FAHP-based shale gas development well platinum target comprehensive optimization method

Abstract

The invention discloses an FAHP-based shale gas development well platinum target comprehensive optimization method, relates to the field of shale gas development geological evaluation, provides an integrated thought of combining geology, engineering and development dynamics, comprehensively considers multiple factors of geology and engineering influencing the development effect after shale gas fracturing, systematically analyzes the internal relation of each factor, and improves the development efficiency. According to the method, an AHP (Analytic Hierarchy Process) and an FCE (Fuzzy Compressive Evaluation) method are fused, the FAHP-based comprehensive optimization method for the platinum target of the shale gas development well is innovatively provided, optimization key parameters of the platinum target of the shale gas development well are quantitatively determined, the position of the optimal platinum target of the shale gas development well can be accurately determined, and the method for optimizing the platinum target of the shale gas development well is provided. The optimal shale layer drilling rate and resource utilization rate of the shale gas well are effectively improved, the production effect of the shale gas development well is guaranteed, and the recovery efficiency of the shale gas well is improved.

Description

Technical Field

[0001] The present invention relates to the field of geological evaluation for shale gas development, and particularly to a comprehensive optimization method for platinum targets of shale gas development wells based on FAHP. Background Art

[0002] The existing technology mainly selects the "platinum targets" of shale gas development wells by using geostatistical methods based on static geological parameters of shale gas. The static parameters include TOC content, gas content, porosity, thickness of Class I reservoirs, brittle mineral content, gas logging display value, and natural gamma, etc. The existing technology methods have insufficient consideration of engineering compressibility parameters, natural fractures, etc. of shale gas, as well as the summary understanding of the post-fracturing effect of shale gas and the development dynamic characteristics of shale gas wells. The determined "platinum targets" of shale gas development wells are not the optimal targets.

[0003] Shale gas practices at home and abroad have confirmed that low-porosity and low-permeability shale gas reservoirs generally have no natural production capacity and are a special type of gas reservoir that can only achieve industrial production capacity after large-scale volume engineering transformation. That is, only by meeting the geological-engineering "double sweet spots" and through large-scale transformation can the test production of shale gas wells be high and the cumulative gas production effect be good. The existing technology for optimizing the platinum targets of shale gas development wells selects based on a single static geological parameter of shale gas, without considering engineering compressibility and the production effect of shale gas wells. Moreover, the optimization result of the platinum targets of shale gas development wells is a qualitative understanding result, and there is uncertainty in the determined position of the platinum targets. Summary of the Invention

[0004] To solve the deficiencies of the existing technology, the present invention provides a comprehensive optimization method for platinum targets of shale gas development wells based on FAHP, which can accurately determine the optimal position of the "platinum targets" of shale gas development wells, effectively improve the drilling encounter rate of the optimal shale layers of shale gas wells and the resource utilization rate, ensure the production effect of shale gas development wells, and improve the recovery rate of shale gas wells.

[0005] The present invention is achieved through the following technical solutions:

[0006] A comprehensive optimization method for platinum targets of shale gas development wells based on FAHP, characterized by including the following steps:

[0007] S1. Obtain the factors affecting the optimization of the "platinum targets" of shale gas development wells, that is, evaluation parameters, and divide them into levels according to their internal relationships, namely the target level, criterion level, and sub-criterion level;

[0008] S2. According to the level division of the evaluation parameters in step S1, establish an evaluation model, and determine the evaluation set H = {h1, h2, ……, h i}; The evaluation set represents the set of evaluation results, and h i represents the evaluation grade for the criterion level.

[0009] S3. Use the analytic hierarchy process to process the factors in the criterion layer and sub-criterion layer, and obtain the weight vector W of each factor in the criterion layer n , the weight vector W of each factor in the sub-criterion layer ni and the weight vector W of the first-level index, where n represents the nth factor in the criterion layer, and ni represents the ith factor in the sub-criterion layer corresponding to the nth factor in the criterion layer;

[0010] S4. Adopt the expert decision-making level scoring and comprehensive consideration to obtain the evaluation matrix R between the evaluation index of the factor layer and the evaluation set i ;

[0011] S5. Use the mathematical fuzzy evaluation method to calculate the evaluation matrix R i and the corresponding weight vector to obtain the overall evaluation vector V. Substitute the overall evaluation vector V into the factor evaluation matrix R i and multiply it with the weight vector of each factor in the sub-criterion layer to obtain the final weight vector I of each evaluation parameter of the platinum target;

[0012] S6. Optimize the final weight vector I of each evaluation parameter.

