Crack complex index construction method and device

Abstract

The invention relates to the field of oil and gas drilling and production engineering, and discloses a crack complex index construction method and device, which can obtain normalized parameter value distribution of a plurality of crack basic parameters in a fractured stratum and a relative importance scale between every two crack basic parameters. Based on the normalized parameter value distribution of each fracture basic parameter, generating an objective weight coefficient of each fracture basic parameter; and based on the relative importance scale between every two fracture basic parameters, generating a subjective weight coefficient of each fracture basic parameter. And according to the objective weight coefficient and the subjective weight coefficient of each fracture basic parameter, performing combined weighting on each fracture basic parameter to generate an optimal weight coefficient of each fracture basic parameter. And performing linear weighting on each fracture basic parameter based on the optimal weight coefficient of each fracture basic parameter so as to construct a fracture complex index. The crack complexity index constructed by the method can be used for comprehensively representing the crack complexity of the fractured stratum.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas drilling and production engineering, and particularly to a method and device for constructing a fracture complexity index. Background Art

[0002] Fractured formations and unconventional formations are important areas for oil and gas exploration and development, and the complexity of formation fractures directly affects the safety and operation efficiency of each stage in the entire life cycle of oil and gas drilling and production.

[0003] The fracture complexity can be used as one of the key indicators for evaluating the fracturability of reservoirs, for optimizing the fracturing stimulation design, improving the efficiency of reservoir stimulation, and reducing the cost of oil and gas development.

[0004] The fracture characteristic parameters mainly include fracture length, fracture width, fracture roughness, etc. Although these parameters can reflect the characteristics of formation fractures from a certain specific angle, they cannot comprehensively characterize the complexity of formation fractures. Summary of the Invention

[0005] The present invention provides a method and device for constructing a fracture complexity index to solve the defect that the fracture characteristic parameters in the related art cannot comprehensively characterize the complexity of formation fractures, and constructs a fracture complexity index to comprehensively characterize the fracture complexity of the fractured formation.

[0006] In a first aspect, the present invention provides a method for constructing a fracture complexity index, including:

[0007] Obtaining the normalized parameter value distribution of multiple fracture basic parameters in a fractured formation, and the relative importance scale between every two of the fracture basic parameters; wherein, the normalized parameter value distribution of each fracture basic parameter is obtained by normalizing the parameter value distribution of the fracture basic parameter;

[0008] Generating an objective weight coefficient for each fracture basic parameter based on the normalized parameter value distribution of each fracture basic parameter; and generating a subjective weight coefficient for each fracture basic parameter based on the relative importance scale between every two of the fracture basic parameters;

[0009] Performing combined weighting on each fracture basic parameter according to the objective weight coefficient and subjective weight coefficient of each fracture basic parameter to generate an optimal weight coefficient for each fracture basic parameter;

[0010] Performing linear weighting on each fracture basic parameter based on the optimal weight coefficient of each fracture basic parameter to construct a fracture complexity index, and the fracture complexity index is used to characterize the fracture complexity of the fractured formation.

[0011] Optionally, in the normalized parameter value distribution of each of the crack basic parameters, it includes multiple normalized parameter values corresponding to different formation depths;

[0012] Generating an objective weight coefficient for each of the crack basic parameters based on the normalized parameter value distribution of each of the crack basic parameters includes:

[0013] For the normalized parameter value distribution of any one of the crack basic parameters, based on each of the normalized parameter values in the normalized parameter value distribution, determining the index proportion data corresponding to the crack basic parameter, based on the index proportion data corresponding to the crack basic parameter, determining the information entropy value corresponding to the crack basic parameter, and based on the information entropy value corresponding to the crack basic parameter, determining the difference coefficient corresponding to the crack basic parameter;

[0014] Generating an objective weight coefficient for each of the crack basic parameters according to the difference coefficient corresponding to each of the crack basic parameters.

[0015] Optionally, the determining the index proportion data corresponding to the crack basic parameter based on each of the normalized parameter values in the normalized parameter value distribution includes:

[0016] Summing each of the normalized parameter values in the normalized parameter value distribution to obtain the total parameter;

[0017] Dividing each of the normalized parameter values in the normalized parameter value distribution by the total parameter respectively to obtain the proportion corresponding to each of the normalized parameter values in the normalized parameter value distribution, and taking the whole as the index proportion data corresponding to the crack basic parameter;

[0018] The determining the information entropy value corresponding to the crack basic parameter based on the index proportion data corresponding to the crack basic parameter includes:

[0019] Inputting the index proportion data corresponding to the crack basic parameter into the established information entropy value model for calculation to obtain the information entropy value corresponding to the crack basic parameter.

[0020] Optionally, the determining the difference coefficient corresponding to the crack basic parameter based on the information entropy value corresponding to the crack basic parameter includes:

[0021] For the information entropy value corresponding to any one of the crack basic parameters, calculating the difference obtained by subtracting the information entropy value from 1, and taking the difference as the difference coefficient corresponding to the crack basic parameter;

[0022] The generating an objective weight coefficient for each of the crack basic parameters according to the difference coefficient corresponding to each of the crack basic parameters includes:

[0023] Sum the difference coefficients corresponding to each of the basic crack parameters to obtain the total coefficient;

[0024] Divide the difference coefficient corresponding to each of the basic crack parameters by the total coefficient respectively to obtain the coefficient ratio corresponding to each of the basic crack parameters, and use them as the objective weight coefficients of each of the basic crack parameters respectively.

