Coal bed gas development target region optimization method based on fuzzy comprehensive evaluation

Abstract

The invention relates to the field of coal bed gas development, and discloses a coal bed gas development target region optimization method based on fuzzy comprehensive evaluation, which comprises the following steps: determining an evaluation factor score set and an evaluation factor weight set; calculating the membership degree of each evaluation factor, and determining a fuzzy evaluation matrix of the to-be-evaluated block; calculating a comprehensive evaluation score, and obtaining overall order values of different to-be-evaluated blocks; sequentially screening out n evaluation factors, and repeating the steps to analyze local order values of different blocks to be processed under the current evaluation factor combination; and calculating the order difference, and under the condition that the order difference of one to-be-evaluated block exceeds a difference threshold value, re-determining a new overall order value based on the original overall order value and the local order value, and screening the target region. According to the method, the sorting result tends to be real under the condition that the evaluation factor score possibly deviates from the real value too much, and the condition that the sorting result error is large due to the fact that the evaluation factor score possibly deviates from the real value is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of coalbed methane development, and in particular to a method for optimizing coalbed methane development target areas based on fuzzy comprehensive evaluation. Background Art

[0002] Coalbed methane is a clean, unconventional natural gas resource. Its development and utilization can improve the energy structure, the atmospheric environment, and protect the human living environment. The first step in coalbed methane development is to optimize the blocks, that is, to select the target areas. Selecting reasonable target areas is the basis for efficient coalbed methane development. The reservoir potential evaluation methods in the early stage of coalbed methane development mainly include conventional hierarchical analysis method coupled with factors affecting coalbed methane extraction, fuzzy comprehensive evaluation method, etc.

[0003] The above evaluation methods are highly subjective in determining the weights of influencing factors during the evaluation of coalbed methane development reservoir potential, which affects the final evaluation results. In this regard, the invention patent with publication number CN112529443B in the prior art provides a coalbed methane development target area optimization method based on fuzzy pattern recognition, which performs potential evaluation based on fuzzy pattern recognition, so that the determination of the results does not rely on human subjective judgment.

[0004] In the existing technology, it is not taken into account that the collected evaluation parameters may be incorrect. If a certain evaluation parameter is incorrect and has a heavy weight, and the score of the evaluation parameter itself deviates too much from the real data, it will have a great impact on the ranking of the block to be evaluated, resulting in a large error in the final ranking result. Summary of the Invention

[0005] To this end, the present invention provides a method for optimizing coalbed methane development target areas based on fuzzy comprehensive evaluation, which effectively solves the technical problem in the prior art that if the scores of the evaluation parameters themselves deviate too much from the real data, it will have a great impact on the ranking of the block to be evaluated, resulting in large errors in the final ranking results.

[0006] To solve the above technical problems, the present invention specifically provides the following technical solutions: a method for optimizing coalbed methane development target areas based on fuzzy comprehensive evaluation, comprising the following steps:

[0007] Perform data representation on each evaluation factor of the block to be evaluated, obtain the evaluation factor score corresponding to each evaluation factor of each block to be evaluated, and determine the evaluation factor score set;

[0008] Setting the weight corresponding to each evaluation factor to determine the evaluation factor weight set;

[0009] Calculate the membership of each evaluation factor based on the evaluation factor score set, and determine the fuzzy evaluation matrix of the block to be evaluated according to the membership;

[0010] Calculate the comprehensive evaluation score based on the fuzzy evaluation matrix, sort by the size of the comprehensive evaluation score, and obtain the overall ranking value of different blocks to be evaluated;

[0011] Eliminate n evaluation factors in turn, re-determine the evaluation factor score set and evaluation factor weight set, and repeat the above steps to analyze the local ranking values ​​of different blocks under the current evaluation factor combination;

[0012] The overall ranking value and the local ranking value are compared and the ranking difference is calculated. If the ranking difference of one of the blocks to be evaluated exceeds the difference threshold, the original overall ranking value is invalidated, and a new overall ranking value is re-determined based on the original overall ranking value and the local ranking value, and the target area is screened.

