capability of the target shale oil reservoir, and the parameter importance of the target influence parameters is different when the expansion capabilities represented by the target influence parameters are different;

[0213] The second determining module 402 is configured to determine the importance comparison result between the target influence parameters according to the parameter importance of the target influence parameters;

[0214] The third determining module 403 is configured to determine the initial weight of the target influence parameters according to the importance comparison result between the target influence parameters;

[0215] The first obtaining module 404 is configured to adjust the initial weight of the target influence parameters according to the parameter value of the target influence parameters to obtain the target weight of the target influence parameters;

[0216] The second obtaining module 405 is configured to obtain the first evaluation result representing the compressibility of the target shale oil reservoir according to the target weight of the target influence parameters and the parameter value of the target influence parameters.

[0217] Optionally, the third determining module 403 can be specifically configured to:

[0218] determine the importance of the first target influence parameter and the importance of the second target influence parameter in the target influence parameters;

[0219] obtain the importance comparison result between the first target influence parameter and the second target influence parameter according to the importance of the first target influence parameter and the importance of the second target influence parameter;

[0220] determine the element value between the first target influence parameter and the second target influence parameter according to the importance comparison result between the first target influence parameter and the second target influence parameter, wherein the element value between the first target influence parameter and the second target influence parameter is greater when the importance of the first target influence parameter is greater compared with the importance of the second target influence parameter;

[0221] constructing and obtaining the influence parameter matrix according to the first target influence parameter, the second target influence parameter, and the element value between the first target influence parameter and the second target influence parameter;

[0222] determining the initial weight of the target influence parameter according to the influence parameter matrix.

[0223] Optionally, the third determining module 403 can be specifically used for:

[0224] determining the maximum eigenvalue of the influence parameter matrix;

[0225] obtaining the consistency index of the influence parameter matrix according to the maximum eigenvalue of the influence parameter matrix;

[0226] if the consistency index of the influence parameter matrix meets the requirement of the consistency index threshold value, determining the weight of the target influence parameter in the influence parameter matrix according to the influence parameter matrix;

[0227] if the consistency index of the influence parameter matrix does not meet the requirement of the consistency index threshold value, adjusting the element value between the first target influence parameter and the second target influence parameter to obtain an adjusted adjustment element value;

[0228] re-executing the step of constructing and obtaining the influence parameter matrix according to the first target influence parameter, the second target influence parameter, and the element value between the first target influence parameter and the second target influence parameter, by taking the adjusted adjustment element value as the element value between the first target influence parameter and the second target influence parameter.

[0229] Optionally, the first obtaining module 404 can be specifically used for:

[0230] determining the entropy value of the target influence parameter according to the parameter value of the target influence parameter;

[0231] determining the second weight of the target influence parameter according to the entropy value of the target influence parameter;

[0232] adjusting the initial weight of the target influence parameter according to the second weight of the target influence parameter to obtain the target weight of the target influence parameter.

[0233] Optionally, the first determining module 401 can be specifically used for:

[0234] determining the target influence factor in the comprehensive logging data of the target shale oil reservoir, the target influence factor being an influence factor affecting the target influence parameter;

[0235] determining the importance ratio result between the target influence factors according to the factor importance of the target influence factors;

[0236] According to the importance ratio between the target influence factors, initial weights of the target influence factors are determined;

[0237] According to the factor values of the target influence factors, the initial weights of the target influence factors are adjusted to obtain target weights of the target influence factors;

[0238] According to the target weights of the target influence factors and the factor values of the target influence factors, the target influence parameters are obtained.

[0239] Optionally, the second obtaining module 405 can be specifically configured to:

[0240] The target logging parameters in the comprehensive logging data are subjected to principal component analysis processing to determine principal components in the logging parameters and cumulative contribution rates of the principal components, the target logging parameters at least including two parameters in acoustic travel time, natural gamma, formation density, formation resistivity, porosity, organic carbon content, maximum horizontal principal stress, oil saturation, and neutron logging value in the comprehensive logging data;

[0241] According to the cumulative contribution rates of the principal components, target principal components whose cumulative contribution rates meet preset cumulative contribution rate requirements and target eigenvalues of the target principal components are determined;

[0242] According to the target eigenvalues of the target principal components, a porosity factor, an oil and gas bearing factor, and a shale factor are obtained.

[0243] According to the porosity factor, the oil and gas bearing factor, and the shale factor, a geology sweet spot parameter is obtained.

[0244] According to the geology sweet spot parameter and the first evaluation result, a target evaluation result representing the compressibility of the target shale oil reservoir is obtained.