[0013] Furthermore, in the step S5, the specific steps of obtaining the overall evaluation vector by using the mathematical fuzzy evaluation method are as follows:

[0014] S5.1. Calculate the evaluation matrix R i and the weight vector of each factor in the corresponding sub-criterion layer to obtain the fuzzy comprehensive evaluation vector M of the sub-criterion layer ni ,

[0015]

[0016] S5.2. Combine the obtained fuzzy comprehensive evaluation vector M of the sub-criterion layer ni to form the fuzzy relation matrix of the sub-criterion layer, denoted as M n ,

[0017]

[0018] S5.3. Multiply the fuzzy relation matrix of the sub-criterion layer by the weight vector of each factor in the corresponding criterion layer to obtain the corresponding criterion layer evaluation vector N n , that is, the primary fuzzy comprehensive evaluation result;

[0019] S5.4. Combine the primary fuzzy comprehensive evaluation result N n to form a multi-level fuzzy relation matrix M;

[0020]

[0021] S5.5. Multiply the multi-level fuzzy relation matrix M by the weight vector W of the first-level indicators to obtain the overall evaluation vector V.

[0022]

[0023] Further, in step S6, the specific steps for optimizing the evaluation parameters are as follows: when I < 0.025, it is a generally important parameter; when 0.025 < I < 0.05, it is an important parameter; when 0.05 < I, it is a key parameter. That is, 0.05 < I is the key parameter affecting the optimization of the platinum target for shale gas development wells.

[0024] Further, in step S3, calculate the weights W of the factors in the criterion layer n and the weights W of the factors in the sub-criterion layer ni The specific steps are as follows:

[0025] S3.1. Establish a hierarchical structure, divide the evaluation indicators into 3 layers, where the top layer is the target layer and the bottom layer is the factor layer;

[0026] S3.2. Construct a hierarchical model, and let experts judge the importance of the evaluation indicators in the criterion layer and the sub-criterion layer, and construct the criterion layer judgment matrix and the sub-criterion layer judgment matrix;

[0027] S3.3. Calculate the maximum eigenvalue λ of each judgment matrix max and the corresponding eigenvector of λ max and perform normalization processing;

[0028]

[0029] Among them, in formula (5), g ij is the normalization of the column of the judgment matrix elements; u ij is the matrix element;

[0030]

[0031] W = [w1 w2 …… w n , In formulas (6) and (7): is the normalization matrix; is the sum of the normalization elements; W is the matrix weight; w i is the element normalization matrix; where i represents the i-th factor among the n factors in the criterion layer, and ij represents the j-th factor in the sub-criterion layer corresponding to the i-th factor in the criterion layer;

[0032] S3.4. Conduct a consistency test on the judgment matrix. The specific steps are as follows:

[0033]

[0034] CI = (λ max - n) / (n - 1) (9);

[0035] CR = CI / RI (10);

[0036] In equations (8), (9), and (10): λ max is the maximum eigenvalue of the judgment matrix; CI is the one - time index of the judgment matrix; CR is the consistency ratio; n represents the order of the judgment matrix; RI represents the average random consistency index; when CR < 0.1, the judgment matrix is considered reasonable, that is, the judgment matrix passes the consistency test;

[0037] S3.5. If the judgment matrix passes the consistency test, the weight vector W n of the criterion layer and the weight vector W ni of the sub - criterion layer elements and the weight vector W of the first - level indicators are obtained through calculation.

[0038] Furthermore, if the consistency test fails, the steps of S3.2 - S3.4 need to be repeated, and the judgment matrix needs to be compared and adjusted until the consistency requirement is met.

[0039] Furthermore, in the step of S3.2, the pairwise comparison method and the 1 - 9 scale method are used to indicate the importance degree of factors to construct the judgment matrix.