[0013] Furthermore, data characterization is performed on each evaluation factor of the block to be evaluated, the evaluation factor score corresponding to each evaluation factor of each block to be evaluated is obtained, and the evaluation factor score set is determined, including the following steps:

[0014] Obtain data collection results of various evaluation factors of the block to be evaluated;

[0015] The data collection results are divided into x evaluation levels, forming an evaluation level set S = (S1, S2, S3, ..., S x ), the evaluation factor scores corresponding to different evaluation levels are w1, w2, ..., w x ;

[0016] The evaluation level of each evaluation factor is determined based on the data collection results, and the evaluation factor score corresponding to each evaluation factor is determined based on the evaluation level to determine the evaluation factor score set.

[0017] Furthermore, the membership of each evaluation factor is calculated based on the evaluation factor score set, and the fuzzy evaluation matrix of the block to be evaluated is determined according to the membership, including the following steps:

[0018] The membership degree of each evaluation factor is calculated by the following formula:

[0019]

[0020] Where r ij Indicates that the block to be evaluated belongs to the evaluation level set S under the i-th evaluation factor j The membership degree of the evaluation factor, w is the evaluation factor score, w x is the maximum value of the evaluation factor score;

[0021] Determine the fuzzy evaluation matrix R of the block to be evaluated based on the degree of membership:

[0022]

[0023] Furthermore, the comprehensive evaluation scores are calculated based on the fuzzy evaluation matrix, sorted according to the size of the comprehensive evaluation scores, and the overall ranking values ​​of different blocks to be evaluated are obtained, including the following steps:

[0024] Multiply the evaluation factor weight set and the fuzzy evaluation matrix to obtain the comprehensive evaluation matrix:

[0025] D = K·R;

[0026] Where D is the comprehensive evaluation matrix, K is the weight set of evaluation factors, and R is the fuzzy evaluation matrix;

[0027] The comprehensive evaluation score is calculated using the following formula:

[0028] U=D·B T ;

[0029] In the formula, U is the comprehensive evaluation score, B T is the membership matrix The transposed matrix of

[0030] According to the size of the comprehensive evaluation score, the blocks to be evaluated are sorted to obtain the overall ranking values ​​of different blocks to be evaluated.

[0031] Furthermore, based on all the evaluation factors of the block to be evaluated, the overall ranking value of the block to be evaluated is obtained through calculation and analysis;

[0032] Based on at least two evaluation factors of the block to be evaluated, the local ranking value of the block to be evaluated is obtained through calculation and analysis;

[0033] Herein, at least two of the evaluation factors are not all evaluation factors.

[0034] Furthermore, n evaluation factors are sequentially screened out, the evaluation factor score set and the evaluation factor weight set are re-determined, and the above steps are repeated to analyze the local ranking values ​​of different blocks under the current evaluation factor combination, including the following steps:

[0035] Eliminate one of the evaluation factors, redetermine the evaluation factor score set and the evaluation factor weight set, and redetermine the fuzzy evaluation matrix of the block to be evaluated;

[0036] Calculate the comprehensive evaluation score based on the fuzzy evaluation matrix, sort the blocks according to the size of the comprehensive evaluation score, and obtain the local ranking values ​​of different blocks to be evaluated;

[0037] Adjust one of the evaluation factors to be screened out, and repeat the above steps until the local ranking value under the conditions of screening out different evaluation factors is obtained.

[0038] Furthermore, n evaluation factors are sequentially screened out, the evaluation factor score set and the evaluation factor weight set are re-determined, and the above steps are repeated to analyze the local ranking values ​​of different blocks under the current evaluation factor combination, including the following steps:

[0039] Screen out n evaluation factors, redefine the evaluation factor score set and evaluation factor weight set, and redefine the fuzzy evaluation matrix of the block to be evaluated;

[0040] Calculate the comprehensive evaluation score based on the fuzzy evaluation matrix, sort the blocks according to the size of the comprehensive evaluation score, and obtain the local ranking values ​​of different blocks to be evaluated;

[0041] Adjust n of the evaluation factors that are screened out, and repeat the above steps until the local ranking values ​​under the conditions of screening out different evaluation factors are obtained;

[0042] Wherein, n is greater than or equal to 1 and less than N-2, and N is the number of all evaluation factors.

[0043] Furthermore, different difference thresholds are set for local ranking values ​​obtained from different numbers of evaluation factors.

