A Method for Determining Seismic Hazard Induced by Shale Gas Extraction Based on Fuzzy Hierarchical Analysis

Abstract

This application discloses a method for determining the seismic hazard induced by shale gas development based on fuzzy hierarchical analysis. Addressing the technical problem of the difficulty in determining the seismic hazard induced by shale gas development, the technical solution in this specification uses fuzzy hierarchical analysis to predict seismic hazard during the development process, thus providing conditions for improving the safety of development. Furthermore, to ensure the accuracy of the induced seismic hazard prediction, the technical solution in this application, based on extensive scientific practice, designs the selection of the number of influencing factors used in the induced seismic hazard prediction process. This ensures prediction accuracy while avoiding overfitting of the prediction results due to the correlation between influencing factors.

Description

Technical Field

[0001] This application relates to the field of data processing technology applicable to specific computational models, and in particular to a method for determining the seismic hazard induced by shale gas extraction based on fuzzy hierarchical analysis. Background Technology

[0002] With the continuous advancement of large-scale industrial shale gas extraction in my country, the workload of hydraulic fracturing and other related operations has surged. The question of whether hydraulic fracturing can induce earthquakes has attracted widespread attention, even impacting the rational development of resources such as natural gas and shale oil and gas. Therefore, the issue of earthquakes induced by shale gas extraction has become a crucial scientific, technological, and social problem that must be addressed in current energy development.

[0003] How to effectively assess the seismic hazard of hydraulic fracturing zones, identify key factors influencing hydraulic fracturing-induced earthquakes, and safeguard people's lives and property has become an urgent scientific problem to be solved. On the one hand, this demonstrates the great potential of data processing technologies based on specific computational models in determining the seismic hazard induced by shale gas extraction; on the other hand, it also shows that there is still a broad prospect for technological expansion in this field. Summary of the Invention

[0004] This application provides a method for determining the seismic hazard induced by shale gas extraction based on fuzzy hierarchical analysis, so as to at least partially solve the above-mentioned technical problems.

[0005] The embodiments of this application adopt the following technical solutions:

[0006] In a first aspect, embodiments of this application provide a method for determining the seismic hazard induced by shale gas extraction based on fuzzy hierarchical analysis, the method comprising:

[0007] For the gas field to be developed, based on the indicators of shale gas extraction-induced earthquakes, the corresponding situations are predicted and refined. Each indicator contains several index factors, which are then used as influencing factors. The number of these influencing factors is positively correlated with the number of risk levels included in the preset hazard assessment results, positively correlated with the maximum potential loss caused by the highest risk included in the hazard assessment results, and the number of influencing factors obtained when the gas field has a history of mining is greater than the number obtained when the gas field has no history of mining. [The last sentence appears to be incomplete and possibly refers to a distance from the gas field.] The number of influencing factors obtained when there are other mining areas within the specified range is greater than the number of influencing factors obtained when there are no other mining areas within the specified distance from the gas field to be mined; the specified distance is positively correlated with the shale gas pressure stored in the gas field to be mined, positively correlated with the amount of shale gas stored in the gas field to be mined, and negatively correlated with the value of the difference between the first difference and the second difference, where the first difference is the difference between the maximum and minimum mining depths of the gas field to be mined, and the second difference is the difference between the average mining depth and the minimum mining depth of the gas field to be mined;

[0008] A hazard prediction model is constructed by using the aforementioned influencing factors as the indicator layer, the aforementioned indicators as the criterion layer, and the obtained seismic hazard assessment results as the target layer.

[0009] The weight vector of the hazard prediction model is determined based on the fuzzy hierarchical analysis method.

[0010] Based on the correlation between the influencing factors obtained from expert experience and the risk level assessment results, a fuzzy evaluation matrix is ​​established;

[0011] The fuzzy evaluation matrix is ​​weighted based on the weight vector to obtain the target evaluation result matrix, which represents the induced seismic hazard of the gas field to be exploited with reference to the influencing factors.

[0012] In an optional embodiment of this specification, the method further includes:

[0013] When the seismic occurrence rate at the geographical location of the gas field to be developed is greater than a preset seismic occurrence threshold, the indicators include at least one of the following: the tectonic stress field environment of the gas field to be developed, the fault activity at the geographical location of the gas field to be developed, the fracture development of the gas field to be developed, the communication between the gas field to be developed and the fluid, the injection distance, the injection depth, the historical seismic background at the geographical location of the gas field to be developed, and the B value; the B value is obtained based on the medium strength and stress magnitude of the area where the gas field to be developed is located.

[0014] In an optional embodiment of this specification, the method further includes:

[0015] When the seismic occurrence rate of the geographical location of the gas field to be developed is not greater than a preset seismic occurrence threshold, the indicators include at least one of the following: the tectonic stress field environment of the gas field to be developed, the fault activity of the geographical location of the gas field to be developed, the fracture development of the gas field to be developed, the communication between the gas field to be developed and the fluid, the injection distance, and the injection depth.

[0016] In an optional embodiment of this specification, the method further includes:

[0017] The number of influencing factors when other mineral mining areas within the specified distance from the gas field to be exploited are gas resource mining areas is greater than the number of influencing factors when other mineral mining areas within the specified distance from the gas field to be exploited are non-gas resource mining areas.

[0018] In an optional embodiment of this specification, the method further includes:

[0019] The risk assessment results include levels of: high risk and low risk; or,

[0020] The risk assessment results include the following levels: high risk, medium risk, and low risk.

[0021] In an optional embodiment of this specification, the method further includes:

[0022] When other mineral mining areas within a specified distance from the gas field to be exploited are gas resource mining areas, the risk assessment results include the following levels: high risk, medium risk, and low risk.

