Shale gas target optimization method and device based on analytic hierarchy process

Abstract

The invention provides a shale gas target optimization method and device based on an analytic hierarchy process, and the method comprises the steps: collecting the feature data of a shale gas target region, and carrying out the preprocessing of the feature data, and obtaining a plurality of feature data sets; for each feature data set, constructing a corresponding pairwise comparison matrix, and constructing a relative importance matrix between the feature data sets; calculating a corresponding feature vector, and determining a weight vector and a consistency index according to the feature vector; analyzing whether the matrix meets a preset requirement or not according to the consistency index, and optimizing the matrix which does not meet the preset requirement; determining an evaluation score of each feature data set according to the paired comparison matrix and the weight vector, and determining a comprehensive evaluation score corresponding to each layer section according to the evaluation score of the feature data set corresponding to each layer section and the weight vector corresponding to the relative importance matrix; and selecting a layer section with the highest comprehensive evaluation score as an optimal drilling target layer, and displaying the optimal drilling target layer.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration and development drilling engineering, and particularly to a method and device for optimizing shale gas target bodies based on the analytic hierarchy process. Background Art

[0002] This section aims to provide background or context for the embodiments of the present invention. The descriptions herein are not admitted to be prior art merely because they are included in this section.

[0003] In oil drilling engineering, the optimization of shale gas target bodies plays a decisive role in the success or failure of drilling operations. Traditional methods for optimizing shale gas target bodies have many limitations. For example, they only consider from a single dimension and focus on geological factors, making it difficult to comprehensively reflect the comprehensive situation in the actual drilling process, such as drilling factors and fracturing factors are not comprehensively considered. At the same time, due to the differences in the target layers of different oil and gas fields, it is difficult for traditional methods for optimizing shale gas target bodies to achieve definite quantitative indicators, resulting in a lack of a standard measurement method for target body optimization.

[0004] In summary, there is an urgent need for a technical solution that can overcome the above defects, improve the target body optimization method, and enhance the rationality of selection. Summary of the Invention

[0005] To solve the problems existing in the prior art, the present invention proposes a method and device for optimizing shale gas target bodies based on the analytic hierarchy process. By comprehensively analyzing the characteristic data of the target layer and quantitatively evaluating and comprehensively scoring the importance of the characteristics, a systematic evaluation and optimization of the shale gas target bodies are realized, thereby improving the efficiency, safety, economy, and overall benefits of drilling operations.

[0006] In the first aspect of the embodiments of the present invention, a method for optimizing shale gas target bodies based on the analytic hierarchy process is proposed. The method includes:

[0007] Collect the characteristic data of the shale gas target body area, and preprocess the characteristic data to obtain multiple characteristic data sets;

[0008] For each of the characteristic data sets, construct a corresponding pairwise comparison matrix, and construct a relative importance matrix between the characteristic data sets;

[0009] Calculate the corresponding eigenvectors according to the pairwise comparison matrix and the relative importance matrix, and determine the weight vector and consistency index according to the eigenvectors;

[0010] Analyze whether the matrix meets the preset requirements according to the consistency index, optimize the pairwise comparison matrix or the relative importance matrix that does not meet the preset requirements, and retain the pairwise comparison matrix or the relative importance matrix that meets the preset requirements;

[0011] Determine the evaluation scores of each feature dataset according to the pairwise comparison matrix and the weight vector that meet the preset requirements, and determine the comprehensive evaluation scores corresponding to each interval according to the evaluation scores of the feature datasets corresponding to each interval and the weight vector corresponding to the relative importance matrix;

[0012] Select the interval with the highest comprehensive evaluation score as the optimal drilling target layer, and display the optimal drilling target layer.

[0013] In the second aspect of the embodiments of the present invention, a shale gas target body optimization device based on the analytic hierarchy process is proposed. The device includes:

[0014] A data acquisition module, configured to acquire the characteristic data of the shale gas target area, and preprocess the characteristic data to obtain a plurality of feature datasets;

[0015] A matrix construction module, configured to respectively construct a corresponding pairwise comparison matrix for each of the feature datasets, and construct a relative importance matrix between the feature datasets;

[0016] A data calculation module, configured to respectively calculate the corresponding eigenvectors according to the pairwise comparison matrix and the relative importance matrix, and determine the weight vector and the consistency index according to the eigenvectors;

[0017] A data optimization module, configured to analyze whether the matrix meets the preset requirements according to the consistency index, optimize the pairwise comparison matrix or the relative importance matrix that does not meet the preset requirements, and retain the pairwise comparison matrix or the relative importance matrix that meets the preset requirements;

[0018] A comprehensive evaluation module, configured to determine the evaluation scores of each feature dataset according to the pairwise comparison matrix and the weight vector that meet the preset requirements, and determine the comprehensive evaluation scores corresponding to each interval according to the evaluation scores of the feature datasets corresponding to each interval and the weight vector corresponding to the relative importance matrix;

[0019] An interval optimization module, configured to select the interval with the highest comprehensive evaluation score as the optimal drilling target layer, and display the optimal drilling target layer.