[0044] Furthermore, the calculated local ranking values ​​are multiple, and the overall ranking value and each local ranking value are subtracted to obtain the ranking difference;

[0045] The rank difference is compared with the difference threshold corresponding to the local rank difference. If the rank difference exceeds the local rank difference, the original overall rank value is invalidated.

[0046] Furthermore, the original overall ranking value and local ranking value are substituted into the following formula to re-determine the new overall ranking value:

[0047]

[0048] Where, Y1 is the new overall ranking value, ROUND is the rounding function, Y0 is the original overall ranking value, and Y j is the local rank value.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] In the present invention, an overall potential analysis is first performed based on all the evaluation factors to obtain an overall ranking value, and then a local potential analysis is performed based on some of the evaluation factors to obtain a local ranking value. The overall ranking value and the local ranking value correspond to the potential ranking of the blocks to be evaluated under different evaluation factor analysis conditions. The different ranking situations are compared and the differences are observed. When the differences are large, the ranking situations are adjusted. Taking into account the possibility that the evaluation factor scores may deviate too much from the true values, the ranking results are made closer to reality, thereby avoiding the situation where the ranking results have large errors due to the possible deviation of the evaluation factor scores from the true values. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0052] Figure 1 A flow chart of a method for optimizing coalbed methane development target areas based on fuzzy comprehensive evaluation is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] like Figure 1 As shown, the present invention provides a method for optimizing coalbed methane development target areas based on fuzzy comprehensive evaluation, comprising the following steps:

[0055] Perform data representation on each evaluation factor of the block to be evaluated, obtain the evaluation factor score corresponding to each evaluation factor of each block to be evaluated, and determine the evaluation factor score set;

[0056] Setting the weight corresponding to each evaluation factor to determine the evaluation factor weight set;

[0057] Calculate the membership of each evaluation factor based on the evaluation factor score set, and determine the fuzzy evaluation matrix of the block to be evaluated according to the membership;

[0058] Calculate the comprehensive evaluation score based on the fuzzy evaluation matrix, sort by the size of the comprehensive evaluation score, and obtain the overall ranking value of different blocks to be evaluated;

[0059] Eliminate n evaluation factors in turn, re-determine the evaluation factor score set and evaluation factor weight set, and repeat the above steps to analyze the local ranking values ​​of different blocks under the current evaluation factor combination;

[0060] The overall ranking value and the local ranking value are compared and the ranking difference is calculated. If the ranking difference of one of the blocks to be evaluated exceeds the difference threshold, the original overall ranking value is invalidated, and a new overall ranking value is re-determined based on the original overall ranking value and the local ranking value, and the target area is screened.

[0061] The present invention mainly screens development target areas based on the corrected overall ranking value.

[0062] In the present invention, an overall potential analysis is first performed based on all the evaluation factors to obtain an overall ranking value, and then a local potential analysis is performed based on some of the evaluation factors to obtain a local ranking value. The overall ranking value and the local ranking value correspond to the potential ranking of the blocks to be evaluated under different evaluation factor analysis conditions. The different ranking situations are compared and the differences are observed. When the differences are large, the ranking situations are adjusted. Taking into account the possibility that the evaluation factor scores may deviate too much from the true values, the ranking results are made closer to reality, thereby avoiding the situation where the ranking results have large errors due to the possible deviation of the evaluation factor scores from the true values.

[0063] In the present invention, based on all the evaluation factors of the block to be evaluated, the overall ranking value of the block to be evaluated is obtained through calculation and analysis;

[0064] Based on at least two evaluation factors of the block to be evaluated, the local ranking value of the block to be evaluated is obtained through calculation and analysis;

[0065] Among them, "at least two of the evaluation factors" does not mean all the evaluation factors.

[0066] Assuming that there are five evaluation factors a, b, c, d, and e, based on all the evaluation factors a, b, c, d, and e of the block to be evaluated, the overall ranking value of the block to be evaluated is obtained through calculation and analysis; based on at least two of the evaluation factors of the block to be evaluated, such as a, b or a, b, c or a, b, c, d, the local ranking value of the block to be evaluated is obtained through calculation and analysis.

[0067] Taking the existence of five evaluation factors a, b, c, d, and e as an example, if the scores of evaluation factors a, b, c, and d are all small, while the score of evaluation factor e is large and its weight is large, the true value of e is not as large as its corresponding evaluation factor score, resulting in the overall comprehensive evaluation score being too large. If the ranking result of this score is in the front, the overall ranking value is small, which will affect the selection of the target area.