[0025] Optionally, generating the subjective weight coefficient of each of the basic crack parameters based on the relative importance scale between every two of the basic crack parameters includes:

[0026] Generate a corresponding complementary judgment matrix based on the relative importance scale between every two of the basic crack parameters; wherein, in each row element and each column element of the complementary judgment matrix, it includes the relative importance scale between one of the basic crack parameters and each of the basic crack parameters respectively;

[0027] Normalize each of the column elements in the complementary judgment matrix respectively to obtain a normalized matrix;

[0028] Generate the subjective weight coefficient of each of the basic crack parameters based on the normalized matrix.

[0029] Optionally, the step of normalizing each of the column elements in the complementary judgment matrix respectively to obtain a normalized matrix includes:

[0030] For any one of the column elements in the complementary judgment matrix, calculate the sum of each of the relative importance scales in the column element to obtain the column scale sum value, divide each of the relative importance scales in the column element by the column scale sum value respectively to obtain the normalized scale corresponding to each of the relative importance scales in the column element, and arrange the normalized scales corresponding to each of the relative importance scales according to the arrangement order of each of the relative importance scales in the column element to obtain the normalized column element corresponding to the column element;

[0031] Arrange the normalized column elements corresponding to each of the column elements according to the arrangement order of each of the column elements in the complementary judgment matrix to obtain the normalized matrix.

[0032] Optionally, each normalized row element in the normalized matrix corresponds to each of the basic crack parameters one by one;

[0033] The step of generating the subjective weight coefficient of each of the basic crack parameters based on the normalized matrix includes:

[0034] For any of the normalized row elements, sum each of the normalized scales in the normalized row element to obtain a row scale sum value;

[0035] Sum each of the row scale sum values to obtain a total scale sum;

[0036] Divide each of the row scale sum values by the total scale sum to obtain a scale ratio corresponding to each of the row scale sum values, and use them as the subjective weight coefficients corresponding to the basic crack parameters respectively.

[0037] Optionally, the step of combining and weighting each of the basic crack parameters according to the objective weight coefficient and the subjective weight coefficient of each of the basic crack parameters to generate an optimal weight coefficient for each of the basic crack parameters includes:

[0038] Perform a consistency test on the objective weight coefficient and the subjective weight coefficient of each of the basic crack parameters;

[0039] When it is determined that the consistency test passes, then combine and weight each of the basic crack parameters according to the objective weight coefficient and the subjective weight coefficient of each of the basic crack parameters to generate an optimal weight coefficient for each of the basic crack parameters;

[0040] After performing the consistency test on the objective weight coefficient and the subjective weight coefficient of each of the basic crack parameters, the method further includes:

[0041] When it is determined that the consistency test fails, optimize the objective weight coefficient and the subjective weight coefficient of each of the basic crack parameters to obtain an optimized objective weight coefficient and an optimized subjective weight coefficient for each of the basic crack parameters;

[0042] Continue to perform a consistency test on the optimized objective weight coefficient and the optimized subjective weight coefficient of each of the basic crack parameters until the consistency test passes, and obtain an optimal objective weight coefficient and an optimal subjective weight coefficient for each of the basic crack parameters;

[0043] Combine and weight each of the basic crack parameters based on the optimal objective weight coefficient and the optimal subjective weight coefficient of each of the basic crack parameters to generate an optimal weight coefficient for each of the basic crack parameters.

[0044] Optionally, the step of combining and weighting each of the basic crack parameters according to the objective weight coefficient and the subjective weight coefficient of each of the basic crack parameters to generate an optimal weight coefficient for each of the basic crack parameters includes:

[0045] For the objective weight coefficient and the subjective weight coefficient of any one of the basic crack parameters, calculate the coefficient difference between the objective weight coefficient and the subjective weight coefficient of the basic crack parameters, and calculate the square of the coefficient difference to obtain the square of the coefficient difference corresponding to the basic crack parameters;

[0046] Sum up the squares of the coefficient differences of each of the basic crack parameters to obtain the corresponding sum of squares value, and use it as the relative distance between the subjective weight coefficient and the objective weight coefficient;

[0047] Based on the relative distance, construct a first equation and a second equation; wherein, the first equation is that the sum of the first preference degree coefficient of the subjective weight coefficient and the second preference degree coefficient of the objective weight coefficient is equal to 1, and the second equation is that the square value of the target difference is equal to the relative distance, and the target difference is the difference between the first preference degree coefficient and the second preference degree coefficient;

[0048] Solve the first equation and the second equation to obtain the first preference degree coefficient and the second preference degree coefficient;

[0049] For the objective weight coefficient and the subjective weight coefficient of any one of the basic crack parameters, based on the first preference degree coefficient and the second preference degree coefficient, perform weighted summation on the objective weight coefficient and the subjective weight coefficient of the basic crack parameters to obtain the optimal weight coefficient corresponding to the basic crack parameters.