[0020] In the third aspect of the embodiments of the present invention, a computer device is proposed, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the shale gas target body optimization method based on the analytic hierarchy process is implemented.

[0021] In the fourth aspect of the embodiments of the present invention, a computer-readable storage medium is proposed. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements a method for optimizing shale gas targets based on the analytic hierarchy process.

[0022] In the fifth aspect of the embodiments of the present invention, a computer program product is proposed. The computer program product includes a computer program, and when the computer program is executed by a processor, it implements a method for optimizing shale gas targets based on the analytic hierarchy process.

[0023] The method and device for optimizing shale gas targets based on the analytic hierarchy process proposed by the present invention have been improved in terms of comprehensiveness, flexibility, and reliability. In terms of comprehensiveness, the overall solution comprehensively considers multiple key indicators of different feature sets, avoiding the limitations of single-factor evaluation, making the target selection more scientific and reasonable, and improving the overall performance of drilling operations. In terms of flexibility, through the analytic hierarchy process, users can flexibly adjust the importance weights of different features according to actual situations, enhancing the adaptability and practicality of the method, and it can be applied to drilling scenarios in different oil and gas fields. In terms of reliability, the consistency test ensures the scientificity and rationality of the evaluation process, avoiding incorrect results caused by unreasonable setting of feature importance, and providing a reliable basis for decision-making. Description of the Drawings

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 It is a schematic flowchart of a method for optimizing shale gas targets based on the analytic hierarchy process according to an embodiment of the present invention.

[0026] Figure 2 It is a schematic diagram of the hierarchical structure for shale gas target selection according to an embodiment of the present invention.

[0027] Figure 3 It is a schematic diagram of the integrated pairwise comparison matrix of geological features according to an embodiment of the present invention.

[0028] Figure 4 It is a schematic diagram of the integrated pairwise comparison matrix of engineering features according to an embodiment of the present invention.

[0029] Figure 5 It is a schematic diagram of the integrated pairwise comparison matrix of geological features and engineering features according to an embodiment of the present invention.

[0030] Figure 6Schematic diagram of the consistency ratio test result of an embodiment of the present invention.

[0031] Figure 7 Schematic diagram of the visualization radar for shale gas target evaluation of an embodiment of the present invention.

[0032] Figure 8 Schematic diagram of the visualization bar chart for shale gas target evaluation of an embodiment of the present invention.

[0033] Figure 9 Schematic diagram of the architecture of the shale gas target optimization device based on the analytic hierarchy process of an embodiment of the present invention.

[0034] Figure 10 Schematic diagram of the structure of a computer device of an embodiment of the present invention. Detailed implementation manners

[0035] Hereinafter, the principles and spirit of the present invention will be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and then implement the present invention, rather than limiting the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to be able to fully convey the scope of the present disclosure to those skilled in the art.

[0036] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, a device, an equipment, a method or a computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0037] According to the embodiments of the present invention, a method and a device for optimizing shale gas targets based on the analytic hierarchy process are proposed, which relate to the technical field of drilling engineering in oil exploration and development. Specifically, the present invention is an innovative method for quantifying the contribution degrees of the attribute characteristics of each layer by the analytic hierarchy process, and then optimizing the shale gas targets. Through the analytic hierarchy process, users can flexibly adjust the importance weights of different characteristics according to the actual situation, enhancing the adaptability and practicability of the method, and it can be applied to the drilling scenarios of different oil and gas fields.

[0038] Hereinafter, with reference to several representative embodiments of the present invention, the principles and spirit of the present invention will be elaborated in detail.

[0039] Figure 1 Schematic diagram of the flow of the method for optimizing shale gas targets based on the analytic hierarchy process of an embodiment of the present invention. As Figure 1 shown, the method includes:

[0040] S101, Collect the characteristic data of the shale gas target area, and preprocess the characteristic data to obtain multiple characteristic data sets;

[0041] S102, For each of the characteristic data sets, construct a corresponding pairwise comparison matrix, and construct a relative importance matrix between the characteristic data sets;

[0042] S103, Calculate the corresponding eigenvectors according to the pairwise comparison matrix and the relative importance matrix respectively, and determine the weight vector and the consistency index according to the eigenvectors;

[0043] S104, Analyze whether the matrix meets the preset requirements according to the consistency index, optimize the pairwise comparison matrix or the relative importance matrix that does not meet the preset requirements, and retain the pairwise comparison matrix or the relative importance matrix that meets the preset requirements;

[0044] S105, Determine the evaluation scores of each characteristic data set according to the pairwise comparison matrix and the weight vector that meet the preset requirements, and determine the comprehensive evaluation scores corresponding to each layer according to the evaluation scores of the characteristic data sets corresponding to each layer and the weight vector corresponding to the relative importance matrix;

[0045] S106, Select the layer with the highest comprehensive evaluation score as the optimal drilling target layer, and display the optimal drilling target layer.

[0046] In order to explain the above shale gas target optimization method based on the analytic hierarchy process more clearly, the following will be described in detail in combination with each step.