[0040] Furthermore, in the step of S2, the evaluation levels are divided into unimportant, generally important, important, and very important.

[0041] Furthermore, the initial value of the evaluation level of unimportant is assigned as 0.1, the initial value of generally important is assigned as 0.2, the initial value of important is assigned as 0.3, and the initial value of very important is assigned as 0.4.

[0042] Furthermore, the target layer is the final result of the comprehensive evaluation by integrating fuzzy mathematics comprehensive evaluation and the analytic hierarchy process, that is, the optimization of the key parameters of the platinum target; the criterion layer is the first - level evaluation index of the analytic hierarchy process, including the static geological factors and engineering compressibility factors that affect the optimization of the platinum target in shale gas development wells; the sub - criterion layer is the second - level evaluation parameter of the analytic hierarchy process.

[0043] Furthermore, the parameter set of the criterion layer is expressed as U = {u1, u2,... u n}, representing the first - level evaluation index; the parameter set of the sub - criterion layer is expressed as Un = {u n1 , u n2 ,... u nm}, representing the second - level evaluation index, u nDenote the nth parameter of the criterion layer, which is determined by m parameters in the sub-criterion layer, u nm Denote the mth parameter in the sub-criterion layer corresponding to the nth parameter in the criterion layer.

[0044] The beneficial effects of the present invention are as follows:

[0045] 1. The analytic hierarchy process introduced in the present invention can systematize and hierarchize numerous complex factors, and the obtained weight results are more objective.

[0046] 2. The comprehensive evaluation method of fuzzy mathematics introduced in the present invention can effectively quantify qualitative problems.

[0047] 3. The method described in the present invention is simple, easy to understand, easy to start with, easy to operate, and convenient for application.

[0048] Therefore, the present invention combines the "integration" idea of geology, engineering, and the development effect of shale gas wells, comprehensively considers the geological and engineering factors affecting the development effect after shale gas fracturing, combines the analytic hierarchy process and the fuzzy mathematics evaluation method, and innovatively proposes a comprehensive optimization method for platinum target bodies of shale gas development wells based on FAHP, quantitatively determines the key parameters for optimizing the "platinum target body" of shale gas development wells, can accurately determine the optimal position of the "platinum target body" of shale gas development wells, effectively improve the drilling encounter rate of the optimal shale layer and the resource utilization rate of shale gas wells, ensure the production effect of shale gas development wells and improve the recovery rate of shale gas wells, and is particularly suitable for determining the key parameters for optimizing the "platinum target body" of shale gas development wells. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Is the hierarchical structure of evaluation indicators;

[0050] Figure 2 Is the comprehensive columnar diagram of parameters of the Wufeng-Longmaxi Formation shale gas potential layer in a certain block in southern Sichuan;

[0051] Figure 3 Comprehensive optimization diagram of platinum target bodies of shale gas potential layers in a certain block in southern Sichuan. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0053] Embodiment 1

[0054] A comprehensive optimization method for platinum target bodies of shale gas development wells based on FAHP, comprising the following steps:

[0055] S1. Obtain the factors affecting the optimization of the "platinum target body" for shale gas development wells, that is, evaluation parameters, and divide the levels according to the internal relationships of each factor, namely the target layer, the criterion layer, and the sub-criterion layer;

[0056] S2. According to the level division of the evaluation parameters in step S1, establish an evaluation model, and determine the evaluation set H = {h1, h2, ……, h i}; The evaluation set represents the set of evaluation results, and h i represents the evaluation grade for the criterion layer.

[0057] S3. Use the analytic hierarchy process to process the factors in the criterion layer and the sub-criterion layer to obtain the weight vector W n of each factor in the criterion layer, the weight vector W ni of each factor in the sub-criterion layer, and the weight vector W of the first-level index, where n represents the nth factor in the criterion layer, and ni represents the ith factor in the sub-criterion layer corresponding to the nth factor in the criterion layer;

[0058] S4. Adopt the comprehensive consideration of the expert decision-making level scores to obtain the evaluation matrix R i between the evaluation index of the factor layer and the evaluation set;