[0068] The method provided by the present invention can directly perform potential analysis based on a, b, c, and d. If the above situation exists, the final comprehensive evaluation score of the potential analysis will inevitably be much smaller than the comprehensive evaluation score calculated based on a, b, c, d, and e. The local ranking value is greater than the overall ranking value. The ranking difference between the local ranking value and the overall ranking value is then analyzed. If it exceeds the difference threshold, the overall ranking value is corrected.

[0069] If the scores of evaluation factors a, b, c, and d are all high, while the score of evaluation factor e is low and its weight is large, the true value of e is not as low as its corresponding evaluation factor score, resulting in a small overall comprehensive evaluation score. If the ranking result of this score is at the back of the list and the overall ranking value is large, it will also affect the selection of the target area.

[0070] The method provided by the present invention can directly perform potential analysis based on a, b, c, and d. If the above situation exists, the final comprehensive evaluation score of the potential analysis will inevitably be much greater than the comprehensive evaluation score obtained by performing potential analysis based on a, b, c, d, and e. The local ranking value is smaller than the overall ranking value. The ranking difference between the local ranking value and the overall ranking value is then analyzed. If the difference exceeds the difference threshold, the overall ranking value is corrected.

[0071] From the above analysis, the method provided by the present invention is mainly aimed at the situation where one or two evaluation factors deviate from the true value, resulting in the ranking being too high or too low. In this case, it is easy for the blocks to be evaluated whose scores of the evaluation factors originally meet the standards to be ranked low, and it is also easy for the blocks to be evaluated whose scores of the evaluation factors originally do not meet the standards to be ranked high. Therefore, the method provided by the present invention can reduce the above situation and make the ranking results of the blocks to be evaluated more referenceable.

[0072] In the present invention, data characterization is performed on each evaluation factor of the block to be evaluated, the evaluation factor score corresponding to each evaluation factor of each block to be evaluated is obtained, and an evaluation factor score set is determined, which includes the following steps:

[0073] Obtain data collection results of various evaluation factors of the block to be evaluated;

[0074] The data collection results are divided into x evaluation levels, forming an evaluation level set S = (S1, S2, S3, ..., S x ), the evaluation factor scores corresponding to different evaluation levels are w1, w2, ..., w x ;

[0075] The evaluation level of each evaluation factor is determined based on the data collection results, and the evaluation factor score corresponding to each evaluation factor is determined based on the evaluation level to determine the evaluation factor score set.

[0076] In the above embodiment, one process for determining the evaluation factor score is disclosed. For different evaluation factors, their evaluation levels and the evaluation factor scores corresponding to different evaluation levels are different. Therefore, for the determination of different evaluation factor scores, it is necessary to re-accord with the above process and allocate and determine the corresponding evaluation factor scores according to different evaluation levels, so as to finally obtain the evaluation factor score set of the block to be evaluated.

[0077] In the present invention, a comprehensive evaluation score can be analyzed and calculated based on the evaluation factor score set and the evaluation factor weights, the membership of each evaluation factor is calculated based on the evaluation factor score set, and the fuzzy evaluation matrix of the block to be evaluated is determined according to the membership, and the comprehensive evaluation score is calculated based on the fuzzy evaluation matrix.

[0078] Specifically, the membership of each evaluation factor is calculated based on the evaluation factor score set, and the fuzzy evaluation matrix of the block to be evaluated is determined according to the membership, including the following steps:

[0079] The membership degree of each evaluation factor is calculated by the following formula:

[0080]

[0081] Where r ij Indicates that the block to be evaluated belongs to the evaluation level set S under the i-th evaluation factor j The membership degree of the evaluation factor, w is the evaluation factor score, w x is the maximum value of the evaluation factor score;

[0082] The fuzzy evaluation matrix R of the block to be evaluated is determined according to the membership degree of each evaluation factor:

[0083]

[0084] Calculate the comprehensive evaluation score based on the fuzzy evaluation matrix, sort by the size of the comprehensive evaluation score, and obtain the overall ranking value of different blocks to be evaluated, including the following steps:

[0085] Multiply the evaluation factor weight set and the fuzzy evaluation matrix to obtain the comprehensive evaluation matrix:

[0086] D = K·R;

[0087] Where D is the comprehensive evaluation matrix, K is the weight set of evaluation factors, and R is the fuzzy evaluation matrix;

[0088] The comprehensive evaluation score is calculated using the following formula:

[0089] U=D·B T ;

[0090] In the formula, U is the comprehensive evaluation score, BT is the membership matrix The transposed matrix of

[0091] According to the size of the comprehensive evaluation score, the blocks to be evaluated are sorted to obtain the overall ranking values ​​of different blocks to be evaluated.