[0047] In one embodiment, for S101, collect the characteristic data of the shale gas target area, and preprocess the characteristic data to obtain multiple characteristic data sets.

[0048] Specifically, the collected characteristic data at least includes geological characteristics, engineering characteristics and fracturing characteristics; among them, the geological characteristics at least include organic carbon data (TOC), porosity, gas content, brittle mineral content, gamma data (GR); the engineering characteristics at least include mechanical drilling rate, vibration data, wellbore stability data; the fracturing characteristics at least include in-situ stress magnitude, in-situ stress direction, bedding and interface characteristics.

[0049] In actual application scenarios, other characteristic data can also be collected according to the actual situation to construct multiple characteristic data sets. The specific characteristic data sets can be flexibly modified according to the differences of the target layers in different oilfields and the key points concerned by oil and gas explorers.

[0050] In one embodiment, for S102, for each of the characteristic data sets, construct a corresponding pairwise comparison matrix, and construct a relative importance matrix between the characteristic data sets.

[0051] Specifically, for each feature dataset, an n×n pairwise comparison matrix is constructed, and the elements in the matrix are a ij , where n represents the number of features in the feature dataset, and the element a ij in the matrix represents the importance degree of the i-th feature compared to the j-th feature, where i and j take values from 1 to n, and the element a ij is assigned a value;

[0052] A relative importance matrix between q×q feature datasets is constructed, and the elements in the matrix are c ij , where q represents the number of data in the feature dataset, and the element c ij in the matrix represents the importance degree of the i-th feature dataset compared to the j-th feature dataset, where i and j take values from 1 to q, and the element c ij is assigned a value.

[0053] In one embodiment, for S013, the corresponding eigenvectors are calculated according to the pairwise comparison matrix and the relative importance matrix, and the weight vector and the consistency index are determined according to the eigenvectors.

[0054] Specifically, for the pairwise comparison matrix corresponding to the feature dataset, the maximum eigenvalue and the corresponding eigenvector are calculated, and the eigenvector is normalized to obtain the weight vector corresponding to each feature in the feature dataset, and the sum of the weight vectors is 1;

[0055] The consistency index corresponding to the feature dataset is calculated according to the maximum eigenvalue, and the calculation method is:

[0056]

[0057] In the formula, CI represents the consistency index, λ max represents the maximum eigenvalue, and n represents the number of features in the feature dataset;

[0058] The consistency ratio is calculated according to the consistency index and the random consistency index;

[0059]

[0060] In the formula, CR represents the consistency ratio, CI represents the consistency index, and RI represents the random consistency index;

[0061] According to the relative importance matrix, the weight vector and the consistency ratio of the relative importance between feature datasets are determined.

[0062] In one embodiment, for S104, according to whether the consistency index analysis matrix meets the preset requirements, the pairwise comparison matrix or relative importance matrix that does not meet the preset requirements is optimized, and the pairwise comparison matrix or relative importance matrix that meets the preset requirements is retained.

[0063] For the pairwise comparison matrix or relative importance matrix that does not meet the preset requirements, after re-optimization, it is rejudged whether it meets the requirements until the preset requirements are met.

[0064] Specifically, the consistency ratio is compared with a preset value; if it is less than or equal to the preset value, the corresponding matrix is considered reasonable; if it is greater than the preset value, the corresponding matrix is considered unreasonable, and the user is prompted to check the matrix and optimize the matrix. After optimization, the consistency ratio is compared with the preset value again until it is less than or equal to the preset value and the preset requirements are met.

[0065] In one embodiment, for S105, according to the pairwise comparison matrix and weight vector that meet the preset requirements, the evaluation scores of each feature data set are determined, and according to the evaluation scores of the feature data sets corresponding to each layer segment and the weight vector corresponding to the relative importance matrix, the comprehensive evaluation scores corresponding to each layer segment are determined.

[0066] Specifically, the pairwise comparison matrix is normalized, and the evaluation score of the feature data set is calculated in combination with the weight vector. The calculation method is:

[0067]

[0068] In the formula, represents the evaluation score of the jth feature data set in the kth layer segment, ω i represents the weight vector corresponding to the ith feature, x i represents the eigenvalue of the ith feature in the normalized pairwise comparison matrix, and n is the number of features;

[0069] The evaluation scores of the feature data sets corresponding to each layer segment are added up to obtain the comprehensive evaluation scores corresponding to each layer segment. The calculation method is:

[0070]

[0071] In the formula, S k represents the comprehensive evaluation score corresponding to the kth layer segment, represents the evaluation score of the jth feature data set in the kth layer segment, represents the weight vector corresponding to the jth feature data set in the relative importance matrix of the kth layer segment, and q represents the number of feature data sets.

[0072] In one embodiment, for S106, the interval with the highest comprehensive evaluation score is selected as the optimal drilling target layer, and the optimal drilling target layer is displayed.