[0023] In an optional embodiment of this specification, the method further includes:

[0024] The influencing factors are determined when the ratio of free gas content to adsorbed gas content in the gas field to be developed is not less than a ratio threshold; the ratio threshold is negatively correlated with the seismic occurrence rate of the geographical location of the gas field to be developed.

[0025] Secondly, embodiments of this application also provide a device for determining the seismic hazard induced by shale gas extraction based on fuzzy hierarchical analysis, the device comprising:

[0026] The influencing factor determination module is configured as follows: For a shale gas field to be exploited, based on the indicators of shale gas extraction-induced earthquakes, the corresponding situations are predicted and refined. Several index factors included in each indicator are obtained as influencing factors. The number of influencing factors is positively correlated with the number of levels included in the preset hazard assessment results, positively correlated with the maximum potential loss caused by the highest risk included in the hazard assessment results, and the number of influencing factors obtained when the shale gas field has a history of mining is greater than the number of influencing factors obtained when the shale gas field has no history of mining. The distance from the shale gas field to be exploited... The number of influencing factors obtained when there are other mining areas within a specified distance of the gas field is greater than the number of influencing factors obtained when there are no other mining areas within a specified distance of the gas field to be mined; the specified distance is positively correlated with the shale gas pressure stored in the gas field to be mined, positively correlated with the amount of shale gas stored in the gas field to be mined, and negatively correlated with the value of the difference between the first difference and the second difference, where the first difference is the difference between the maximum and minimum mining depths of the gas field to be mined, and the second difference is the difference between the average and minimum mining depths of the gas field to be mined;

[0027] The hazard prediction model construction module is configured to: use the influencing factors as the indicator layer, the indicators as the criterion layer, and obtain the seismic hazard degree evaluation results as the target layer to construct a hazard prediction model;

[0028] The weight vector determination module is configured to: determine the weight vector of the hazard prediction model based on fuzzy hierarchical analysis.

[0029] The fuzzy evaluation matrix construction module is configured to: establish a fuzzy evaluation matrix based on the correlation between the influencing factors obtained from expert experience and the risk level evaluation results;

[0030] The target evaluation result matrix determination module is configured to: weight the fuzzy evaluation matrix based on the weight vector to obtain the target evaluation result matrix, wherein the target evaluation result matrix represents the induced seismic hazard of the gas field to be exploited with reference to the influencing factors.

[0031] Thirdly, embodiments of this application also provide an electronic device, including:

[0032] Processor; and

[0033] A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the method described in the first aspect.

[0034] Fourthly, embodiments of this application also provide a computer-readable storage medium storing one or more programs that, when executed by an electronic device including multiple applications, cause the electronic device to perform the steps of the method described in the first aspect.

[0035] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0036] To address the technical challenge of determining the seismic hazard induced by shale gas development, the technical solution in this specification utilizes fuzzy hierarchical analysis (AHP) to predict seismic hazard during the extraction process, thus improving extraction safety. Furthermore, to ensure the accuracy of the seismic hazard prediction, the technical solution in this application, based on extensive scientific practice, designs the selection of the number of influencing factors used in the seismic hazard prediction process. This approach ensures prediction accuracy while avoiding overfitting of the prediction results due to the correlation between influencing factors. Attached Figure Description

[0037] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0038] Figure 1 A schematic diagram illustrating the process of the method for determining the seismic hazard induced by shale gas extraction based on fuzzy hierarchical analysis, provided in the embodiments of this specification.

[0039] Figure 2 This is a schematic diagram of the hazard prediction model provided in the embodiments of this specification;

[0040] Figure 3 This is a schematic diagram of the area division provided for an embodiment of this specification;

[0041] Figure 4 This specification provides a zoning map for induced seismic hazard assessment in a shale gas extraction area in Sichuan Province, as part of an embodiment of the present specification.

[0042] Figure 5 This is a schematic diagram of the structure of an electronic device in an embodiment of this specification. Detailed Implementation

[0043] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. Similar elements in different embodiments are referred to by related similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of the present application. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to the present application are not shown or described in the specification. This is to avoid obscuring the core parts of the present application with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0044] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.

[0045] The serial numbers assigned to components in this document, such as "first" and "second," are used only to distinguish the described objects and have no sequential or technical meaning. The terms "connection" and "linkage" used in this application, unless otherwise specified, include both direct and indirect connections (linkages).

[0046] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0047] With the continuous advancement of large-scale industrial shale gas extraction in my country, the workload of hydraulic fracturing and other related tasks has surged. The question of whether hydraulic fracturing induces earthquakes has attracted widespread attention, even impacting the rational development of resources such as natural gas and shale oil and gas. Therefore, the issue of shale gas extraction-induced earthquakes has become a crucial scientific, technological, and social problem that current energy development must address. How to effectively assess the seismic hazard of hydraulic fracturing zones, identify key factors inducing hydraulic fracturing-induced earthquakes, and ensure the safety of people's lives and property has become an urgent scientific problem to be solved. This invention patent, based on extensive previous research on shale gas extraction-induced earthquakes, identifies the influencing factors of shale gas extraction-induced earthquakes and employs fuzzy hierarchical analysis to assess the hazard of shale gas extraction-induced earthquakes.

[0048] like Figure 1As shown, the method for determining the seismic hazard induced by shale gas extraction based on fuzzy hierarchical analysis in this specification includes the following steps:

[0049] S100: For the gas field to be developed, the corresponding situation is predicted according to the indicators of earthquakes induced by shale gas development, and the indicators are refined to obtain several indicator factors included in each indicator, which are used as influencing factors.

[0050] The gas fields to be exploited in this specification are shale gas fields that have not yet been developed or whose development has not been completed.

[0051] The indicators are the factors that influence the induction of earthquakes and can be determined based on expert experience and actual circumstances.