[0092] Through the above process, the membership of each evaluation factor can be calculated. Assume that the corresponding evaluation level set for the first evaluation factor is S2. If the corresponding evaluation factor score is w, the corresponding r 11 =0, r 12 =w、……、r 1j =0.

[0093] Through the above process, the evaluation factor score of each evaluation factor can be determined, and the fuzzy evaluation matrix of the block to be evaluated can be determined according to the evaluation factor score, so as to calculate the comprehensive evaluation score.

[0094] After determining the comprehensive evaluation score of each block to be evaluated, sort them according to the size of the comprehensive evaluation score, and obtain the overall ranking values ​​of different blocks to be evaluated. Assuming that the comprehensive evaluation score of a block to be evaluated is the largest, its corresponding overall ranking value is 1. If the comprehensive evaluation score of a block to be evaluated is ranked second after sorting, its corresponding overall ranking value is 2.

[0095] In addition to performing potential analysis on all evaluation factors, the present invention also performs local potential analysis on several of the evaluation factors. The local potential analysis mainly adopts the following implementation methods, wherein the process of potential analysis is the process of calculating the overall ranking value or the local ranking value based on the evaluation factor score set and the evaluation factor weight set.

[0096] The first implementation method of local potential analysis is as follows:

[0097] Eliminate n evaluation factors in sequence, re-determine the evaluation factor score set and the evaluation factor weight set, and repeat the above steps to analyze the local ranking values ​​of different blocks under the current evaluation factor combination, including the following steps:

[0098] Eliminate one of the evaluation factors, redetermine the evaluation factor score set and the evaluation factor weight set, and redetermine the fuzzy evaluation matrix of the block to be evaluated;

[0099] Calculate the comprehensive evaluation score based on the fuzzy evaluation matrix, sort the blocks according to the size of the comprehensive evaluation score, and obtain the local ranking values ​​of different blocks to be evaluated;

[0100] Adjust one of the evaluation factors to be screened out, and repeat the above steps until the local ranking value under the conditions of screening out different evaluation factors is obtained.

[0101] Considering that the probability of only one data being wrong in the actual data collection process may be relatively high, the potential analysis was performed after only one evaluation factor was screened out.

[0102] Assuming there are five evaluation factors a, b, c, d, and e, there are five local potential analysis processes:

[0103] Eliminate the evaluation factor a and retain the evaluation factors b, c, d, and e to conduct corresponding potential analysis, and obtain the local ranking values ​​corresponding to the potential analysis of b, c, d, and e;

[0104] Eliminate the evaluation factor b and retain the evaluation factors a, c, d, and e for corresponding potential analysis to obtain the local ranking values ​​corresponding to the potential analysis of a, c, d, and e;

[0105] Eliminate the evaluation factor c and retain the evaluation factors a, b, d, and e for corresponding potential analysis to obtain the local ranking values ​​corresponding to the potential analysis of a, b, d, and e;

[0106] Eliminate the evaluation factor d and retain the evaluation factors a, b, c, and e for corresponding potential analysis to obtain the local ranking values ​​corresponding to the potential analysis of a, b, c, and e;

[0107] Eliminate the evaluation factor e, retain the evaluation factors a, b, c, and d, and conduct corresponding potential analysis to obtain the local ranking values ​​corresponding to the potential analysis of a, b, c, and d.