[0050] In a second aspect, the present invention provides a device for constructing a crack complexity index, including:

[0051] A data acquisition unit, configured to acquire the normalized parameter value distribution of multiple basic crack parameters in a fractured formation, and the relative importance scale between any two of the basic crack parameters; wherein, the normalized parameter value distribution of each of the basic crack parameters is obtained by normalizing the parameter value distribution of the basic crack parameters;

[0052] A first generation unit, configured to generate an objective weight coefficient for each of the basic crack parameters based on the normalized parameter value distribution of each of the basic crack parameters;

[0053] A second generation unit, configured to generate a subjective weight coefficient for each of the basic crack parameters based on the relative importance scale between any two of the basic crack parameters;

[0054] A combined weighting unit, configured to perform combined weighting on each of the basic crack parameters according to the objective weight coefficient and the subjective weight coefficient of each of the basic crack parameters to generate an optimal weight coefficient for each of the basic crack parameters;

[0055] An index construction unit is configured to linearly weight each of the basic fracture parameters based on the optimal weight coefficient of each basic fracture parameter to construct a fracture complexity index, which is used to characterize the fracture complexity of the fractured formation.

[0056] In a third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the fracture complexity index construction method according to the first aspect or any corresponding embodiment thereof.

[0057] In a fourth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored. The computer instructions are used to cause a computer to execute the fracture complexity index construction method according to the first aspect or any corresponding embodiment thereof.

[0058] The fracture complexity index construction method and device provided by the present invention can obtain the normalized parameter value distribution of multiple basic fracture parameters in a fractured formation, and the relative importance scale between every two basic fracture parameters; wherein, the normalized parameter value distribution of each basic fracture parameter is obtained by normalizing the parameter value distribution of the basic fracture parameter. Based on the normalized parameter value distribution of each basic fracture parameter, an objective weight coefficient of each basic fracture parameter is generated; and, based on the relative importance scale between every two basic fracture parameters, a subjective weight coefficient of each basic fracture parameter is generated. According to the objective weight coefficient and subjective weight coefficient of each basic fracture parameter, combined weighting is performed on each basic fracture parameter to generate an optimal weight coefficient of each basic fracture parameter. Based on the optimal weight coefficient of each basic fracture parameter, linear weighting is performed on each basic fracture parameter to construct a fracture complexity index. The fracture complexity index constructed by the present invention can be used to comprehensively characterize the fracture complexity of the fractured formation. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0060] Figure 1 It is a flowchart of a fracture complexity index construction method provided by an embodiment of the present invention;

[0061] Figure 2 It is a fracture complexity index profile diagram of a fractured formation provided by an embodiment of the present invention;

[0062] Figure 3 Flow chart of another method for constructing fracture complexity index provided by an embodiment of the present invention;

[0063] Figure 4 Objective weight-related data of fracture aperture and fracture strength provided by an embodiment of the present invention;

[0064] Figure 5 Relative importance scale diagram of fracture aperture and fracture strength provided by an embodiment of the present invention;

[0065] Figure 6 Complementary judgment diagram of fracture aperture and fracture strength provided by an embodiment of the present invention;

[0066] Figure 7 Schematic diagram of relevant weight coefficients of fracture aperture and fracture strength provided by an embodiment of the present invention;

[0067] Figure 8 Schematic structural diagram of a device for constructing fracture complexity index provided by an embodiment of the present invention;

[0068] Figure 9 Schematic structural diagram of a computer device provided by an embodiment of the present invention. Detailed implementation manners

[0069] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present invention fall within the protection scope of the present invention.

[0070] The following combines Figures 1-7 to describe the method for constructing fracture complexity index of the present invention.<s

[0071] As Figure 1 shown, the first method for constructing fracture complexity index is proposed in this embodiment, and the method may include the following steps:

[0072] S101. Obtain the normalized parameter value distribution of multiple fracture basic parameters in a fractured formation, and the relative importance scale between two-by-two fracture basic parameters; wherein, the normalized parameter value distribution of each fracture basic parameter is obtained by normalizing the parameter value distribution of the fracture basic parameter.

[0073] Among them, the fractured formation may be a fractured formation in an oilfield area.

[0074] Optionally, multiple fracture basic parameters may include at least two of fracture length, fracture width, fracture aperture, fracture strength, and fracture roughness.

[0075] Among them, the relative importance scale can be determined by a technician according to the mutual importance scale between different fracture basic parameters. The mutual importance scale between two fracture basic parameters is reciprocal to each other, that is, the product is 1. For example, the importance scale value of fracture aperture for fracture strength is 0.5, and the importance scale value of fracture strength for fracture aperture is 2. The mutual importance scale between the same fracture basic parameters is 1. For example, the importance scale value of fracture strength for fracture strength is 1.

[0076] Specifically, in this embodiment, logging methods such as dual laterolog resistivity logging and imaging logging can be used to log the fractured formation to obtain the parameter value distribution of multiple fracture basic parameters in the fractured formation. The parameter value distribution of each fracture basic parameter includes the parameter values at different formation depths in the fractured formation.