[0052] In an optional embodiment of this specification, the index is determined based on the seismic occurrence rate of the geographical location of the gas field to be developed. In this embodiment, when the seismic occurrence rate of the geographical location of the gas field to be developed is greater than a preset seismic occurrence threshold (which can be an empirical value or positively correlated with the degree of unmanned operation of the planned extraction methods), the index includes at least one of the following: the tectonic stress field environment of the gas field to be developed, the fault activity of the geographical location of the gas field to be developed, the fracture development of the gas field to be developed, the communication between the gas field to be developed and the fluid, injection distance, injection depth, historical seismic background of the geographical location of the gas field to be developed, and b-value. When the seismic occurrence rate of the geographical location of the gas field to be developed is not greater than the preset seismic occurrence threshold, the index includes at least one of the following: the tectonic stress field environment of the gas field to be developed, the fault activity of the geographical location of the gas field to be developed, the fracture development of the gas field to be developed, the communication between the gas field to be developed and the fluid, injection distance, and injection depth.

[0053] The value of b is derived from the medium strength and stress magnitude of the region where the gas field to be developed is located. Specifically, logN = a - bM is the formula in the Gutenberg-Richter equation, where N is the frequency of earthquakes with magnitude M, and M is the earthquake magnitude. a and b are constants; a represents the level of seismic activity within the statistical time and region; the value of b represents the ratio of large to small earthquakes in the region, with a smaller value when there are relatively more large earthquakes.

[0054] For example, there is a certain correlation between the tectonic stress field environment of a gas field to be developed and the fault activity of its geographical location. If the means to mitigate earthquake risks are relatively complete and the losses caused by earthquakes are low, excessive consideration of both may lead to overfitting of risk prediction results. Conversely, if the means to mitigate earthquake risks are relatively incomplete and the losses caused by earthquakes are high, insufficient consideration of at least one of the two factors may result in low accuracy of risk prediction.

[0055] In view of this, the detailed objectives in this specification are: to ensure that the number of influencing factors is positively correlated with the number of levels included in the preset risk assessment results (higher levels indicate higher accuracy requirements, thus requiring an increase in the number of influencing factors obtained through refinement to ensure accuracy); to be positively correlated with the maximum potential loss caused by the highest risk included in the risk assessment results (to identify risks as much as possible); and that the number of influencing factors obtained when the gas field to be exploited has a history of mining is greater than the number obtained when the gas field to be exploited has no history of mining (gas fields with mining records have more historical data that can be collected, providing more basis for prediction, and the number can be increased to ensure the rational use of data); and that other mining activities exist within a specified distance from the gas field to be exploited. The number of influencing factors obtained under the condition of a mining area is greater than the number of influencing factors obtained under the condition that there are no other mining areas within a specified distance from the gas field to be mined (this is beneficial for identifying risks and preventing risks from spreading to other mining areas); the specified distance is positively correlated with the pressure of shale gas stored in the gas field to be mined, positively correlated with the amount of shale gas stored in the gas field to be mined (if there is a risk, the potential harm is greater, and the risk needs to be identified as much as possible), and negatively correlated with the value of the difference between the first difference and the second difference. The first difference is the difference between the maximum and minimum mining depths of the gas field to be mined, and the second difference is the difference between the average and minimum mining depths of the gas field to be mined (this difference can characterize the mining difficulty and the utilization rate of the mined resources to a certain extent).

[0056] Furthermore, in a further optional embodiment of this specification, the number of influencing factors when other mineral mining areas within the specified distance from the gas field to be exploited are gas resource mining areas is greater than the number of influencing factors when other mineral mining areas within the specified distance from the gas field to be exploited are non-gas resource mining areas.

[0057] Refinement can also be understood as breakdown. For example, "fault activity" can be refined into "active and inactive," or further refined into "frequently active, moderately active, and inactive." Different degrees of refinement result in different numbers of influencing factors. Which indicator to refine more or less can be determined based on expert experience.

[0058] In one optional embodiment of this specification, the hazard assessment results include levels of high risk and low risk. In another optional embodiment of this specification, the hazard assessment results include levels of high risk, medium risk, and low risk. The finer the level classification, the higher the accuracy of the prediction results. Furthermore, when other mineral extraction areas within a specified distance from the gas field to be exploited are gas resource extraction areas, the hazard assessment results include levels of high risk, medium risk, and low risk. If an earthquake occurs, the risk of gas resource leakage is high, and the resulting losses may be significant, thus requiring improved prediction accuracy.

[0059] In an optional embodiment of this specification, the influencing factors are determined when the ratio of free gas content to adsorbed gas content in the gas field to be exploited is not less than a ratio threshold; the ratio threshold is negatively correlated with the seismic occurrence rate of the geographical location of the gas field to be exploited, so as to avoid the consumption of data processing resources.

[0060] S102: Construct a hazard prediction model by using the influencing factors as the indicator layer, the indicators as the criterion layer, and the obtained seismic hazard assessment results as the target layer.

[0061] The hazard prediction model in this specification comprises three levels, progressively increasing to ultimately arrive at the seismic hazard assessment result. For example, the hazard prediction model is as follows: Figure 2 As shown.

[0062] The method described in this specification decomposes the induced earthquake risk assessment problem into multiple levels and multiple factors, calculates the interrelationships and membership relationships (including but not limited to: correlation and relevance) of the assessment factors, constructs a comparison matrix through pairwise comparisons, determines the relative importance of each factor in the level, and then constructs a judgment matrix to determine the weight of each element, thereby providing a basis for the selection of decision-making schemes.

[0063] S104: Determine the weight vector of the hazard prediction model based on the fuzzy hierarchical analysis method.

[0064] Fuzzy Analytic Hierarchy Process (FAHP) is a decision analysis method that combines Analytic Hierarchy Process (AHP) and fuzzy mathematics. It was proposed by T.S. Thaaty, a professor of operations research in the United States, in the 1970s, and aims to solve complex system analysis problems that combine qualitative and quantitative methods.