[0108] The second implementation method of local potential analysis is as follows:

[0109] Eliminate n evaluation factors in sequence, re-determine the evaluation factor score set and the evaluation factor weight set, and repeat the above steps to analyze the local ranking values ​​of different blocks under the current evaluation factor combination, including the following steps:

[0110] Screen out n evaluation factors, redefine the evaluation factor score set and evaluation factor weight set, and redefine the fuzzy evaluation matrix of the block to be evaluated;

[0111] Calculate the comprehensive evaluation score based on the fuzzy evaluation matrix, sort the blocks according to the size of the comprehensive evaluation score, and obtain the local ranking values ​​of different blocks to be evaluated;

[0112] Adjust n of the evaluation factors that are screened out, and repeat the above steps until the local ranking values ​​under the conditions of screening out different evaluation factors are obtained;

[0113] Wherein, n is greater than or equal to 1 and less than N-2, and N is the number of all evaluation factors.

[0114] Considering that there may be more than one data error in a block to be evaluated, the potential analysis can be conducted after one of the evaluation factors is screened out, or the potential analysis can be conducted after two, three, ... evaluation factors are screened out.

[0115] Since in general the data of the same block to be evaluated may only have one data error, and the cases where two, three or more data errors exist at the same time are rare, in practical applications, generally only one evaluation factor is screened out for local potential analysis, and at most two evaluation factors are screened out for local potential analysis.

[0116] In the present invention, after performing an overall potential analysis based on all the evaluation factors and a local potential analysis based on some of the evaluation factors, the overall ranking value and local ranking value of the block to be evaluated will be obtained. At this time, it is necessary to compare the overall ranking value and the local ranking value and calculate the ranking difference, and determine whether the ranking difference exceeds the difference threshold.

[0117] Among them, different difference thresholds are set for the local ranking values ​​obtained by different types and numbers of evaluation factors. For example, the difference threshold corresponding to the local ranking value obtained by 4 evaluation factors is different from the difference threshold corresponding to the local ranking value obtained by 3 evaluation factors; taking the existence of 5 evaluation factors a, b, c, d, and e as an example, the difference threshold corresponding to the local ranking value obtained by evaluation factors a, b, c, and d is different from the difference threshold corresponding to the local ranking value obtained by evaluation factors b, c, d, and e. The specific difference threshold is adjusted according to the actual situation.

[0118] The calculated local ranking values ​​are generally multiple, and the ranking difference is obtained by subtracting the overall ranking value from each local ranking value;

[0119] The rank difference is compared with the difference threshold corresponding to the local rank difference. If the rank difference exceeds the local rank difference, the original overall rank value is invalidated.

[0120] Assuming that there are five evaluation factors a, b, c, d, and e, if only one evaluation factor is screened out and local potential analysis is performed, five local ranking values ​​will be calculated. There is a difference threshold corresponding to each of these five local ranking values. The ranking difference is compared with the difference threshold corresponding to the local ranking difference. If the ranking difference exceeds the local ranking difference, the original overall ranking value will be invalidated.

[0121] After invalidating the original overall ranking value, substitute the original overall ranking value and local ranking value into the following formula to re-determine the new overall ranking value:

[0122]

[0123] Where, Y1 is the new overall ranking value, ROUND is the rounding function, Y0 is the original overall ranking value, and Yj is the local rank value.

[0124] The present invention mainly takes the middle value of the original overall ranking value and the local ranking value, and then rounds it up as the new overall ranking value. This ranking correction method may also have corresponding problems. Therefore, in practical applications, the original overall ranking value and the local ranking value can also be substituted into the following formula to re-determine the new overall ranking value:

[0125]

[0126] In the formula, e is a constant whose value is determined according to actual conditions.

[0127] In practice, the ranking correction is generally carried out in sequence. Assuming that there are five evaluation factors a, b, c, d, and e, and only one evaluation factor is screened out for local potential analysis, the overall ranking value of the block to be evaluated is obtained through calculation and analysis based on all the evaluation factors a, b, c, d, and e of the block to be evaluated; the local ranking value of the block to be evaluated is obtained through calculation and analysis based on the evaluation factors b, c, d, and e of the block to be evaluated, and the ranking difference is calculated. The ranking difference is compared with the difference threshold corresponding to the local ranking difference. If the ranking difference exceeds the local ranking difference, the original overall ranking value is invalidated and the overall ranking value is corrected according to the above steps (first correction);

[0128] Based on all evaluation factors a, b, c, d, and e of the block to be evaluated, the overall ranking value of the block to be evaluated is obtained through calculation and analysis; based on the evaluation factors a, c, d, and e of the block to be evaluated, the local ranking value of the block to be evaluated is obtained through calculation and analysis, the ranking difference is calculated, and the ranking difference is compared with the difference threshold corresponding to the local ranking difference. If the ranking difference exceeds the local ranking difference, the overall ranking value obtained by the first correction is invalidated and the overall ranking value is corrected according to the above steps (second correction);

[0129] The subsequent steps are carried out in this way to obtain the final overall ranking value.