[0077] Specifically, for the parameter value distribution of any fracture basic parameter in this embodiment, normalization can be performed. In the parameter value distribution of this fracture basic parameter, the maximum parameter value and the minimum parameter value of this fracture basic parameter can be determined, and each parameter value of this fracture basic parameter is normalized based on the maximum parameter value and the minimum parameter value to obtain multiple normalized parameter values of this fracture basic parameter, and the whole is used as the normalized parameter value of this fracture basic parameter.

[0078] S102. Generate the objective weight coefficient of each fracture basic parameter based on the normalized parameter value distribution of each fracture basic parameter.

[0079] Specifically, in this embodiment, the objective weight coefficient of each fracture basic parameter can be determined based on the normalized parameter value distribution of all fracture basic parameters.

[0080] S103. Generate the subjective weight coefficient of each fracture basic parameter based on the relative importance scale between pairwise fracture basic parameters.

[0081] Specifically, in this embodiment, the subjective weight coefficient of each fracture basic parameter can be determined based on the relative importance scale between pairwise fracture basic parameters.

[0082] It can be understood that, as Figure 1 shown, in this embodiment, step S102 can be executed first, and then step S103. In this embodiment, step S103 can also be executed first, and then step S102. Of course, in this embodiment, step S102 and step S103 can also be executed simultaneously.

[0083] S104. Based on the objective weight coefficient and subjective weight coefficient of each basic fracture parameter, perform combined weighting on each basic fracture parameter to generate the optimal weight coefficient of each basic fracture parameter.

[0084] Specifically, in this embodiment, based on the objective weight coefficient and subjective weight coefficient of all basic fracture parameters, combined weighting can be performed on all basic fracture parameters to determine the optimal weight coefficient of each basic fracture parameter.

[0085] S105. Perform linear weighting on each basic fracture parameter based on the optimal weight coefficient of each basic fracture parameter to construct a fracture complexity index, which is used to characterize the fracture complexity of a fractured formation.

[0086] Specifically, in this embodiment, the fracture complexity index can be constructed by performing linear weighting on each basic fracture parameter using the optimal weight coefficient of each basic fracture parameter. For example, when the above-mentioned multiple basic fracture parameters include fracture aperture A and fracture intensity B, and the optimal weight coefficient of fracture aperture is a and the optimal weight coefficient of fracture intensity is b, the fracture complexity index FCI = A × a + B × b.

[0087] It can be understood that the fracture complexity index can be used to comprehensively characterize the fracture complexity of the formation.

[0088] Among them, in this embodiment, the fracture complexity index at different formation depths in the fractured formation can be determined. Specifically, in this embodiment, the normalized parameter values of each basic fracture parameter at a certain formation depth in the fractured formation can be obtained, and based on the optimal weight coefficient of each basic fracture parameter, linear weighting is performed on the normalized parameter values of each basic fracture parameter, and the obtained value is the fracture complexity index at this formation depth.

[0089] It should be noted that in this embodiment, the fracture complexity index at different formation depths in the fractured formation can be determined, and a fracture complexity index profile as shown in Figure 2 can be constructed to comprehensively characterize the fracture complexity in a single-well fractured formation.

[0090] The method for constructing the fracture complexity index proposed in this embodiment can obtain the normalized parameter value distribution of multiple fracture basic parameters in a fractured formation, as well as the relative importance scale between every two fracture basic parameters; wherein, the normalized parameter value distribution of each fracture basic parameter is obtained by normalizing the parameter value distribution of the fracture basic parameter. Based on the normalized parameter value distribution of each fracture basic parameter, an objective weight coefficient of each fracture basic parameter is generated; and, based on the relative importance scale between every two fracture basic parameters, a subjective weight coefficient of each fracture basic parameter is generated. According to the objective weight coefficient and the subjective weight coefficient of each fracture basic parameter, combined weighting is performed on each fracture basic parameter to generate an optimal weight coefficient of each fracture basic parameter. Based on the optimal weight coefficient of each fracture basic parameter, linear weighting is performed on each fracture basic parameter to construct a fracture complexity index. The fracture complexity index constructed in this embodiment can be used to comprehensively characterize the fracture complexity of a fractured formation.

[0091] Based on Figure 1 , this embodiment proposes a second method for constructing the fracture complexity index. In this method, the normalized parameter value distribution of each fracture basic parameter includes multiple normalized parameter values corresponding to different formation depths. At this time, step S102 may include:

[0092] For the normalized parameter value distribution of any fracture basic parameter, based on each normalized parameter value in the normalized parameter value distribution, determine the index proportion data corresponding to the fracture basic parameter, based on the index proportion data corresponding to the fracture basic parameter, determine the information entropy value corresponding to the fracture basic parameter, and based on the information entropy value corresponding to the fracture basic parameter, determine the difference coefficient corresponding to the fracture basic parameter;

[0093] According to the difference coefficient corresponding to each fracture basic parameter, an objective weight coefficient of each fracture basic parameter is generated.