[0065] When applying AHP analysis to solve problems, the first step is to organize and hierarchically structure the problem, constructing a hierarchical structural model. The highest level is the target level, representing the predicted goal or outcome; the levels above act as criteria, governing the elements in the lower-level criterion level; the lowest level is the indicator level. This forms a hierarchical structural model. The weight vector obtained in this step can, to a certain extent, characterize the correlation between influencing factors and the degree of impact of this correlation on risk.

[0066] In an optional embodiment of this specification, the process of determining the weight vector is as follows:

[0067] 1.1 Constructing the comparison judgment matrix

[0068] The core of the Analytic Hierarchy Process (AHP) is the construction of the judgment matrix. The most crucial improvement to AHP is replacing the difficult-to-precise nine-scale judgment with a readily applicable three-scale judgment when comparing the importance of two elements. The three-scale method is used to compare the importance of factors at each level pairwise, constructing the corresponding comparison judgment matrix C. ij The values ​​“0”, “1”, and “2” are used to represent the vague concepts of “unimportant”, “equally important”, and “important”, respectively. ij This indicates the relative importance of the i-th element and the j-th element, and C ∈ C. ii =1.

[0069] Assuming there are n elements at the same level, the following comparison matrix can be obtained using a three-scale:

[0070]

[0071]

[0072] Then calculate the importance ranking index r for each factor. i for:

[0073]

[0074] Next, we calculate the pairwise comparison matrix under the three-scale and a random pairwise comparison coefficient b. ij as follows:

[0075]

[0076] From equations (1) to (3), the nine-scale indirect judgment matrix is ​​obtained as follows:

[0077]

[0078] 1.2 Establishment of the Quasi-Optimal Consistent Matrix

[0079] This patent employs the concept of an optimal transfer matrix to construct the judgment matrix more accurately, avoiding consistency checks and adjustments to the judgment matrix. By solving for the optimal transfer matrix using the judgment matrix, and then solving for the eigenvector corresponding to the largest eigenvalue of the quasi-optimal consistency matrix, the weight values ​​of each factor can be obtained, ensuring that the judgment matrix naturally meets the consistency requirements, thus avoiding consistency checks and adjustments.

[0080] According to equations (1) and (4) above, C = [c ij ], B = [b ij ], let A=[a ij ]∈R m×n It is a real matrix.

[0081] Definition 1: If c ij =1 / c ij If b, then C is a reciprocal matrix; ij =-b ji If B is an antisymmetric matrix, then B is an antisymmetric matrix.

[0082] Definition 2: If C is a reciprocal matrix, and c ij =c ik / c jk If B is an antisymmetric matrix, then C is consistent; if B is an antisymmetric matrix, and b ij =b ik -b jk If B is the transitive matrix, then B is the decision matrix. Clearly, the decision matrices B are inverse and consistent.

[0083] Let a ij =lgb ij If (i,j=1,2,....,n), then matrix A is an antisymmetric matrix and also a transitive matrix.

[0084] If there exists a transfer matrix D such that... If the minimum value is found, then D is the optimal transfer matrix of A.

[0085] If A is an antisymmetric matrix, then its optimal transfer matrix D satisfies:

[0086]

[0087] If B is a reciprocal matrix, A = lgB, and D is the optimal transfer matrix of A, then B' = 10d Let B be a near-optimal consistent matrix. As shown above, the weight values ​​can be directly obtained from B' without further consistency checks.

[0088] 1.3 Calculating Factor Weights

[0089] The weight calculation based on the judgment matrix, mathematically speaking, is the calculation of the matrix's eigenvectors. To simplify the calculation, an approximate square root method is used. First, calculate B. ij 'The product of the elements in each row M' i (i = 1, 2, ..., n), then calculate the root. Finally, for vectors Normalization is performed, that is...

[0090]

[0091] Then, W = (W1W2ΛW) n ) T This is the weight vector we are looking for.

[0092] S106: Based on the correlation between the influencing factors obtained from expert experience and the risk level assessment results, a fuzzy evaluation matrix is ​​established.

[0093] In an optional embodiment of this specification, the process of establishing the fuzzy evaluation matrix may be as follows:

[0094] 2.1 Establishing the factor set and evaluation set

[0095] The set of influencing factors U is a set composed of various influencing factors affecting the object of evaluation, where U = {u1, u2, u3…u}. n}

[0096] An evaluation set is a collection of all possible overall evaluation results that evaluators may make about the evaluated object, denoted by V: V = {V1, V2, ..., V...} n}. Here, element Vj represents the j-th evaluation result, which can be represented by different levels, comments, or numbers depending on the actual situation.

[0097] 2.2 Constructing the fuzzy evaluation matrix

[0098] Given a factor set U and an evaluation set V, the evaluation begins with individual factors in factor set U to determine the degree of membership of the evaluated object to each element in factor set U. Then, the evaluation sets of the n factors are combined to form a total fuzzy evaluation matrix, typically denoted by R.

[0099]

[0100] In the formula: 0≤r ij≤1, r ij This represents the membership degree of the i-th factor to the j-th risk level. Commonly used methods for calculating membership include the experimental method, empirical formula method, fuzzy statistical method, and illustrative method. This specification uses the fuzzy statistical method.

[0101] In another optional embodiment of this specification,

[0102] Where Q is the correction factor. The formula for calculating Q is:

[0103]

[0104] In the formula, F i It is the i-th fault. It is the maximum stress obtained from the survey of the i-th fault. It is the minimum stress obtained from the survey of the i-th fault. This is the average stress obtained from the survey of the i-th fault. max The average displacement of the fault with the largest vertical displacement in historical earthquakes among all faults. p is the number of faults involved in the gas field to be developed. g is a coefficient that is positively correlated with the ratio of free gas content to adsorbed gas content in the gas field to be developed and negatively correlated with the minimum production depth of the gas field to be developed.