[0059] S5. Use the mathematical fuzzy evaluation method to calculate the evaluation matrix R i with the corresponding weight vector to obtain the overall evaluation vector V, and substitute the overall evaluation vector V into the factor evaluation matrix R i and multiply it by the weight vector of each factor in the sub-criterion layer to obtain the final weight vector I of each evaluation parameter of the platinum target body;

[0060] In this embodiment, the parameter set of the criterion layer is represented as U = {u1, u2, …… u n}, representing the first-level evaluation index; the parameter set of the sub-criterion layer is represented as Un = {u n1 , u n2 , …… u nm}, representing the second-level evaluation index, u n represents the nth parameter of the criterion layer, which is determined by m parameters in the sub-criterion layer, and u nm represents the mth parameter in the sub-criterion layer corresponding to the nth parameter in the criterion layer.

[0061] Embodiment 2

[0062] This embodiment further elaborates and supplements the implementation manner of the present invention on the basis of Embodiment 1.

[0063] In step S5, the specific steps for obtaining the overall evaluation vector by using the mathematical fuzzy evaluation method are as follows:

[0064] S5.1. Calculate the evaluation matrix \(R\) i and the weight vectors of each factor in the corresponding sub - criterion layer to obtain the fuzzy comprehensive evaluation vector \(M\) of the sub - criterion layer ni ,

[0065]

[0066] S5.2. Combine the obtained fuzzy comprehensive evaluation vector \(M\) of the sub - criterion layer ni to form the fuzzy relation matrix of the sub - criterion layer, denoted as \(M\) n ,

[0067]

[0068] S5.3. Multiply the fuzzy relation matrix of the sub - criterion layer by the weight vectors of each factor in the corresponding criterion layer to obtain the corresponding evaluation vector \(N\) of the criterion layer n , that is, the primary fuzzy comprehensive evaluation result;

[0069] S5.4. Combine the primary fuzzy comprehensive evaluation result \(N\) n to form a multi - level fuzzy relation matrix \(M\);

[0070]

[0071] S5.5. Multiply the multi - level fuzzy relation matrix \(M\) by the weight vector \(W\) of the first - level index to obtain the overall evaluation vector \(V\),

[0072]

[0073] As an implementation manner of this embodiment, in step S6, the specific steps for optimizing the evaluation parameters are as follows: when \(I < 0.025\), it is a generally important parameter; when \(0.025 < I < 0.05\), it is an important parameter; when \(0.05 < I\), it is a key parameter. That is, \(0.05 < I\) is the key parameter affecting the optimization of the platinum target for shale gas development wells.

[0074] Example 3

[0075] This embodiment further elaborates and supplements the implementation manner of the present invention on the basis of Example 1 or Example 2.

[0076] In step S3, to obtain the weight \(W\) of each factor in the criterion layer n and the weight \(W\) of each factor in the sub - criterion layer ni The specific steps are as follows:

[0077] S3.1. Establish a hierarchical structure, divide the evaluation indicators into 3 layers, where the top layer is the target layer and the bottom layer is the element layer;

[0078] S3.2. Construct a hierarchical model, where experts judge the importance of evaluation indicators at the criterion layer and sub-criterion layer, and construct a criterion layer judgment matrix and a sub-criterion layer judgment matrix;

[0079] S3.3. Calculate the maximum eigenvalue λ of each judgment matrix max and the corresponding eigenvector of λ max and perform normalization processing;

[0080]

[0081] Among them, in formula (5), g ij is the normalization of the column of the judgment matrix element; u ij is the matrix element;

[0082]

[0083]

[0084] In formulas (6) and (7): is the normalized matrix; is the sum of normalized elements; W is the matrix weight; w i is the element normalization matrix; where i represents the i-th factor among n criterion layer factors, and ij represents the j-th factor in the sub-criterion layer corresponding to the i-th criterion layer factor;

[0085] S3.4. Conduct a consistency test on the judgment matrix, and the specific steps are as follows:

[0086]

[0087] CI = (λ max - n) / (n - 1) (9);

[0088] CR = CI / RI (10);

[0089] In formulas (8), (9), and (10): λ max is the maximum eigenvalue of the judgment matrix; CI is the judgment matrix single - time index; CR is the consistency ratio; n represents the order of the judgment matrix; RI represents the average random consistency index; when CR < 0.1, it is considered that the judgment matrix is reasonable, that is, the judgment matrix passes the consistency test;

[0090] Table 1 AHP Random Index

[0091] n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58 1.59

[0092] S3.5. If the judgment matrix passes the consistency test, calculate the weight vector W of each factor in the criterion layer through calculation nand the weight vector W of each factor in the sub-criterion layer ni , combine the weight vectors W of each factor in the criterion layer n to obtain the weight vector W of the first-level index.