[0245] Optionally, the second obtaining module 405 can be specifically configured to:

[0246] Weights of the porosity factor, the oil and gas bearing factor, and the shale factor are determined.

[0247] According to the porosity factor, the oil and gas bearing factor, the shale factor, the weight of the porosity factor, the weight of the oil and gas bearing factor, and the weight of the shale factor, the geology sweet spot parameter is obtained.

[0248] The shale oil reservoir compressibility evaluation device provided in the embodiment can execute the method provided in the method embodiment, and has similar implementation principles and technical effects, which will not be described here.

[0249] Fig. 5 is a structural schematic diagram of the shale oil reservoir compressibility evaluation device provided in the present application. As shown in Fig. 5, the shale oil reservoir compressibility evaluation device 50 provided in the present embodiment comprises at least one processor 501 and a memory 502. Optionally, the device 50 further comprises a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected through a bus 504.

[0250] In the implementation process, the at least one processor 501 executes the computer-executed instructions stored in the memory 502, so that the at least one processor 501 executes the method described above.

[0251] The specific implementation process of the processor 501 can refer to the method embodiments described above, which have similar implementation principles and technical effects, and will not be described here in detail.

[0252] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the present application can be directly embodied as the execution of the hardware processor, or executed by the combination of hardware and software modules in the processor.

[0253] The memory can contain a random access memory (RAM), and can also include a non-volatile memory (NVM), such as at least one disk memory.

[0254] The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0255] The application further provides a computer program product, comprising a computer program or instructions, which, when executed by a processor, implement the steps in any shale oil reservoir compressibility evaluation method provided in the application.

[0256] The specific implementation of the above operations can refer to the foregoing embodiments, which will not be described here again.

[0257] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions or by controlling relevant hardware by the instructions, which can be stored in a computer readable storage medium and loaded and executed by a processor.

[0258] To this end, the embodiments of the application provide a computer readable storage medium, which stores a plurality of instructions, which can be loaded by a processor to execute the steps in any shale oil reservoir compressibility evaluation method provided in the embodiments of the application.

[0259] The storage medium can include a read only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0260] According to an aspect of the application, a computer program product or computer program is provided, which comprises computer instructions stored in a computer readable storage medium.

[0261] Since the instructions stored in the storage medium can execute the steps in any shale oil reservoir compressibility evaluation method provided in the embodiments of the application, the beneficial effects of any shale oil reservoir compressibility evaluation method provided in the embodiments of the application can be achieved, which will be described in detail in the foregoing embodiments and will not be described here again.

[0262] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. The application is intended to cover any variations, uses or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice in the art to which the application pertains. The specification and examples are to be regarded as exemplary only, and the true scope and spirit of the application are indicated by the following claims.

[0263] It should be understood that the application is not limited to the precise construction that has been described above and illustrated in the accompanying drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the application is limited only by the claims that follow.

Claims

1. A method for evaluating the compressibility of a shale oil reservoir, characterized in that, The method comprises the following steps: determining target influence parameters and parameter importance of the target influence parameters, wherein the target influence parameters are used to represent hydraulic fracture longitudinal extension capability, complex fracture network extension capability or multi-fracture synchronous extension capability of a target shale oil reservoir, the target influence parameters represent different extension capabilities and have different parameter importance; determining importance ratio results between the target influence parameters according to the parameter importance of the target influence parameters; determining initial weights of the target influence parameters according to the importance ratio results between the target influence parameters; adjusting the initial weights of the target influence parameters according to parameter values of the target influence parameters to obtain target weights of the target influence parameters; obtaining a first evaluation result representing compressibility of the target shale oil reservoir according to the target weights of the target influence parameters and the parameter values of the target influence parameters.

2. The method of claim 1, wherein, The step of determining the initial weights of the target influence parameters according to the importance ratio results between the target influence parameters comprises the following steps: determining importance of a first target influence parameter and importance of a second target influence parameter in the target influence parameters; obtaining importance ratio results of the first target influence parameter and the second target influence parameter according to the importance of the first target influence parameter and the importance of the second target influence parameter; determining element values between the first target influence parameter and the second target influence parameter according to the importance ratio results of the first target influence parameter and the second target influence parameter, wherein the element values between the first target influence parameter and the second target influence parameter are greater when the importance of the first target influence parameter is greater compared with the importance of the second target influence parameter; constructing and obtaining the influence parameter matrix according to the first target influence parameter, the second target influence parameter and the element values between the first target influence parameter and the second target influence parameter; determining the initial weights of the target influence parameters according to the influence parameter matrix.