[0130] If the ranking difference does not exceed the local ranking difference during the above calculation process, the current overall ranking difference will be retained.

[0131] The above process is the ranking correction process of a single block to be evaluated. Assuming there are h blocks to be evaluated, after completing one block to be evaluated, the ranking correction of the next block to be evaluated is carried out, and so on, until the ranking correction of the hth block to be evaluated is completed.

[0132] The present invention can not only make the sorting results more realistic when considering that the evaluation factor scores may deviate too much from the true values, and avoid the situation where the sorting results have large errors due to the possible deviation of the evaluation factor scores from the true values, but also avoid the situation where the target area is selected due to uneven values ​​of some factors. For example, if there are five evaluation factors a, b, c, d, and e, if the scores of the evaluation factors a, b, c, and d are all small, and the score of the evaluation factor e is large, and its weight is large, the overall comprehensive evaluation score is large. If the sorting result of this score is in the front, the overall ranking value is small, and it is selected as the target area, its a, b, c, and d evaluation factor scores are not high, and the values ​​are uneven. In theory, it is not suitable as a target area. However, it is selected as a target area because of its high score. Such a screening method is unreasonable. Therefore, the local potential analysis of the present invention can perform local potential analysis on the evaluation factors a, b, c, and d, which can effectively avoid the occurrence of the above situation.

[0133] The above embodiments are merely exemplary embodiments of the present application and are not intended to limit the scope of the present application. The scope of protection of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and scope of protection of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the scope of protection of the present application.

Claims

1. A method for optimizing coalbed methane development target areas based on fuzzy comprehensive evaluation, characterized in that: The following steps are involved: Perform data representation on each evaluation factor of the block to be evaluated, obtain the evaluation factor score corresponding to each evaluation factor of each block to be evaluated, and determine the evaluation factor score set; Setting the weight corresponding to each evaluation factor to determine the evaluation factor weight set; Calculate the membership of each evaluation factor based on the evaluation factor score set, and determine the fuzzy evaluation matrix of the block to be evaluated according to the membership; Calculate the comprehensive evaluation score based on the fuzzy evaluation matrix, sort by the size of the comprehensive evaluation score, and obtain the overall ranking value of different blocks to be evaluated; Eliminate n evaluation factors in turn, re-determine the evaluation factor score set and evaluation factor weight set, and repeat the above steps to analyze the local ranking values ​​of different blocks under the current evaluation factor combination; The overall ranking value and the local ranking value are compared and the ranking difference is calculated. If the ranking difference of one of the blocks to be evaluated exceeds the difference threshold, the original overall ranking value is invalidated, and a new overall ranking value is re-determined based on the original overall ranking value and the local ranking value, and the target area is screened.

2. The method for selecting target areas for coalbed methane development based on fuzzy comprehensive evaluation according to claim 1, characterized in that: The data representation of each evaluation factor of the block to be evaluated is performed to obtain the evaluation factor score of each evaluation factor corresponding to each block to be evaluated, and the evaluation factor score set is determined, including the following steps: Obtain data collection results of various evaluation factors of the block to be evaluated; The data collection results are divided into x evaluation levels, forming an evaluation level set S = (S1, S2, S3, ..., S x ), the evaluation factor scores corresponding to different evaluation levels are w1, w2, ..., w x ; The evaluation level of each evaluation factor is determined based on the data collection results, and the evaluation factor score corresponding to each evaluation factor is determined based on the evaluation level to determine the evaluation factor score set.