[0105] S108: The fuzzy evaluation matrix is ​​weighted based on the weight vector to obtain the target evaluation result matrix.

[0106] The target evaluation result matrix in this specification represents the induced seismic hazard of the gas field to be exploited, taking the aforementioned influencing factors into account. In other words, the target evaluation result matrix can be viewed as a quantified set of all risks associated with exploitation. After obtaining the target evaluation result matrix, experts can interpret it to decide whether to proceed with exploitation and how to adjust the exploitation plan.

[0107] To address the technical challenge of determining the seismic hazard induced by shale gas development, the technical solution in this specification utilizes fuzzy hierarchical analysis (AHP) to predict seismic hazard during the extraction process, thus improving extraction safety. Furthermore, to ensure the accuracy of the seismic hazard prediction, the technical solution in this application, based on extensive scientific practice, designs the selection of the number of influencing factors used in the seismic hazard prediction process. This approach ensures prediction accuracy while avoiding overfitting of the prediction results due to the correlation between influencing factors.

[0108] Specifically, the fuzzy comprehensive evaluation result vector RS for the shale mining-induced seismic hazard assessment can be obtained by using the synthesis operator to synthesize W and the fuzzy evaluation matrix R. After normalizing RS, the result of the evaluation object is determined according to the principle of maximum membership.

[0109] The synthesis rule of the fuzzy comprehensive evaluation method can be expressed by the following formula:

[0110] RS = W·R

[0111] In the formula, "·" is a common operator in fuzzy matrices.

[0112] 3.1 Construction of a hierarchical model for induced seismic hazard assessment

[0113] The following is a detailed explanation of the shale gas extraction-induced seismic hazard assessment model and weight calculation in one embodiment.

[0114] like Figure 2 As shown, to evaluate the seismic hazard induced by shale gas extraction, a hierarchical model of the seismogenic factors must first be established. Based on the understanding of the formation conditions and mechanisms of shale gas extraction-induced earthquakes, and according to the actual situation of global shale gas extraction-induced earthquake cases, nine influencing factors were selected to form the criterion layer U: tectonic stress field environment (G1), fault activity (G2), fracture development (G3), fluid communication (G4), injection distance (H1), injection depth (H2), earthquake occurrence rate (S1), b-value (S2), and historical seismic activity background (S3).

[0115] U=(G1,G2,G3,G4,H1,H2,S1,S2,S3)

[0116] Below the criteria layer is the indicator layer, which is further subdivided into 24 sets of influencing factors:

[0117] G1=(G 11 G 12 G 13 ),G2=(G 21 G 22 ),G3=(G 31 G 32 G 33 ),G4=(G 41 G 42 G 43 ),

[0118] H1=(H 11 H 12 H 13 ),H2=(H 21 H 22 H 23 )

[0119] S1=(S 11 ,S 12 ),S2=(S 21 ,S 22 ,S 23 ),S3=(S 31 ,S 32 ,S 33 )

[0120] The assessment criteria for the seismic hazard induced by shale gas extraction are divided into three levels, forming a set of evaluation comments:

[0121] V = {High Risk, Medium Risk, Low Risk}, with corresponding scores set as follows:

[0122] RS=(0.8~1.0,0.4~0.8,0.0~0.4)

[0123] 3.2 Determination of the weights of seismic induction factors for each layer in the hierarchical structural model

[0124] Using the principles of the Analytic Hierarchy Process (AHP), a weight analysis was performed on the established hazard assessment index system. The method for determining the weights of the criterion layer is as follows:

[0125] (1) Invite experts in the shale gas extraction industry and experts in induced earthquake research to conduct a questionnaire survey, obtain the importance ranking values ​​of each factor in the criterion layer, and determine the 3-scale comparison matrix Cu.

[0126] (2) Calculate the importance ranking index r max =17, r min =6, transform the 3-scale comparison judgment matrix into a 9-scale indirect judgment matrix B. u .

[0127] (3) Calculate the transfer matrix A sequentially. u Optimal transfer matrix D u And the optimal consistency matrix B ij Calculate the relative weights W of the elements in the criterion layer. u .

[0128] 3-scale comparison matrix C in the criterion layer u 9-scale indirect judgment matrix B u and the relative weights W of each inducing factor u See the following formula:

[0129]

[0130]

[0131] Finally, the eigenvectors of the quasi-consistent matrix are calculated and normalized to obtain the weight vector W = (0.087, 0.521, 0.031, 0.087, 0.067, 0.031, 0.067, 0.055, 0.052).

[0132] The method for determining the weights of the indicator layer is as follows:

[0133] (1) Based on the collected existing earthquake case data, the importance ranking values ​​of each seismogenic factor in the index layer are obtained, and then the 3-scale comparison matrix C is determined. For example, the index layer of the criterion layer factor tectonic stress field G1 is divided into 3 factor states: compression G 11 Pulling G 12 Cut G 13 Based on the collected earthquake cases, 90% of shale gas extraction-induced earthquakes were caused by strike-slip faults, i.e., shear stress environments; followed by normal faults under tensile stress environments; and then reverse faults under compressive stress environments. Therefore, the order of importance is: shear (G... 13 > Pulling (G) 12 > Extrusion (G) 11 ).

[0134] (2) The 3-scale comparison judgment matrix was transformed into a 9-scale indirect judgment matrix B;

[0135] (3) Find the eigenvectors of the quasi-consistent matrix and normalize them to obtain the weight vectors of each index layer element.