[0073] Specifically, the interval with the highest comprehensive evaluation score is selected as the optimal drilling target layer, and the evaluation results of different intervals are displayed in the form of images. Among them, the display methods include at least bar charts, radar charts or other visualization means.

[0074] The present invention will be described below with a specific embodiment, taking geological characteristics and engineering characteristics as the data basis for shale gas target optimization.

[0075] Combined with S101, data is collected and screened; among them, geological characteristics at least include: TOC, porosity, gas content, brittle mineral content, GR; engineering characteristics at least include mechanical drilling rate, vibration, and wellbore stability data.

[0076] Combined with S102, a pairwise comparison matrix is constructed; specifically, a pairwise comparison matrix for feature dataset 1, a pairwise comparison matrix for feature dataset 2,..., a pairwise comparison feature for feature dataset q, and a relative importance matrix between each feature dataset are constructed.

[0077] These matrices quantify and assign values to the relative importance of different features through expert experience, on-site data statistical analysis or other relevant information, reflecting the importance degree of different feature dataset factors in target evaluation.

[0078] For feature dataset 1, an n×n pairwise comparison matrix A is constructed G1 , where n represents the number of features in feature dataset 1, and the element a ij in the matrix represents the importance degree of the i-th feature compared to the j-th feature. Among them, a ii = 1, which means that the feature is equally important compared to itself. This matrix reflects the relative importance between features and is assigned values according to expert experience, on-site data statistical analysis or other relevant information.

[0079] For feature dataset 2, an m×m pairwise comparison matrix A is constructed G2 , where m represents the number of features in feature dataset 2, and the element b ij in the matrix represents the importance degree of the i-th feature compared to the j-th feature, and the values are assigned similarly.

[0080] Similarly, for the remaining q - 2 feature datasets, similar processing is performed.

[0081] A q×q relative importance matrix A between feature datasets is constructed GE , and the element c in the matrixij represents the importance degree of the \(i\)-th feature dataset compared to the \(j\)-th feature dataset, where \(i\) and \(j\) take values from 1 to \(q\), and assign values to the element \(c\). ij Among them, for the element \(c\). ii = 1. For example, if the element \(c\). 12 represents the importance degree of feature dataset 1 relative to feature dataset 2, then \(c\). 21 = 1 / \(c\). 12 .

[0082] Combined with S103, calculate the weight vector. For the matrix \(A\). G1 , calculate its maximum eigenvalue \(\lambda_{max}\). and its corresponding eigenvector \(\vec{\omega}\). Normalize the eigenvector \(\vec{\omega}\). to obtain the weight vector \(\vec{W}_1\) of feature dataset 1, such that \(\sum_{i = 1}^{q}W_{1i}=1\). At the same time, calculate the consistency index \(CI\). and calculate the consistency ratio \(CR\) according to the random consistency index \(RI\). G1 where \(CR=\frac{CI}{RI}\). G1 If \(CR\). ≤ 0.1, it can be considered that the consistency of the matrix is acceptable; otherwise, the matrix elements need to be adjusted again. G1

[0083] For the matrix \(A\). G2 , perform the same operation to obtain the weight vector \(\vec{W}_2\) of feature dataset 2 and the consistency ratio \(CR_2\). G2 . For the matrix \(A\). GE , calculate the weight vector \(\vec{W}\) of the relative importance between feature datasets and the consistency ratio \(CR\). GE .

[0084] Combined with S104, perform consistency test; ensure that \(CR_1\). G1 , \(CR_2\). G2 , …, \(CR_q\). Gn , \(CR\). GE do not exceed 0.1. If the consistency ratio is too high, prompt the user to check the rationality of the matrix to avoid deviation of the evaluation results caused by unreasonable setting of feature importance.

[0085] Combined with S105, calculate the comprehensive score; perform positive processing and normalization processing on the collected feature dataset data, and unify data of different magnitudes into a comparable range (such as between 0 and 1) for subsequent comprehensive evaluation calculation. Assume that the matrix of feature dataset 1 is \(X\). G1 , \(X\). G1 is an \(N\times n\) matrix, where \(N\) represents the number of segments, and the matrix of feature dataset 2 is \(X\). G2 , \(X\).​G2 is an N×m matrix, then the matrix of the normalized feature dataset 1 The matrix of the normalized feature dataset 2 Calculate the comprehensive evaluation score for each layer segment. For the k-th layer segment (i ≤ k ≤ N), the score of the feature dataset 1 The score of the feature dataset 2 is the weight vector corresponding to each feature, are the i-th eigenvalues of the feature dataset 1 and the feature dataset 2 of the k-th layer segment after normalization respectively. Similarly, calculate for the remaining feature datasets.

[0086] The final evaluation score where, is the weight vector corresponding to each feature dataset, is the evaluation score corresponding to each feature dataset.

[0087] Combined with S106, target body optimization, visual display; according to the calculated comprehensive evaluation score, find out the layer segment with the highest score as the optimal drilling target layer. In the actual application scenario, the evaluation results of different layer segments can be displayed in the form of images; for example, through bar charts, radar charts or other visualization means, visually present the evaluation scores of each layer segment, which is convenient for users to quickly understand the advantages and disadvantages of different layer segments and provide a clear basis for drilling decisions.