[0094] It should be noted that the level of the information entropy value reflects the amount of effective information provided by the index data. The information entropy value can be used to evaluate the variation degree of the parameter value distribution of the fracture basic parameter. The smaller the information entropy value, the greater the variation degree of the parameter value of the fracture basic parameter, the more information is provided, and the greater the role it can play in the comprehensive evaluation of the fracture complexity. The difference coefficient can reflect the contribution degree of the fracture basic parameter to the comprehensive evaluation of the fracture complexity. Calculate the difference coefficient of the fracture basic parameter, and the larger the difference coefficient, the greater the influence of the fracture basic parameter on the comprehensive evaluation. Calculate the proportion of the difference coefficient of the fracture basic parameter in the total sum of the difference coefficients of all fracture basic parameters, which is the objective weight coefficient of the fracture basic parameter. The greater the objective weight of the fracture basic parameter, the higher the importance of the fracture basic parameter in the comprehensive evaluation of the fracture complexity.

[0095] Optionally, determining the index weight data corresponding to the basic crack parameters based on each normalized parameter value in the normalized parameter value distribution includes:

[0096] Sum each normalized parameter value in the normalized parameter value distribution to obtain the total parameter;

[0097] Divide each normalized parameter value in the normalized parameter value distribution by the total parameter respectively to obtain the proportion corresponding to each normalized parameter value in the normalized parameter value distribution, and use the whole as the index weight data corresponding to the basic crack parameters.

[0098] Optionally, determining the information entropy value corresponding to the basic crack parameters based on the index weight data corresponding to the basic crack parameters includes:

[0099] Input the index weight data corresponding to the basic crack parameters into the established information entropy value model for calculation to obtain the information entropy value corresponding to the basic crack parameters; where the information entropy value model is:

[0100]

[0101] E is the information entropy value corresponding to the basic crack parameters; m is the total number of proportions in the index weight data, i is the serial number of the proportion in the index weight data, and A i is the proportion with the serial number i in the index weight data.

[0102] Optionally, determining the coefficient of variation corresponding to the basic crack parameters based on the information entropy value corresponding to the basic crack parameters includes:

[0103] For the information entropy value corresponding to any basic crack parameter, calculate the difference obtained by subtracting the information entropy value from 1, and use the difference as the coefficient of variation corresponding to the basic crack parameter;

[0104] Generating the objective weight coefficient of each basic crack parameter according to the coefficient of variation corresponding to each basic crack parameter includes:

[0105] Sum the coefficients of variation corresponding to each basic crack parameter to obtain the total coefficient;

[0106] Divide the coefficient of variation corresponding to each basic crack parameter by the total coefficient respectively to obtain the coefficient ratio corresponding to each basic crack parameter, and use them as the objective weight coefficients of each basic crack parameter respectively.

[0107] Specifically, this embodiment can effectively determine the objective weight coefficient of each basic crack parameter.

[0108] Optionally, in this method, step S103 may include:

[0109] Based on the relative importance scales between pairwise crack basic parameters, a corresponding complementary judgment matrix is generated; wherein, in each row element and each column element of the complementary judgment matrix, there is included the relative importance scale between one crack basic parameter and each crack basic parameter respectively.

[0110] Normalize each column element in the complementary judgment matrix respectively to obtain a normalized matrix.

[0111] Based on the normalized matrix, the subjective weight coefficients of each crack basic parameter are generated.

[0112] Optionally, the above-mentioned normalization of each column element in the complementary judgment matrix respectively to obtain a normalized matrix includes:

[0113] For any column element in the complementary judgment matrix, calculate the sum of each relative importance scale in the column element to obtain the column scale sum value. Divide each relative importance scale in the column element by the column scale sum value respectively to obtain the normalized scale corresponding to each relative importance scale in the column element. Arrange the normalized scales corresponding to each relative importance scale according to the arrangement order of each relative importance scale in the column element to obtain the normalized column element corresponding to the column element.

[0114] Arrange the normalized column elements corresponding to each column element according to the arrangement order of each column element in the complementary judgment matrix to obtain a normalized matrix.

[0115] Wherein, the row element in the complementary judgment matrix is a whole row element in the complementary judgment matrix, and the column element in the complementary judgment matrix is a whole column element in the complementary judgment matrix.

[0116] To better illustrate the composition structure of the complementary judgment matrix, Example 1 below is proposed in this embodiment for illustration.

[0117] Example 1: Among the above-mentioned multiple crack basic parameters, there are A, B, and C. The relative importance scale of A to A is aa, the relative importance scale of A to B is ab, and the relative importance scale of A to C is ac. The relative importance scale of B to A is ba, the relative importance scale of B to B is bb, and the relative importance scale of B to C is bc. The relative importance scale of C to A is ca, the relative importance scale of C to B is cb, and the relative importance scale of C to C is cc. At this time, the complementary judgment matrix can be:

[0118]

[0119] Specifically, in this embodiment, a normalization matrix can be determined. For example, the sum of each column element in the complementary judgment matrix S1 is calculated. The sum value L1 of the first column element is L1 = aa + ba + ca, the sum value L2 of the second column element is L2 = ab + bb + cb, and the sum value L3 of the third column element is L3 = ac + bc + cc. At this time, the normalization matrix is:

[0120]

[0121] Optionally, each normalized row element in the normalization matrix corresponds one-to-one to each crack basic parameter;

[0122] Based on the normalization matrix, subjective weight coefficients of each crack basic parameter are generated, including:

[0123] For any normalized row element, the sum of each normalized scale in the normalized row element is calculated to obtain the row scale sum value;

[0124] The sum of each row scale sum value is calculated to obtain the scale total;

[0125] Each row scale sum value is divided by the scale total respectively to obtain the scale ratio corresponding to each row scale sum value, and they are respectively used as the subjective weight coefficients of the corresponding crack basic parameters.