[0093] As an implementation manner of this embodiment, if the consistency test fails, the steps of S3.2 to S3.4 need to be repeated, and the judgment matrix needs to be compared and adjusted until the consistency requirement is met.

[0094] Embodiment 4

[0095] This embodiment further elaborates and supplements the implementation manner of the present invention on the basis of Embodiment 1, Embodiment 2 or Embodiment 3.

[0096] As another implementation manner of this embodiment, in the step S3.2, the pairwise comparison method and the 1-9 point scale method are used to mark the importance degree of the factors, and a judgment matrix is constructed.

[0097] Table 2 Saaty's 9-point scale method

[0098] <![CDATA[Scale value u ij = u i / u j > meaning 1 <![CDATA[u i is equally important as u j > 3 <![CDATA[u i associated with u j slightly important]]> 5 <![CDATA[u i is more important than u j significantly]]> 7 <![CDATA[u i is more important than u j significantly]]> 9 <![CDATA[u i is more important than u j extremely important]]> 2，4，6，8 respectively represent the median values of adjacent judgments 1 - 3, 3 - 5, 5 - 7, 7 - 9 reciprocal <![CDATA[u i associated with u j significantly important]]>

[0099] As another implementation manner of this embodiment, in the step S2, the evaluation levels are divided into unimportant, generally important, important, and very important.

[0100] As another implementation manner of this embodiment, the initial value assigned to the evaluation level of unimportant is 0.1, the initial value assigned to the evaluation level of generally important is 0.2, the initial value assigned to the evaluation level of important is 0.3, and the initial value assigned to the evaluation level of important is 0.4.

[0101] Embodiment 5

[0102] This embodiment further elaborates and supplements the method steps of the above embodiments through an example.

[0103] Taking the platinum target body optimization of the Wufeng-Longmaxi Formation shale gas development wells in a certain block in southern Sichuan as an example. The 4-meter box of shale gas in the established production area of the Wufeng-Longmaxi Formation shale gas in a certain block in southern Sichuan is the Wufeng-Long 1 1 sub-layer. Combining the thickness of the Long 1 1 sub-layer and the height of the hydraulic fracture network spread (the hydraulic fracture height is 10m - 15m), and the static parameters of the shale reservoir from the Long 1 2 to the Long 1 4C sub-layer, it is considered that the Long 1 3 , Long 1 4A , Long 1 4B sub-layers are potential development layers. As shown in Table 3, according to the geological parameter sweet spots, the 2m platinum target body section is optimized to be 3521m - 3523m, as Figure 2 shown. However,Figure 2 The 2m platinum target section selected as the preferred option in

[0104] Table 3 Preferred Parameter Table for the Wufeng-Longmaxi Formation Shale Gas Potential Layers in a Certain Block in Southern Sichuan

[0105]

[0106] 1. Determine evaluation parameters and establish an evaluation model

[0107] Based on the analysis of the post-fracturing effects of more than 200 shale gas development wells in a certain block in southern Sichuan, the results show that there are 12 main geological-engineering factors affecting the post-fracturing effects of shale gas development wells. The main static geological parameters are 7: clay mineral content, TOC content, porosity, gas content, thickness of Class I reservoirs, peak value of gas logging display, and reserve abundance. The main engineering compressibility parameters are 5: brittle mineral content, Poisson's ratio, Young's modulus, principal stress difference (difference between the maximum horizontal principal stress and the minimum principal stress), and degree of natural fracture development. The established evaluation model is shown in Table 4. And an evaluation set V = {v1, v2, v3, v4} = {unimportant, of general importance, important, very important} is established. The initial value of 0.1 is assigned to unimportant, 0.2 to of general importance, 0.3 to important, and 0.4 to very important.