[0088] Combined with Figure 2 As shown, it is the hierarchical structure diagram for shale gas target body selection. Collect the feature set data of different target layer segments, identify missing values, error values, etc., and preprocess the data.

[0089] Construct pairwise comparison matrices A G1 、A G2 、…、A Gq and A GE , and calculate the corresponding weight vectors and the consistency ratio CR G1 、CR G2 、…、CR Gq 、CR GE .

[0090] Judge whether CR G1 、CR G2 、…、CR Gq 、CR GE is less than 0.1; if the requirement is not met, reconstruct the pairwise comparison matrix, check the rationality of the matrix, and avoid deviation of the evaluation results caused by unreasonable setting of feature importance.

[0091] If the consistency ratio meets the requirements, calculate the comprehensive scores of different intervals, and find the interval with the highest score as the shale gas target interval.

[0092] Suppose for a certain oilfield, we focus on the data of two feature sets G and E, and perform data inspection and preprocessing. The specific data table after processing is shown in Table 1:

[0093] Table 1 Feature values

[0094] Horizon C11 C12 C13 C14 C15 C16 C17 C18 C21 C22 C23 Layer1 8.76 7.17 6.56 62.51 238.89 1.33 5.24 6.03 14.76 0.31 0.62 Layer2 8.79 9.65 6.36 73.15 208.58 1.54 4.47 6.05 22.05 0.31 0.31 Layer3 5.60 5.12 8.89 64.36 239.51 1.47 4.06 7.08 12.76 0.15 0.75 Layer4 3.65 7.09 8.88 79.10 223.27 1.90 4.05 6.27 49.06 0.67 0.83 Layer5 7.49 5.51 4.08 63.84 227.39 1.88 7.06 7.34 18.10 0.58 0.40 Layer6 7.76 8.64 8.10 67.69 208.55 1.40 5.95 6.11 30.79 0.97 0.31 Layer7 8.86 5.34 7.62 77.95 236.88 1.99 6.67 5.70 31.73 0.41 0.76 Layer8 9.77 5.98 7.74 71.26 249.93 1.58 4.40 4.66 44.37 0.06 0.99 Layer9 9.36 9.88 7.59 71.11 224.13 1.04 7.29 6.72 29.36 0.21 0.72 Layer10 5.16 6.09 5.54 64.06 234.89 1.67 7.27 6.50 26.52 0.69 0.05

[0095] In the table, Layer is the horizon serial number, and C represents the feature value.

[0096] Construct pairwise comparison matrices A G 、A E and A GE , corresponding to Figure 3 、 Figure 4 、 Figure 5 respectively. Calculate the corresponding weight vectors and Calculate the consistency ratio CR G 、CR E 、CR GE , and the calculation results of the consistency ratio are shown in Figure 6 . It can be seen from the figure that all the tests pass.

[0097] Calculate the comprehensive scores of different intervals and draw relevant visualization images, referring to Figure 7 、 Figure 8 , which are the radar chart and the bar chart respectively. Layer represents the horizon. It can be seen from the figure that the target target is horizon 7, and its corresponding score is the highest.

[0098] The shale gas target optimization method based on the analytic hierarchy process proposed by the present invention makes improvements in terms of comprehensiveness, flexibility and reliability. In terms of comprehensiveness, the overall scheme comprehensively considers multiple key indicators of different feature sets, avoiding the limitations of single-factor evaluation, making the target selection more scientific and reasonable, and improving the overall performance of drilling operations. In terms of flexibility, through the analytic hierarchy process, users can flexibly adjust the importance weights of different features according to the actual situation, enhancing the adaptability and practicality of the method, and it can be applied to drilling scenarios in different oil and gas fields. In terms of reliability, the consistency test ensures the scientificity and rationality of the evaluation process, avoiding incorrect results caused by unreasonable setting of feature importance, and providing a reliable basis for decision-making.

[0099] It should be noted that although the operations of the method of the present invention are described in a specific order in the above embodiments and accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution.

[0100] After introducing the method of the exemplary embodiment of the present invention, next, reference is made to Figure 9 introduce the shale gas target body optimization device based on the analytic hierarchy process of the exemplary embodiment of the present invention.