[0126] Among them, the normalized row element is a whole row of elements in the normalization matrix, and the normalized column element is a whole column of elements in the normalization matrix. It can be understood that since each row element and each column element in the complementary judgment matrix include the relative importance scale between a crack basic parameter and each crack basic parameter, and the normalization matrix is obtained by normalizing each column element in the complementary judgment matrix. Therefore, each normalized row element and each normalized column element in the normalization matrix include the normalized scale between a crack basic parameter and each crack basic parameter. At this time, each normalized row element in the normalization matrix will uniquely correspond to a crack basic parameter. For example, when the first row of normalized row elements in the normalization matrix includes the normalized scale between crack basic parameter A and each crack basic parameter, then the first row of normalized row elements uniquely corresponds to crack basic parameter A.

[0127] Continuing with the above example of the normalization matrix S2, the first normalized row element in the normalization matrix S2 uniquely corresponds to crack basic parameter A, the second normalized row element in the normalization matrix S2 uniquely corresponds to crack basic parameter B, and the third normalized row element in the normalization matrix S2 uniquely corresponds to crack basic parameter C.

[0128] Specifically, in this embodiment, the subjective weight coefficients of the crack basic parameters can be determined respectively based on the normalization matrix.

[0129] The method for constructing the fracture complexity index proposed in this embodiment can effectively determine the objective weight coefficient and subjective weight coefficient of each fracture basic parameter, and effectively ensure the smooth implementation of the subsequent construction of the fracture complexity index.

[0130] Based on Figure 1 , this embodiment proposes a third method for constructing the fracture complexity index. In this method, step S104 may include:

[0131] Conduct a consistency test on the objective weight coefficient and subjective weight coefficient of each fracture basic parameter;

[0132] When it is determined that the consistency test passes, then according to the objective weight coefficient and subjective weight coefficient of each fracture basic parameter, a combined weighting is performed on each fracture basic parameter to generate the optimal weight coefficient of each fracture basic parameter.

[0133] At this time, after the above-mentioned consistency test on the objective weight coefficient and subjective weight coefficient of each fracture basic parameter, this method may further include:

[0134] When it is determined that the consistency test fails, optimize the objective weight coefficient and subjective weight coefficient of each fracture basic parameter to obtain the optimized objective weight coefficient and optimized subjective weight coefficient of each fracture basic parameter;

[0135] Continue to conduct a consistency test on the optimized objective weight coefficient and optimized subjective weight coefficient of each fracture basic parameter until the consistency test passes, and obtain the optimal objective weight coefficient and optimal subjective weight coefficient of each fracture basic parameter;

[0136] Based on the optimal objective weight coefficient and optimal subjective weight coefficient of each fracture basic parameter, perform a combined weighting on each fracture basic parameter to generate the optimal weight coefficient of each fracture basic parameter.

[0137] Among them, this embodiment may use the Spearman rank correlation coefficient to conduct a consistency test on the objective weight coefficient and subjective weight coefficient of all fracture basic parameters. Specifically, this embodiment may calculate the Spearman rank correlation coefficient based on the objective weight coefficient and subjective weight coefficient of the fracture basic parameters. The calculation formula of the Spearman rank correlation coefficient is:

[0138]

[0139] Among them, ρ is the Spearman rank correlation coefficient, n is the total number of fracture basic parameters, j is the serial number of the fracture basic parameter, P j is the subjective weight coefficient of the jth fracture basic parameter, Q j is the objective weight coefficient of the jth fracture basic parameter.

[0140] Specifically, if the Spearman rank correlation coefficient is in the interval (0, 1], it indicates that the subjective weight coefficient and the objective weight coefficient are consistent. Otherwise, if the Spearman rank correlation coefficient is not in the interval (0, 1], it indicates that the subjective weight coefficient and the objective weight coefficient are not consistent, and it is necessary to optimize the subjective weight coefficient and the objective weight coefficient of at least one crack basic parameter, and continue to perform consistency test during the optimization until the consistency test passes.

[0141] Specifically, after the consistency test passes in this embodiment, based on the current objective weight coefficient and subjective weight coefficient of all crack basic parameters, combined weighting is performed on each crack basic parameter to determine the optimal weight coefficient of each crack basic parameter.

[0142] The method for constructing the crack complexity index proposed in this embodiment can first perform consistency test on the objective weight coefficient and subjective weight coefficient of each crack basic parameter. After determining that the consistency test passes, combined weighting is performed on each crack basic parameter, which can effectively ensure the accuracy and effectiveness of weighting.

[0143] Based on Figure 1 , the fourth method for constructing the crack complexity index is proposed in this embodiment. In this method, step S104 may include:

[0144] For the objective weight coefficient and subjective weight coefficient of any crack basic parameter, calculate the coefficient difference between the objective weight coefficient and the subjective weight coefficient of the crack basic parameter, and calculate the square of the coefficient difference to obtain the squared coefficient difference corresponding to the crack basic parameter.