[0108] Table 4 Hierarchical Model of Preferred Parameters for Platinum Targets in the Wufeng-Longmaxi Formation Shale Gas in a Certain Block in Southern Sichuan

[0109]

[0110] 2. Calculate the weight vector

[0111] The analytic hierarchy process is used for weight calculation. The judgment matrices at all levels are constructed. Among them, the judgment matrix A at the criterion layer, the judgment matrix B for static geological parameters, and the judgment matrix C for engineering compressibility parameters are as follows:

[0112]

[0113] According to the judgment matrix A, judgment matrix B, and judgment matrix C, the eigenvector W ni , and the maximum eigenvalue λ max of the judgment matrix B and judgment matrix C, and the corresponding maximum eigenvector are calculated, and consistency detection is carried out.

[0114] W maxB = (0.490, 1.268, 0.601, 1.178, 0.783, 0.960, 1.890), λ max is 7.280, and CR is 0.020; W maxc=(2.330, 1.068, 1.380, 0.397, 0.400), λ max is 5.706, and CR is 0.102. The CR values of judgment matrix B and judgment matrix C are less than 0.1, indicating that the judgment matrices are reasonable and the weight vectors can be further calculated.

[0115] Calculate the weight vector of judgment matrix B: W niB =(0.068, 0.174, 0.084, 0.166, 0.109, 0.136, 0.263), and the weight vector of judgment matrix C: W nic =(0.413, 0.191, 0.242, 0.076, 0.078).

[0116] 3. Establish the factor evaluation matrix

[0117] The evaluation matrix is comprehensively considered by using the expert decision-making method for grading. The evaluation matrix R between the evaluation indicators of the factor layer and the evaluation set is obtained i , that is, the membership matrix, as shown in Table 5.

[0118] Table 5 Static geology and engineering compressibility evaluation form

[0119]

[0120] 4. Fuzzy comprehensive evaluation

[0121] Multiply the evaluation matrix R i of the factor layer by the corresponding weight W ni of the sub-criterion layer to obtain the fuzzy comprehensive evaluation vector M ni of each secondary evaluation index, that is, the sub-criterion layer. M niB =(0.068, 0.087, 0.025, 0.083, 0.109, 0.054, 0.184), M niC =(0.124, 0.134, 0.121, 0.069, 0.047).

[0122] Combine the obtained fuzzy evaluation vectors of the sub-criterion layer to form the fuzzy relation matrix of the sub-criterion layer, denoted as M n :

[0123]

[0124]

[0125] Multiply the fuzzy relation matrix of the sub-criterion layer by its corresponding weight vector of the criterion layer to obtain the evaluation vector N n of each factor in the criterion layer. N B =(0.040, 0.120, 0.275, 0.176), NC =(0.049, 0.162, 0.176, 0.107).

[0126] According to the principle of maximum membership, for N B the maximum value is 0.275, and the corresponding evaluation interval is important. For N c the maximum value is 0.176, and the corresponding evaluation interval is important.

[0127] 5. Multilevel fuzzy comprehensive evaluation

[0128] Based on the results of the primary fuzzy comprehensive evaluation, for N nB and N nc , a preliminary evaluation was carried out on the geological static parameters and engineering compressibility parameters. The results show that both the geological static parameters and engineering compressibility parameters are important parameters for the optimization of shale gas platinum targets. Combining the primary evaluation results, a multilevel fuzzy relation matrix M is constructed:

[0129]

[0130] The weight of the criterion layer is W n =(0.667, 0.333). Multiplying the weight vector by the multilevel fuzzy relation matrix M gives the overall evaluation vector V.

[0131]

[0132] Among them, the weight of unimportant is 0.043, the weight of generally important is 0.134, the weight of important is 0.242, and the weight of very important is 0.153.

[0133] 6. Optimization of evaluation parameters

[0134] Substitute the overall evaluation vector V into the factor evaluation matrix R i , R i and multiply it by the corresponding sub-criterion layer weight vector W ni to obtain the final weight vector I of the platinum target evaluation parameters.