[0101] The implementation of the shale gas target body optimization device based on the analytic hierarchy process can refer to the implementation of the above method, and the repeated parts will not be elaborated. The terms "module" or "unit" used hereinafter can be a combination of software and / or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0102] Based on the same inventive concept, the present invention also proposes a shale gas target body optimization device based on the analytic hierarchy process, as Figure 9 shown, the device includes:

[0103] A data acquisition module 910, configured to acquire characteristic data of the shale gas target body area, and preprocess the characteristic data to obtain a plurality of characteristic data sets;

[0104] A matrix construction module 920, configured to respectively construct a corresponding pairwise comparison matrix for each of the characteristic data sets, and construct a relative importance matrix between the characteristic data sets;

[0105] A data calculation module 930, configured to respectively calculate corresponding eigenvectors according to the pairwise comparison matrix and the relative importance matrix, and determine a weight vector and a consistency index according to the eigenvectors;

[0106] A data optimization module 940, configured to analyze whether the matrix meets a preset requirement according to the consistency index, optimize the pairwise comparison matrix or the relative importance matrix that does not meet the preset requirement, and retain the pairwise comparison matrix or the relative importance matrix that meets the preset requirement;

[0107] A comprehensive evaluation module 950, configured to determine an evaluation score of each characteristic data set according to the pairwise comparison matrix and the weight vector that meet the preset requirement, and determine a comprehensive evaluation score corresponding to each layer segment according to the evaluation score of the characteristic data set corresponding to each layer segment and the weight vector corresponding to the relative importance matrix;

[0108] The layer section optimization module 960 is configured to select the layer section with the highest comprehensive evaluation score as the optimal drilling target layer and display the optimal drilling target layer.

[0109] In one embodiment, the data acquisition module 910 acquires the characteristic data of the shale gas target area and preprocesses the characteristic data to obtain a plurality of characteristic data sets, including:

[0110] The collected characteristic data at least includes geological characteristics, engineering characteristics and fracturing characteristics; wherein, the geological characteristics at least include organic carbon data, porosity, gas content, brittle mineral content, gamma data; the engineering characteristics at least include mechanical drilling rate, vibration data, wellbore stability data; the fracturing characteristics at least include in-situ stress magnitude, in-situ stress direction, bedding and interface characteristics.

[0111] In one embodiment, for each of the characteristic data sets, the matrix construction module 920 constructs a corresponding pairwise comparison matrix and constructs a relative importance matrix between the characteristic data sets, including:

[0112] For each characteristic data set, an n×n pairwise comparison matrix is constructed, and the elements in the matrix are a ij , where n represents the number of characteristics of the characteristic data set, and the element a ij in the matrix represents the importance degree of the i-th characteristic compared to the j-th characteristic, and i and j take values from 1 to n, and the element a ij is assigned a value;

[0113] A q×q relative importance matrix between the characteristic data sets is constructed, and the elements in the matrix are c ij , where q represents the number of data of the characteristic data set, and the element c ij in the matrix represents the importance degree of the i-th characteristic data set compared to the j-th characteristic data set, and i and j take values from 1 to q, and the element c ij is assigned a value.

[0114] In one embodiment, the data calculation module 930 calculates the corresponding eigenvectors according to the pairwise comparison matrix and the relative importance matrix, and determines the weight vector and the consistency index according to the eigenvectors, including:

[0115] For the pairwise comparison matrix corresponding to the characteristic data set, calculate the maximum eigenvalue and the corresponding eigenvector, normalize the eigenvector to obtain the weight vector corresponding to each characteristic in the characteristic data set, and make the sum of the weight vectors equal to 1;

[0116] Calculate the consistency index corresponding to the characteristic data set according to the maximum eigenvalue, and the calculation method is:

[0117]

[0118] In the formula, CI represents the consistency index, λ max represents the maximum eigenvalue, and n represents the number of features in the feature dataset;

[0119] Calculate the consistency ratio based on the consistency index and the random consistency index;

[0120]

[0121] In the formula, CR represents the consistency ratio, CI represents the consistency index, and RI represents the random consistency index;

[0122] Determine the weight vector of the relative importance between feature datasets and the consistency ratio according to the relative importance matrix.

[0123] In one embodiment, the data optimization module 940 analyzes whether the matrix reaches the preset requirements according to the consistency index, optimizes the pairwise comparison matrix or the relative importance matrix that does not meet the preset requirements, and retains the pairwise comparison matrix or the relative importance matrix that meets the preset requirements, including:

[0124] Compare the consistency ratio with a preset value; if it is less than or equal to the preset value, the corresponding matrix is considered reasonable; if it is greater than the preset value, the corresponding matrix is considered unreasonable, prompt the user to check the matrix and optimize the matrix, and after optimization, compare the consistency ratio with the preset value again until it is less than or equal to the preset value and meets the preset requirements.

[0125] In one embodiment, the comprehensive evaluation module 950 determines the evaluation score of each feature dataset according to the pairwise comparison matrix and the weight vector that meet the preset requirements, and determines the comprehensive evaluation score corresponding to each segment according to the evaluation score of the feature dataset corresponding to each segment and the weight vector corresponding to the relative importance matrix, including:

[0126] Perform normalization processing on the pairwise comparison matrix, and calculate the evaluation score of the feature dataset in combination with the weight vector. The calculation method is:

[0127]

[0128] In the formula, represents the evaluation score of the jth feature dataset in the kth segment, ω i represents the weight vector corresponding to the ith feature, x i represents the eigenvalue of the ith feature in the normalized pairwise comparison matrix, and n is the number of features;

[0129] Add the evaluation scores of the feature data sets corresponding to each layer segment to obtain the comprehensive evaluation score corresponding to each layer segment. The calculation method is as follows:

[0130]

[0131] In the formula, S k represents the comprehensive evaluation score corresponding to the k-th layer segment, represents the evaluation score of the j-th feature data set in the k-th layer segment, represents the weight vector corresponding to the j-th feature data set in the k-th layer segment in the relative importance matrix, and q represents the number of feature data sets.