3. The method of claim 2, wherein, The step of determining the initial weights of the target influence parameters according to the influence parameter matrix comprises the following steps: determining a maximum eigenvalue of the influence parameter matrix; obtaining a consistency index of the influence parameter matrix according to the maximum eigenvalue of the influence parameter matrix; if the consistency index of the influence parameter matrix meets a requirement of a consistency index threshold value, determining weights of target influence parameters in the influence parameter matrix according to the influence parameter matrix; if the consistency index of the influence parameter matrix does not meet the requirement of the consistency index threshold value, adjusting the element values between the first target influence parameter and the second target influence parameter to obtain adjusted adjustment element values. Re-executing the step of constructing and obtaining the influence parameter matrix according to the first target influence parameter, the second target influence parameter and the element value between the first target influence parameter and the second target influence parameter, with the adjusted element value of the adjustment element as the element value between the first target influence parameter and the second target influence parameter.

4. The method of claim 1, wherein, The adjusting of the initial weight of the target influence parameter according to the parameter value of the target influence parameter to obtain the target weight of the target influence parameter comprises: determining an entropy value of the target influence parameter according to the parameter value of the target influence parameter; determining a second weight of the target influence parameter according to the entropy value of the target influence parameter; adjusting the initial weight of the target influence parameter according to the second weight of the target influence parameter to obtain the target weight of the target influence parameter.

5. The method of claim 1, wherein, The determining of the target influence parameter and the parameter importance of the target influence parameter comprises: determining target influence factors in the comprehensive logging data of the target shale oil reservoir, the target influence factors being influence factors influencing the target influence parameter; determining an importance comparison result between the target influence factors according to the factor importance of the target influence factors; determining an initial weight of the target influence factors according to the importance comparison result between the target influence factors; adjusting the initial weight of the target influence factors according to the factor value of the target influence factors to obtain a target weight of the target influence factors; obtaining the target influence parameter according to the target weight of the target influence factors and the factor value of the target influence factors.

6. The method of claim 1, wherein, After the obtaining of the first evaluation result representing the compressibility of the target shale oil reservoir according to the target weight of the target influence parameter and the parameter value of the target influence parameter, the method further comprises: performing principal component analysis processing on target logging parameters in the comprehensive logging data to determine principal components in the logging parameters and a cumulative contribution rate of the principal components, the target logging parameters at least including two parameters in acoustic travel time, natural gamma, formation density, formation resistivity, porosity, organic matter carbon content, maximum horizontal principal stress, oil saturation, and neutron logging value; determining target principal components in which the cumulative contribution rate meets a preset cumulative contribution rate requirement and target eigenvalues of the target principal components according to the cumulative contribution rate of the principal components; obtaining a porosity factor, an oil and gas bearing factor and a shale factor according to the target eigenvalues of the target principal components; obtaining a geological sweet spot parameter according to the porosity factor, the oil and gas bearing factor and the shale factor; obtaining a target evaluation result representing the compressibility of the target shale oil reservoir according to the geological sweet spot parameter and the first evaluation result.

7. The method of claim 6, wherein, The obtaining of the geological sweet spot parameter according to the porosity factor, the oil and gas bearing factor and the shale factor comprises: determining a weight of the porosity factor, a weight of the oil and gas bearing factor and a weight of the shale factor; According to the porosity factor, the oil-gas bearing factor, the shale factor, the weight of the porosity factor, the weight of the oil-gas bearing factor, and the weight of the shale factor, a geological dessert parameter is obtained.

8. A device for evaluating the compressibility of shale oil reservoirs, characterized in that, Comprising: A first determining module, configured to determine target influence parameters and parameter importance degrees of the target influence parameters, wherein the target influence parameters are used to represent hydraulic fracture longitudinal extension capability, complex fracture network extension capability, or multi-fracture synchronous extension capability of a target shale oil reservoir, the extension capabilities represented by the target influence parameters are different, and the parameter importance degrees of the target influence parameters are also different; A second determining module, configured to determine importance degree comparison results between the target influence parameters according to the parameter importance degrees of the target influence parameters; A third determining module, configured to determine initial weights of the target influence parameters according to the importance degree comparison results between the target influence parameters; A first obtaining module, configured to adjust the initial weights of the target influence parameters according to parameter values of the target influence parameters, to obtain target weights of the target influence parameters; A second obtaining module, configured to obtain a first evaluation result representing compressibility of the target shale oil reservoir according to the target weights of the target influence parameters and the parameter values of the target influence parameters.

9. An apparatus for evaluating the compressibility of a shale oil reservoir, the apparatus comprising: Comprising: A memory and a processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory, so that the processor executes the method in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method in any one of claims 1-7.

11. A computer program product, characterised in that, The computer program is executed to implement the method in any one of claims 1-7.

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