[0145] Sum the squared coefficient differences of each crack basic parameter to obtain the corresponding sum of squared values, and use it as the relative distance between the subjective weight coefficient and the objective weight coefficient.

[0146] Based on the relative distance, construct a first equation and a second equation; wherein, the first equation is that the sum of the first preference degree coefficient of the subjective weight coefficient and the second preference degree coefficient of the objective weight coefficient is equal to 1, and the second equation is that the squared value of the target difference is equal to the relative distance, and the target difference is the difference between the first preference degree coefficient and the second preference degree coefficient.

[0147] Solve the first equation and the second equation to obtain the first preference degree coefficient and the second preference degree coefficient.

[0148] For the objective weight coefficient and subjective weight coefficient of any crack basic parameter, perform weighted summation on the objective weight coefficient and the subjective weight coefficient of the crack basic parameter based on the first preference degree coefficient and the second preference degree coefficient to obtain the optimal weight coefficient corresponding to the crack basic parameter.

[0149] Specifically, after passing the consistency test, this embodiment can perform combined weighting on the objective weight coefficient and the subjective weight coefficient of each basic crack parameter, including but not limited to using the addition synthesis method, the multiplication synthesis method, and the range maximization combined weighting method, etc. Optionally, this embodiment can construct an optimization decision model using the Lagrange function and the Euclidean distance function to determine the optimal weight coefficients of the crack aperture and the crack intensity.

[0150] The method for constructing the crack complexity index proposed in this embodiment can effectively determine the optimal weight coefficients.

[0151] It should be noted that the complexity of formation fractures in fractured formations and unconventional formations directly affects the safety and operation efficiency of each stage in the entire life cycle of oil and gas drilling and production. In fractured formations, the existence of fractures not only significantly reduces the equivalent rock mass mechanical strength of the formation, but also provides a flow channel for the invasion of working fluids into the formation, thereby changing the formation pore pressure distribution, intensifying risks such as lost circulation and wellbore instability, resulting in an increase in non-productive time during the drilling process and an aggravation of reservoir damage, and even potentially causing the abandonment of the wellbore. In addition, in unconventional formations, the synergistic effect of crack aperture and crack intensity has a decisive impact on the formation of complex fracture networks in the formation.

[0152] In related technologies, interpreting fractures based on logging data has advantages such as high resolution, strong continuity, and strong reliability, and has become the main means to characterize fracture characteristic parameters. However, conventional fracture characteristic parameters mainly include fracture length, fracture width, fracture intensity, and fracture roughness, etc. Although these indicators can reflect the distribution law of formation fractures from a certain specific angle, there is no comprehensive indicator that can comprehensively characterize the complexity of formation fractures. In oil and gas drilling and production engineering, the fracture intensity parameter is mostly used as the only fracture characteristic parameter affecting the lost circulation risk prediction of fractured formations and the fracturing stimulation design of unconventional formations. This approach ignores the comprehensive influence of the synergistic effect of crack aperture and crack intensity on the complexity of formation fractures, resulting in insufficient accuracy of lost circulation risk prediction and reduced accuracy of the assessability of pressibility. Therefore, accurately characterizing the fracture complexity is of great significance for realizing the efficient development of oil and gas resources.

[0153] This embodiment can at least select the crack aperture and the crack intensity as the above-mentioned multiple basic crack parameters, fully consider the comprehensive influence of the crack aperture and the crack intensity on the crack complexity, and can construct a crack complexity index by means of objective and subjective combined weighting to quantitatively characterize the complexity of formation fractures, providing technical support for the lost circulation risk prediction of fractured formations and the optimization design of fracturing stimulation transformation of unconventional formations.

[0154] Specifically, as Figure 3 shown, in other methods for constructing the crack complexity index proposed in this embodiment, the following steps can be included:

[0155] In the selected target oilfield area, the parameter value distributions of fracture aperture and fracture intensity are obtained through well logging data interpretation, and are respectively normalized to obtain the normalized parameter value distributions of fracture aperture and fracture intensity;

[0156] Based on the normalized parameter value distributions of fracture aperture and fracture intensity, the objective weight coefficients of fracture aperture and fracture intensity are calculated using the entropy weight method;

[0157] The subjective weight coefficients of fracture aperture and fracture intensity are calculated using the analytic hierarchy process;

[0158] The Spearman rank correlation coefficient is used to conduct a consistency test on the weight coefficients obtained by the objective method and the subjective method;

[0159] After passing the consistency test, the combined weighting method is used to determine the optimal weight coefficients of fracture aperture and fracture intensity;

[0160] The linear weighting method is used to fuse the two parameters of fracture aperture and fracture intensity to construct a fracture complexity index for characterizing the complexity of fractures.

[0161] Furthermore, the well logging data interpretation method includes, but is not limited to, using data interpretations such as dual laterolog resistivity logging and imaging logging to obtain fracture aperture and fracture intensity.