[0132] In one embodiment, the layer segment optimization module 960 selects the layer segment with the highest comprehensive evaluation score as the optimal drilling target layer and displays the optimal drilling target layer, including:

[0133] Select the layer segment with the highest comprehensive evaluation score as the optimal drilling target layer, and display the evaluation results of different layer segments in the form of images. Among them, the display method includes at least through bar charts, radar charts or other visualization means.

[0134] It should be noted that although several modules of the shale gas target optimization device based on the analytic hierarchy process are mentioned in the above detailed description, this division is only exemplary and not mandatory. In fact, according to the embodiments of the present invention, the features and functions of the two or more modules described above can be embodied in one module. Conversely, the features and functions of one module described above can be further divided and embodied by multiple modules.

[0135] Based on the foregoing inventive concept, as Figure 10 shown, the present invention also proposes a computer device 1000, including a memory 1010, a processor 1020, and a computer program 1030 stored on the memory 1010 and executable on the processor 1020. When the processor 1020 executes the computer program 1030, it implements the foregoing shale gas target optimization method based on the analytic hierarchy process.

[0136] Based on the foregoing inventive concept, the present invention proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the foregoing shale gas target optimization method based on the analytic hierarchy process.

[0137] Based on the foregoing inventive concept, the present invention proposes a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the shale gas target optimization method based on the analytic hierarchy process.

[0138] The shale gas target body optimization method and device based on the analytic hierarchy process proposed by the present invention have been improved in terms of comprehensiveness, flexibility, and reliability. In terms of comprehensiveness, the overall solution comprehensively considers multiple key indicators of different feature sets, avoiding the limitations of single-factor evaluation, making the target body selection more scientific and reasonable, and improving the overall performance of drilling operations. In terms of flexibility, through the analytic hierarchy process, users can flexibly adjust the importance weights of different features according to actual situations, enhancing the adaptability and practicality of the method, and it can be applied to drilling scenarios in different oil and gas fields. In terms of reliability, the consistency test ensures the scientificity and reasonableness of the evaluation process, avoiding incorrect results caused by unreasonable setting of feature importance, and providing a reliable basis for decision-making.

[0139] In the technical solution of this application, the acquisition, storage, use, processing, etc. of data all comply with the relevant regulations of laws and regulations.

[0140] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a device, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented 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.

[0141] The present invention is described with reference to the flowcharts and / or block diagrams of methods and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0142] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0143] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the process Figure 1 a process or processes and / or blocks Figure 1 steps for the functions specified in a block or blocks.

[0144] Finally, it should be noted that the above embodiments are only specific embodiments of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions recorded in the foregoing embodiments or easily think of changes, or equivalently replace some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A shale gas target optimization method based on hierarchical analysis method, characterized in that: The method includes: Collecting characteristic data of the shale gas target area, and preprocessing the characteristic data to obtain multiple characteristic data sets; For each of the feature data sets, a corresponding pairwise comparison matrix is ​​constructed respectively, and a relative importance matrix between the feature data sets is constructed; Calculating corresponding eigenvectors according to the pairwise comparison matrix and the relative importance matrix, and determining a weight vector and a consistency index according to the eigenvectors; Analyzing whether the matrix meets the preset requirements according to the consistency index, optimizing the pairwise comparison matrix or the relative importance matrix that does not meet the preset requirements, and retaining the pairwise comparison matrix or the relative importance matrix that meets the preset requirements; Determine the evaluation score of each feature data set according to the pairwise comparison matrix and the weight vector that meet the preset requirements, and determine the comprehensive evaluation score corresponding to each layer segment according to the evaluation score of the feature data set corresponding to each layer segment and the weight vector corresponding to the relative importance matrix; The layer section with the highest comprehensive evaluation score is selected as the optimal drilling target layer, and the optimal drilling target layer is displayed.

2. The shale gas target optimization method based on the analytic hierarchy process according to claim 1, characterized in that: The characteristic data of the shale gas target area are collected and preprocessed to obtain multiple characteristic data sets, including: The collected characteristic data include at least geological characteristics, engineering characteristics and fracturing characteristics; wherein the geological characteristics include at least organic carbon data, porosity, gas content, brittle mineral content, and gamma data; the engineering characteristics include at least mechanical drilling speed, vibration data, and wellbore stability data; the fracturing characteristics include at least the magnitude of geostress, geostress direction, bedding and interface characteristics.