[0135]

[0136]

[0137] When I < 0.025, it is a generally important parameter; when 0.025 < I < 0.05, it is an important parameter; when 0.05 < I, it is a key parameter. According to the value of the final weight vector I, 7 key parameters affecting the optimization of shale gas platinum targets in the Long 1 sub-member of the Wufeng-Longmaxi Formation in a certain block in southern Sichuan are selected, as shown in Table 6. Among them, the static geological key parameters are TOC content, gas content, thickness of Class I reservoirs, and reserve abundance, and the key parameters beneficial to engineering compressibility are brittle mineral content, Poisson's ratio, and Young's modulus.

[0138] Based on the key parameter characteristics of the platinum targets in shale gas development wells, the potential development layer Longyi 1 of the Wufeng-Longmaxi Formation in the Weiyuan block was finally obtained 3 、Longyi 1 4A 、Longyi 1 4B The 2m platinum target positions of the small layers with geological-engineering "double sweet spots" are: 3519m - 3521m. Compared with the original geological sweet spot, it has been raised by 2m, as Figure 3 shown

[0139] Table 6 Optimal evaluation results of platinum targets in the Wufeng-Longmaxi Formation shale gas in a certain block in southern Sichuan

[0140]

Claims

1. A comprehensive optimization method for platinum targets in shale gas development wells based on FAHP, characterized in that It includes the following steps: S1. Obtain the factors affecting the optimization of the "platinum target" for shale gas development wells, i.e., evaluation parameters, and divide the levels according to the internal relationships of each factor, namely the target layer, criterion layer, and sub-criterion layer; S2. Establish an evaluation model according to the hierarchical division of evaluation parameters in step S1, and determine the evaluation set H = {h1, h2, ……, h i}; The evaluation set represents the set of evaluation results, and h i represents the evaluation level for the criterion layer. S3. Use the analytic hierarchy process to process the factors in the criterion layer and sub-criterion layer, and obtain the weight vector \(W\) of each factor in the criterion layer. n , the weight vector \(W\) of each factor in the sub-criterion layer ni and the weight vector \(W\) of the first-level index, where \(n\) represents the \(n\)th factor in the criterion layer, and \(n_i\) represents the \(i\)th factor in the sub-criterion layer corresponding to the \(n\)th factor in the criterion layer. S4. Through comprehensive consideration using expert decision-making level scoring, the evaluation matrix R between the evaluation indexes of the factor layer and the evaluation set is obtained i ; S5. Use the mathematical fuzzy evaluation method to calculate the evaluation matrix R i and the corresponding weight vector to obtain the overall evaluation vector V. Substitute the overall evaluation vector V into the factor evaluation matrix R i and multiply it with the weight vectors of each factor in the sub-criterion layer to obtain the final weight vector I of each evaluation parameter of the platinum target S6. Optimize the final weight vector I of each evaluation parameter.

2. The comprehensive optimization method of platinum targets for shale gas development wells based on FAHP according to claim 1, wherein, In the S5 step, the specific steps to obtain the overall evaluation vector using the mathematical fuzzy evaluation method are as follows: S5.

1. Calculate the evaluation matrix R i and the weight vectors of each factor in the corresponding sub-criterion layer to obtain the fuzzy comprehensive evaluation vector M ni , S5.

2. Combine the obtained fuzzy comprehensive evaluation vectors M of the sub-criterion layer ni to form a fuzzy relation matrix of the sub-criterion layer, denoted as M n , S5.

3. Multiply the fuzzy relation matrix of the sub-criterion layer by the weight vectors of the factors at the corresponding criterion layer to obtain the corresponding evaluation vector N of the criterion layer n , which is the primary fuzzy comprehensive evaluation result S5.

4. Combine the primary fuzzy comprehensive evaluation result N n to form a multi-level fuzzy relation matrix M; S5.

5. Multiply the multi-level fuzzy relation matrix M by the first-level index weight vector W to obtain the overall evaluation vector V.

3. A comprehensive optimization method for platinum targets in shale gas development wells based on FAHP according to claim 1, characterized in that In the S6 step, the specific steps for evaluating parameter optimization are as follows: when I < 0.025, it is a generally important parameter; when 0.025 < I < 0.05, it is an important parameter; when 0.05 < I, it is a key parameter. That is, 0.05 < I is the key parameter affecting the optimization of the platinum target for shale gas development wells.