[0136] W G1 =(W G11 W G12 W G13 );W G2 =(W G21 W G22 );W G3 =(W G31 W G32 );

[0137] W G4 =(W G41 W G42 W G43 );W H1 =(W H11 W H12 W H13 );W H2 =(W H21 W H22 W H23 );

[0138] W S1 =(W S11 W S12 );WS2 =(W S21 W S22 W S23 );W S3 =(W S31 W S32 W S33 ).

[0139] Table 1. Factors and Status in the Assessment of Seismic Hazard Induced by Shale Gas Extraction

[0140]

[0141] In fuzzy comprehensive evaluation, the seismic hazard assessment induced by shale gas extraction is divided into three levels: high risk, medium risk, and low risk. The evaluation set is defined as: V = {high risk (V1); medium risk (V2); low risk (V3)}. Another crucial issue is the construction of the membership function, which describes the degree of membership of the research object to a certain fuzzy subset. Considering the characteristics of shale gas extraction-induced seismicity and the rigor, comprehensiveness, and overlap of the applied membership function, and combining the characteristics of relevant membership functions, a Gaussian function is selected. The final membership degree of each indicator to the evaluation set V is obtained, thus yielding the fuzzy relation matrix R. Based on the synthesis method of fuzzy comprehensive evaluation, the seismic hazard RS induced by shale gas extraction is calculated.

[0142]

[0143] In the formula, n = 1, 2, 3. Based on the principle of maximum membership, the RS value is determined to obtain the risk assessment level.

[0144] The following describes a practical application of the technical solution in this specification:

[0145] To verify and apply this method, a shale gas extraction area in Yibin City, Sichuan Province, was selected to determine the state of induced earthquake factors and conduct hazard analysis.

[0146] This shale gas extraction area is located at the junction of Sichuan, Yunnan, and Guizhou provinces, structurally situated in the transition zone between the gentle fold belt of southern Sichuan and the Daliangshan-Daloushan fault-fold belt. Its eastern side is influenced by compressive stress from the eastern Sichuan-Hunan-Hubei tectonic belt, its western side by long-range compressive stress transmission from the Longmenshan direction, its northern side is confined by the Sichuan Basin and Huayingshan fault zone, and its southern side is superimposed with compressional uplift caused by the tectonic transformation of the Ziyun-Luodian fault zone. The anticline's axis runs northwest to southeast, with Cambrian strata exposed in the core, followed by Ordovician, Silurian, Permian, Triassic, and Jurassic strata in succession. A series of thrust faults are also developed in the anticline's core area, often cutting through the Cambrian strata, and secondary folds are relatively well-developed within the anticline. Well 02 in the core of the anticline encountered Cambrian and Sinian strata with a total thickness of 3300m. The Cambrian Qiongzhusi Formation contains a 225m thick layer of black carbonaceous shale, and the upper part of the Gaotai Formation exhibits multiple gypsum layers in a cyclic pattern. Wells 03 and 01 are located on the southern slope of the anticline and at the syncline turning point, respectively. Both wells encountered Triassic, Permian, Silurian, and part of the Ordovician strata. The Lower Silurian Longmaxi Formation-Upper Ordovician Wufeng Formation, dominated by organic-rich shale and mudstone, is the main gas-producing layer for shale gas extraction. The surface fault data for the study area comes from local regional geological survey data. A total of 36 faults are identified, including 8 known reverse faults and 2 normal faults. A north-south trending compressional-shear fault at Yantou penetrates the hydraulically fractured zone, extending for 17km.

[0147] The shale gas field's producing formations are at relatively shallow elevations, ranging from 0 to 2600 m, with the main producing area located in the syncline region on the southern flank of the anticline. The pressure coefficient of the Longmaxi Formation in this area is mainly 1.2–2.0, indicating overpressure or abnormally high pressure. In-situ stress testing data shows that the biaxial stress difference in the anticline region is 21.4–22.3 MPa. According to well logging interpretation, the maximum horizontal principal stress in the Longmaxi Formation of well 01 is 57 MPa, and the minimum is 44.6 MPa. The measured direction of the current maximum horizontal principal stress in this area is 100°–155° north of east, obliquely intersecting the axis of the Changning anticline. Natural fractures are relatively well-developed, and their orientation is generally consistent with the direction of the maximum horizontal principal stress. During fracturing operations, the net formation pressure is much greater than the horizontal stress difference of 12.4 MPa, which can lead to the formation of complex fracture networks. Shale gas exploration in anticlines typically involves pressures of 56–66 MPa, while the formation fluid pressure in the Silurian Longmaxi Formation is mostly less than 55 MPa. This fluid pressure can significantly influence in-situ stress. Shale gas exploration in this anticline began in 2011, and large-scale shale fracturing operations have been conducted since 2014. Since earthquake records began, this anticline has consistently been a seismically active area, with earthquakes of magnitude 5.7 and 5.3 occurring on December 16, 2018, and January 3, 2019, respectively. Earthquakes with a focal depth greater than 3.0 primarily occurred between 5 and 15 km, a depth range precisely situated within the Foresinian basement of the anticline. In areas shallower than 2 km, earthquakes were mainly of magnitude less than 2.0.

[0148] The main steps include:

[0149] The area is divided into five zones, A through E (e.g., Figure 3 As shown in the figure, F1 to F6 represent 6 faults respectively, and their seismic hazard is evaluated, and a comprehensive seismic hazard evaluation map is drawn.

[0150] Area A: The study area is generally located within a tectonic compressional stress environment, with seismic activity in a region of decreasing activity rate (Z>0). Reverse faults are present in the area, but none are active faults and are located at a certain distance from shale gas fracturing platforms, without direct communication. Fractures are not well-developed. Earthquakes are infrequent in this area, but destructive earthquakes of magnitude 4.7 or higher have occurred historically. Therefore, the influencing factors for this area are set as follows: G13, G22, G32, G42, H12, H21, S12, S23, S32.