3. The method for selecting target areas for coalbed methane development based on fuzzy comprehensive evaluation according to claim 2, characterized in that: The membership degree of each evaluation factor is calculated based on the evaluation factor score set, and the fuzzy evaluation matrix of the block to be evaluated is determined according to the membership degree, including the following steps: The membership degree of each evaluation factor is calculated by the following formula: Where r ij Indicates that the block to be evaluated belongs to the evaluation level set S under the i-th evaluation factor j The membership degree of the evaluation factor, w is the evaluation factor score, w x is the maximum value of the evaluation factor score; Determine the fuzzy evaluation matrix R of the block to be evaluated based on the degree of membership:

4. The method for selecting target areas for coalbed methane development based on fuzzy comprehensive evaluation according to claim 3, characterized in that: Calculate the comprehensive evaluation score based on the fuzzy evaluation matrix, sort by the size of the comprehensive evaluation score, and obtain the overall ranking value of different blocks to be evaluated, including the following steps: Multiply the evaluation factor weight set and the fuzzy evaluation matrix to obtain the comprehensive evaluation matrix: D = K·R; Where D is the comprehensive evaluation matrix, K is the weight set of evaluation factors, and R is the fuzzy evaluation matrix; The comprehensive evaluation score is calculated using the following formula: U=D·B T ; In the formula, U is the comprehensive evaluation score, B T is the membership matrix The transposed matrix of According to the size of the comprehensive evaluation score, the blocks to be evaluated are sorted to obtain the overall ranking values ​​of different blocks to be evaluated.

5. The method for selecting target areas for coalbed methane development based on fuzzy comprehensive evaluation according to claim 1, characterized in that: Based on all the evaluation factors of the block to be evaluated, the overall ranking value of the block to be evaluated is obtained through calculation and analysis; Based on at least two evaluation factors of the block to be evaluated, the local ranking value of the block to be evaluated is obtained through calculation and analysis; Herein, at least two of the evaluation factors are not all evaluation factors.

6. The method for selecting target areas for coalbed methane development based on fuzzy comprehensive evaluation according to claim 1, characterized in that: Eliminate n evaluation factors in sequence, re-determine the evaluation factor score set and the evaluation factor weight set, and repeat the above steps to analyze the local ranking values ​​of different blocks under the current evaluation factor combination, including the following steps: Eliminate one of the evaluation factors, redetermine the evaluation factor score set and the evaluation factor weight set, and redetermine the fuzzy evaluation matrix of the block to be evaluated; Calculate the comprehensive evaluation score based on the fuzzy evaluation matrix, sort the blocks according to the size of the comprehensive evaluation score, and obtain the local ranking values ​​of different blocks to be evaluated; Adjust one of the evaluation factors to be screened out, and repeat the above steps until the local ranking value under the conditions of screening out different evaluation factors is obtained.

7. The method for selecting target areas for coalbed methane development based on fuzzy comprehensive evaluation according to claim 1, characterized in that: Eliminate n evaluation factors in sequence, re-determine the evaluation factor score set and the evaluation factor weight set, and repeat the above steps to analyze the local ranking values ​​of different blocks under the current evaluation factor combination, including the following steps: Screen out n evaluation factors, redefine the evaluation factor score set and evaluation factor weight set, and redefine the fuzzy evaluation matrix of the block to be evaluated; Calculate the comprehensive evaluation score based on the fuzzy evaluation matrix, sort the blocks according to the size of the comprehensive evaluation score, and obtain the local ranking values ​​of different blocks to be evaluated; Adjust n of the evaluation factors that are screened out, and repeat the above steps until the local ranking values ​​under the conditions of screening out different evaluation factors are obtained; Wherein, n is greater than or equal to 1 and less than N-2, and N is the number of all evaluation factors.

8. The method for selecting target areas for coalbed methane development based on fuzzy comprehensive evaluation according to claim 1, characterized in that: Different difference thresholds are set for the local ranking values ​​obtained for different numbers of evaluation factors.

9. The method for selecting target areas for coalbed methane development based on fuzzy comprehensive evaluation according to claim 1, characterized in that: There are multiple local ranking values ​​calculated, and the ranking difference is obtained by subtracting the overall ranking value from each local ranking value; The rank difference is compared with the difference threshold corresponding to the local rank difference. If the rank difference exceeds the local rank difference, the original overall rank value is invalidated.

10. The method for selecting target areas for coalbed methane development based on fuzzy comprehensive evaluation according to claim 9, characterized in that: Substitute the original overall ranking value and local ranking value into the following formula to re-determine the new overall ranking value: Where, Y1 is the new overall ranking value, ROUND is the rounding function, Y0 is the original overall ranking value, and Y j is the local rank value.

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