4. A comprehensive optimization method for platinum targets of shale gas development wells based on FAHP according to claim 1, characterized in that, In the step S3, calculate the weights \(W\) of the factors in the criterion layer n and the weights \(W\) of the factors in the sub-criterion layer ni The specific steps are as follows: S3.

1. Establish a hierarchical structure, divide the evaluation indicators into 3 levels, where the top layer is the target layer and the bottom layer is the factor layer; S3.

2. Construct a hierarchical model, and let experts judge the importance of the evaluation indicators in the criterion layer and sub-criterion layer, and construct the criterion layer judgment matrix and sub-criterion layer judgment matrix; S3.

3. Calculate the maximum eigenvalue λ of each judgment matrix max and the corresponding eigenvector of λ max , and perform normalization processing; In formula (5), g ij is the normalization of the column of the judgment matrix elements; u ij is the matrix element; In formulas (6) and (7): is the normalization matrix; is the sum of normalized elements; W is the matrix weight; w i is the element normalization matrix; where i represents the i-th factor among the n criterion-level factors, and ij represents the j-th factor in the sub-criterion level corresponding to the i-th criterion-level factor; S3.

4. Conduct a consistency test on the judgment matrix. The specific steps are as follows: CI = (λ max - n) / (n - 1) (9); CR = CI / RI (10); In equations (8), (9), and (10): λ max is the maximum eigenvalue of the judgment matrix; CI is the one-time index of the judgment matrix; CR is the consistency ratio; n represents the order of the judgment matrix; RI represents the average random consistency index; when CR < 0.1, it is considered that the judgment matrix is reasonable, that is, the judgment matrix passes the consistency test; S3.

5. If the judgment matrix passes the consistency test, the weight vector W of the criterion layer is obtained by calculation n and the weight vector W of the sub-criterion layer elements ni and the weight vector W of the first-level indicators.

5. A comprehensive optimization method for platinum targets of shale gas development wells based on FAHP according to claim 4, characterized in that, If the consistency test fails, the steps of S3.2 - S3.4 need to be repeated, and the judgment matrix needs to be compared and adjusted until the consistency requirement is met.

6. A comprehensive optimization method for platinum targets in shale gas development wells based on FAHP according to claim 4, characterized in that In the S3.2 step, the importance of factors is marked using the pairwise comparison method and the 1 - 9 point scale method to construct the judgment matrix.

7. A comprehensive optimization method for platinum targets of shale gas development wells based on FAHP according to claim 1, characterized in that In the S2 step, the evaluation levels are divided into unimportant, generally important, important, and very important.

8. A comprehensive optimization method for platinum targets in shale gas development wells based on FAHP according to claim 7, characterized in that, The initial value assigned to the unimportant evaluation level is 0.1, the initial value assigned to the generally important level is 0.2, the initial value assigned to the important level is 0.3, and the initial value assigned to the very important level is 0.

4.

9. A comprehensive optimization method for platinum targets of shale gas development wells based on FAHP according to any one of claims 1-8, characterized in that The target layer is the final result of the comprehensive evaluation by integrating fuzzy mathematics comprehensive evaluation and the analytic hierarchy process, that is, the optimization of the key parameters of the platinum target; the criterion layer is the first-level evaluation index of the analytic hierarchy process, including the static geological factors and engineering compressibility factors affecting the optimization of the platinum target for shale gas development wells; the sub-criterion layer is the second-level evaluation parameter of the analytic hierarchy process.

10. A comprehensive optimization method for platinum targets in shale gas development wells based on FAHP according to claim 9, characterized in that, The parameter set of the criterion layer is expressed as U = {u1, u2,..., u n}, representing the first-level evaluation indicators; the parameter set of the sub-criterion layer is expressed as Un = {u n1 , u n2 ,..., u nm}, representing the second-level evaluation indicators, where u n represents the nth parameter of the criterion layer, which is determined by m parameters in the sub-criterion layer, and u nm represents the mth parameter in the sub-criterion layer corresponding to the nth parameter in the criterion layer.

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