[0151] Area B: The study area is generally located within a tectonic compressional stress environment. Multiple reverse faults are present, but they are relatively small in scale and not considered active faults. Fractures are well-developed, and the faults are located within the envelope of the shale gas fracturing platform, with hydraulic fracturing directly communicating with the faults. Seismic activity is in an area of ​​increasing activity rate (Z<0). This area experiences relatively frequent seismic activity, with historical destructive earthquakes of magnitude 4.7 or higher. Therefore, the influencing factors for this area are set as follows: G13, G22, G31, G41, H11, H21, S11, S22, S32.

[0152] The influencing factors for zones C to E are determined by considering these nine influencing factors, and the resulting state combinations are as follows:

[0153] Area C: G13, G22, G31, G42, H11, H21, S11, S21, S32.

[0154] Area D: G13, G22, G32, G43, H13, H23, S12, S22, S32.

[0155] Area E: G13, G22, G31, G42, H12, H22, S11, S23, S32.

[0156] The evaluation results are shown in Table 2. The resulting zoning map of induced seismic hazard assessment for a shale gas extraction area in Sichuan Province is shown below. Figure 4 As shown.

[0157] Table 2 Results of Seismic Hazard Assessment in Shale Gas Extraction Areas

[0158]

[0159] Furthermore, this specification also provides a device for determining the seismic hazard induced by shale gas extraction based on fuzzy hierarchical analysis, the device comprising:

[0160] The influencing factor determination module is configured as follows: For a shale gas field to be exploited, based on the indicators of shale gas extraction-induced earthquakes, the corresponding situations are predicted and refined. Several index factors included in each indicator are obtained as influencing factors. The number of influencing factors is positively correlated with the number of levels included in the preset hazard assessment results, positively correlated with the maximum potential loss caused by the highest risk included in the hazard assessment results, and the number of influencing factors obtained when the shale gas field has a history of mining is greater than the number of influencing factors obtained when the shale gas field has no history of mining. The distance from the shale gas field to be exploited... The number of influencing factors obtained when there are other mining areas within a specified distance of the gas field is greater than the number of influencing factors obtained when there are no other mining areas within a specified distance of the gas field to be mined; the specified distance is positively correlated with the shale gas pressure stored in the gas field to be mined, positively correlated with the amount of shale gas stored in the gas field to be mined, and negatively correlated with the value of the difference between the first difference and the second difference, where the first difference is the difference between the maximum and minimum mining depths of the gas field to be mined, and the second difference is the difference between the average and minimum mining depths of the gas field to be mined;

[0161] The hazard prediction model construction module is configured to: use the influencing factors as the indicator layer, the indicators as the criterion layer, and obtain the seismic hazard degree evaluation results as the target layer to construct a hazard prediction model;

[0162] The weight vector determination module is configured to: determine the weight vector of the hazard prediction model based on fuzzy hierarchical analysis.

[0163] The fuzzy evaluation matrix construction module is configured to: establish a fuzzy evaluation matrix based on the correlation between the influencing factors obtained from expert experience and the risk level evaluation results;

[0164] The target evaluation result matrix determination module is configured to: weight the fuzzy evaluation matrix based on the weight vector to obtain the target evaluation result matrix, wherein the target evaluation result matrix represents the induced seismic hazard of the gas field to be exploited with reference to the influencing factors.

[0165] The device is capable of performing the methods in any of the foregoing embodiments and can achieve the same or similar technical effects, which will not be elaborated here.

[0166] Figure 5This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 5 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0167] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0168] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0169] The processor reads the corresponding computer program from non-volatile memory into memory and then runs it, forming a device for determining the seismic hazard induced by shale gas extraction based on fuzzy hierarchical analysis at the logical level. The processor executes the program stored in memory and specifically performs any of the aforementioned methods for determining the seismic hazard induced by fuzzy hierarchical analysis based on shale gas extraction.

[0170] The above is as stated in this application. Figure 1The fuzzy hierarchical analysis-based method for determining seismic hazard induced by shale gas extraction, as disclosed in the illustrated embodiments, can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0171] The electronic device can also perform Figure 1 A method for determining the seismic hazard induced by shale gas extraction based on fuzzy hierarchical analysis was developed and implemented. Figure 1 The functions of the embodiments shown are not described in detail here.

[0172] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, perform any of the aforementioned methods for determining the seismic hazard induced by shale gas extraction based on fuzzy hierarchical analysis.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0178] Memory may include non-persistent storage 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.

[0179] 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 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.

[0180] 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 a process, method, article, or apparatus. Without further limitation, 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 said element.

[0181] 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.

[0182] The above description is merely an embodiment of this application and is 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.