3. The shale gas target optimization method based on the analytic hierarchy process according to claim 1, characterized in that: For each of the feature data sets, a corresponding pairwise comparison matrix is ​​constructed, and a relative importance matrix between the feature data sets is constructed, including: For each feature data set, construct an n×n pairwise comparison matrix, the elements in the matrix are a ij , where n represents the number of features in the feature data set, and the element a in the matrix ij Represents the importance of the i-th feature compared to the j-th feature. The values ​​of i and j range from 1 to n. ij Assign values; Construct a relative importance matrix between q×q feature data sets, where the elements in the matrix are c ij , where q represents the number of data in the feature data set and the element c in the matrix ij Represents the importance of the i-th feature data set compared to the j-th feature data set. i and j range from 1 to q. ij Assign a value.

4. The shale gas target optimization method based on the analytic hierarchy process according to claim 1, characterized in that: Calculating corresponding eigenvectors according to the pairwise comparison matrix and the relative importance matrix, and determining weight vectors and consistency indicators according to the eigenvectors, including: For the pairwise comparison matrix corresponding to the feature data set, calculate the maximum eigenvalue and the corresponding eigenvector, normalize the eigenvector, obtain the weight vector corresponding to each feature in the feature data set, and make the sum of the weight vectors equal to 1; The consistency index corresponding to the feature data set is calculated according to the maximum eigenvalue. The calculation method is: In the formula, CI represents the consistency index, λ max represents the maximum eigenvalue, and n represents the number of features in the feature data set; Calculate the consistency ratio based on the consistency index and random consistency index; In the formula, CR represents the consistency ratio, CI represents the consistency index, and RI represents the random consistency index; According to the relative importance matrix, the weight vector and consistency ratio of the relative importance between feature data sets are determined.

5. The shale gas target optimization method based on the analytic hierarchy process according to claim 4, characterized in that: According to whether the consistency index analysis matrix meets the preset requirements, the paired comparison matrix or the relative importance matrix that does not meet the preset requirements is optimized, and the paired comparison matrix or the relative importance matrix that meets the preset requirements is retained, including: The consistency ratio is compared with the preset value; if it is less than or equal to the preset value, the corresponding matrix is ​​considered reasonable; if it is greater than the preset value, the corresponding matrix is ​​considered unreasonable, and the user is prompted to check the matrix and optimize the matrix. After optimization, the consistency ratio is compared with the preset value again until it is less than or equal to the preset value and meets the preset requirements.

6. The shale gas target optimization method based on the analytic hierarchy process according to claim 1, characterized in that: Determine the evaluation score of each feature data set according to the pairwise comparison matrix and weight vector that meet the preset requirements, and determine the comprehensive evaluation score corresponding to each layer segment according to the evaluation score of the feature data set corresponding to each layer segment and the weight vector corresponding to the relative importance matrix, including: The pairwise comparison matrix is ​​normalized and combined with the weight vector to calculate the evaluation score of the feature data set. The calculation method is: In the formula, represents the evaluation score of the jth feature data set in the kth layer segment, ω i represents the weight vector corresponding to the i-th feature, x i represents the eigenvalue of the i-th feature in the normalized pairwise comparison matrix, and n is the number of features; The evaluation scores of the characteristic data sets corresponding to each layer segment are added together to obtain the comprehensive evaluation score corresponding to each layer segment. The calculation method is: In the formula, S k represents the comprehensive evaluation score corresponding to the kth layer segment, represents the evaluation score of the jth feature data set in the kth layer segment, represents the weight vector corresponding to the jth feature data set in the kth layer segment in the relative importance matrix, and q represents the number of feature data sets.

7. The shale gas target optimization method based on the analytic hierarchy process according to claim 1, characterized in that: The layer section with the highest comprehensive evaluation score is selected as the optimal drilling target layer, and the optimal drilling target layer is displayed, including: The layer section with the highest comprehensive evaluation score is selected as the optimal drilling target layer, and the evaluation results of different layer sections are displayed in the form of images, wherein the display method at least includes a bar chart, a radar chart or other visualization means.

8. A shale gas target optimization device based on hierarchical analysis method, characterized in that: The device includes: A data acquisition module is used to collect characteristic data of the shale gas target area and pre-process the characteristic data to obtain multiple characteristic data sets; A matrix construction module, used to construct a corresponding pairwise comparison matrix for each of the feature data sets, and to construct a relative importance matrix between the feature data sets; A data calculation module, used to calculate the corresponding eigenvectors according to the pairwise comparison matrix and the relative importance matrix, and determine the weight vector and the consistency index according to the eigenvectors; A data optimization module, used to analyze whether the matrix meets the preset requirements according to the consistency index, optimize the paired comparison matrix or relative importance matrix that does not meet the preset requirements, and retain the paired comparison matrix or relative importance matrix that meets the preset requirements; A comprehensive evaluation module, used to determine the evaluation score of each feature data set according to the paired comparison matrix and weight vector that meet the preset requirements, and determine the comprehensive evaluation score corresponding to each layer segment according to the evaluation score of the feature data set corresponding to each layer segment and the weight vector corresponding to the relative importance matrix; The layer section optimization module is used to select the layer section with the highest comprehensive evaluation score as the optimal drilling target layer, and display the optimal drilling target layer.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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