Claims

1. A method for determining the seismic hazard induced by shale gas extraction based on fuzzy hierarchical analysis, characterized in that, The method includes: For the gas field to be developed, based on the indicators of shale gas extraction-induced earthquakes, the corresponding situations are predicted and refined. Each indicator contains several index factors, which are then used as influencing factors. The number of these influencing factors is positively correlated with the number of risk levels included in the preset hazard assessment results, positively correlated with the maximum potential loss caused by the highest risk included in the hazard assessment results, and the number of influencing factors obtained when the gas field has a history of mining is greater than the number obtained when the gas field has no history of mining. [The last sentence appears to be incomplete and possibly refers to a distance from the gas field.] The number of influencing factors obtained when there are other mining areas within the specified range is greater than the number of influencing factors obtained when there are no other mining areas within the specified distance from the gas field to be mined; the specified distance is positively correlated with the shale gas pressure stored in the gas field to be mined, positively correlated with the amount of shale gas stored in the gas field to be mined, and negatively correlated with the value of the difference between the first difference and the second difference, where the first difference is the difference between the maximum and minimum mining depths of the gas field to be mined, and the second difference is the difference between the average mining depth and the minimum mining depth of the gas field to be mined; A hazard prediction model is constructed by using the aforementioned influencing factors as the indicator layer, the aforementioned indicators as the criterion layer, and the obtained seismic hazard assessment results as the target layer. The weight vector of the hazard prediction model is determined based on the fuzzy hierarchical analysis method. Based on the correlation between the influencing factors obtained from expert experience and the risk level assessment results, a fuzzy evaluation matrix is ​​established; The process of establishing the fuzzy evaluation matrix includes: A factor set U is established based on the aforementioned influencing factors, and an evaluation set V is established based on the aforementioned risk assessment results; Given the factor set U and the evaluation set V, we first evaluate each factor in the factor set U to determine the degree of membership of the evaluation object to each element in the factor set U. Then, we form a total fuzzy evaluation matrix R from the evaluation sets of n factors. ， In the formula: 0≤r ij ≤1, r ij Let represent the membership degree of the i-th factor to the j-th risk level; the membership degree is calculated using fuzzy statistics; Q is a correction coefficient, and the formula for calculating Q is: ， In the formula, It is the i-th fault. It is the maximum stress obtained from the survey of the i-th fault. It is the minimum stress obtained from the survey of the i-th fault. It is the average stress obtained from the survey of the i-th fault. The average displacement of the fault with the largest vertical displacement in historical earthquakes among all fault types. It refers to the number of faults involved in the gas field to be developed. It is a coefficient that is positively correlated with the ratio of free gas content to adsorbed gas content in the gas field to be exploited, and negatively correlated with the minimum exploitation depth of the gas field to be exploited. The fuzzy evaluation matrix is ​​weighted based on the weight vector to obtain the target evaluation result matrix, which represents the induced seismic hazard of the gas field to be exploited with reference to the influencing factors. When the seismic occurrence rate at the geographical location of the gas field to be developed is greater than a preset seismic occurrence threshold, the indicators include: the tectonic stress field environment of the gas field to be developed, the fault activity at the geographical location of the gas field to be developed, the fracture development of the gas field to be developed, the communication between the gas field to be developed and the fluid, the injection distance, the injection depth, the historical seismic background at the geographical location of the gas field to be developed, and the b-value; the b-value is obtained by analyzing existing seismic data of the area where the gas field to be developed is located, and reflects the regional medium strength and stress state; When the seismic occurrence rate at the geographical location of the gas field to be developed is not greater than a preset seismic occurrence threshold, the indicators include: the tectonic stress field environment of the gas field to be developed, the fault activity at the geographical location of the gas field to be developed, the fracture development of the gas field to be developed, the communication between the gas field to be developed and the fluid, the injection distance, and the injection depth.

2. The method as described in claim 1, characterized in that, The method further includes: When other mineral mining areas within the specified distance from the gas field to be exploited are gas resource mining areas, the number of influencing factors is greater than the number of influencing factors when other mineral mining areas within the specified distance from the gas field to be exploited are non-gas resource mining areas.

3. The method as described in claim 1, characterized in that, The method further includes: The risk assessment results include levels of: high risk and low risk; or, The risk assessment results include the following levels: high risk, medium risk, and low risk.

4. The method as described in claim 3, characterized in that, The method further includes: When other mineral mining areas within a specified distance from the gas field to be exploited are gas resource mining areas, the risk assessment results include the following levels: high risk, medium risk, and low risk.

5. The method as described in claim 1, characterized in that, The method further includes: The influencing factors are determined when the ratio of free gas content to adsorbed gas content in the gas field to be developed is not less than a ratio threshold; the ratio threshold is negatively correlated with the seismic occurrence rate of the geographical location of the gas field to be developed.

6. A device for determining the seismic hazard induced by shale gas extraction based on fuzzy hierarchical analysis, characterized in that, The apparatus used in the method for determining the seismic hazard induced by shale gas extraction based on fuzzy hierarchical analysis as described in any one of claims 1-5 includes: The influencing factor determination module is configured as follows: For a shale gas field to be exploited, based on the indicators of shale gas extraction-induced earthquakes, the corresponding situations are predicted and refined. Several index factors included in each indicator are obtained as influencing factors. The number of influencing factors is positively correlated with the number of levels included in the preset hazard assessment results, positively correlated with the maximum potential loss caused by the highest risk included in the hazard assessment results, and the number of influencing factors obtained when the shale gas field has a history of mining is greater than the number of influencing factors obtained when the shale gas field has no history of mining. The distance from the shale gas field to be exploited... The number of influencing factors obtained when there are other mining areas within a specified distance of the gas field is greater than the number of influencing factors obtained when there are no other mining areas within the specified distance of the gas field to be mined; the specified distance is positively correlated with the shale gas pressure stored in the gas field to be mined, positively correlated with the amount of shale gas stored in the gas field to be mined, and negatively correlated with the value of the difference between the first difference and the second difference, where the first difference is the difference between the maximum and minimum mining depths of the gas field to be mined, and the second difference is the difference between the average mining depth and the minimum mining depth of the gas field to be mined; The hazard prediction model construction module is configured to: use the influencing factors as the indicator layer, the indicators as the criterion layer, and obtain the seismic hazard degree evaluation results as the target layer to construct a hazard prediction model; The weight vector determination module is configured to: determine the weight vector of the hazard prediction model based on fuzzy hierarchical analysis. The fuzzy evaluation matrix construction module is configured to: establish a fuzzy evaluation matrix based on the correlation between the influencing factors obtained from expert experience and the risk level evaluation results; The target evaluation result matrix determination module is configured to: weight the fuzzy evaluation matrix based on the weight vector to obtain the target evaluation result matrix, wherein the target evaluation result matrix represents the induced seismic hazard of the gas field to be exploited with reference to the influencing factors.

7. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the method of any one of claims 1 to 5.

8. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the method of any one of claims 1 to 5.

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