[0162] Furthermore, the above-mentioned calculation of the objective weight coefficients of fracture aperture and fracture intensity using the entropy weight method based on the normalized parameter value distributions of fracture aperture and fracture intensity includes:

[0163] Calculate the relative proportion of each sample in fracture aperture and fracture intensity. Divide the normalized value of each sample of fracture aperture and fracture intensity by the sum of the normalized values of all samples of this index respectively, and the index proportion of fracture aperture and fracture intensity can be obtained.

[0164] The level of information entropy value reflects the amount of effective information provided by the index data. The information entropy is used to evaluate the variation degree of the data distribution of fracture aperture and fracture intensity. The smaller the entropy value, the greater the variation degree of the index value, the more information it provides, and the greater the role it can play in the comprehensive evaluation.

[0165] The difference coefficient reflects the contribution degree of fracture aperture and fracture intensity to the comprehensive evaluation of fracture complexity. Calculate the difference coefficients of fracture aperture and fracture intensity according to the entropy value. The greater the difference coefficient, the greater the influence of this index on the comprehensive evaluation.

[0166] Calculate the proportion of the difference coefficient in the total sum of all index difference coefficients, which is the objective weight coefficient of fracture aperture and fracture intensity. The greater the weight, the higher the importance of this index in the comprehensive evaluation.

[0167] Furthermore, the analytic hierarchy process is used to calculate the subjective weight coefficients of the fracture aperture and fracture strength, including:

[0168] Taking the numerical scale method as the reference basis, expert decision-making is used to rank the importance of the fracture aperture and fracture strength, determine the relative importance of the two parameters, and construct a complementary judgment table.

[0169] The complementary judgment table is transformed into a complementary judgment matrix. Based on the complementary judgment matrix, the subjective weight coefficients of the fracture aperture and fracture strength are calculated by, including but not limited to, the sum-product method, the square root method, the eigenvalue method, the logarithmic least squares method, and the least squares method, etc.

[0170] Furthermore, when the expert decision-making ranks the importance of the fracture aperture and fracture strength, it needs to be determined according to the formation type of the oilfield and the engineering application scenarios. The application scenarios include but are not limited to the prediction of lost circulation risk in fractured formations, the design of stimulation for unconventional formations, etc.

[0171] The Spearman rank correlation coefficient is used to conduct a consistency test on the weight coefficients obtained by the objective method and the subjective method, including:

[0172] The Spearman rank correlation coefficient can be used to reflect the correlation degree between the objective weighting method and the subjective weighting method. The Spearman rank correlation coefficient is in the range of (0, 1], indicating that the objective and subjective weighting methods are consistent and can be combined for weighting.

[0173] Furthermore, after the consistency test is passed, a combined weighting method is used to determine the optimal weight coefficients of the fracture aperture and fracture strength, including:

[0174] Select a certain combined weighting method, including but not limited to the addition synthesis method, the multiplication synthesis method, and the range maximization combined weighting, etc., to obtain the optimal weight coefficients of the fracture aperture and fracture strength.

[0175] Furthermore, the linear weighting method is used to fuse the two parameters of the fracture aperture and fracture strength to construct a fracture complexity index for characterizing the complexity of fractures, including:

[0176] After determining the optimal weight coefficients of the fracture aperture and fracture strength, the linear weighting method is used to fuse them to obtain the fracture complexity index.

[0177] This embodiment fully considers the comprehensive influence of the fracture aperture and fracture strength on the complexity of formation fractures. The constructed fracture complexity index not only reflects the fracture distribution degree but also reflects the internal structure differences of fractures, thereby realizing the quantitative characterization of fracture complexity. Using this index can significantly improve the prediction accuracy of lost circulation risk and the evaluation accuracy of compressibility.

[0178] To better illustrate the above execution process, Example 2 is presented in this embodiment for illustration.

[0179] Step 1: Select a certain oilfield block. This block is a typical fractured formation with well-developed formation fractures, and the temporal and spatial distribution of fractures has strong complexity and non-uniformity. Accurately predicting the fracture complexity is crucial for avoiding leakage events during the subsequent drilling process. Obtain the fracture aperture and fracture intensity through well logging data interpretation, including but not limited to using dual laterolog resistivity logging, imaging logging and other data interpretations. In this embodiment, the fracture aperture and fracture intensity data are obtained through imaging logging interpretation and are respectively normalized:

[0180]

[0181] Among them, FA n is the normalized fracture aperture; FA is the fracture aperture; FA min is the minimum value of the fracture aperture; FA max is the maximum value of the fracture aperture; FS n is the normalized fracture intensity; FS is the fracture intensity; FS min is the minimum value of the fracture intensity; FS max is the maximum value of the fracture intensity.

[0182] Step 2: Use the entropy weight method to calculate the index weights, information entropy values and difference coefficients of the fracture aperture and fracture intensity in sequence, and obtain the objective weight coefficients of the fracture aperture and fracture intensity. Specifically, the calculation process is as follows:

[0183] S1: Calculate the index weights of the fracture aperture and fracture intensity:

[0184]

[0185] Among them, j is the serial number of the fracture basic parameter, i is the serial number of the normalized parameter value in the normalized parameter value distribution of the fracture basic parameter, F ij is the i-th normalized parameter value of the j-th fracture basic parameter, A ij is the weight corresponding to the i-th normalized parameter value of the j-th fracture basic parameter.

[0186] S2: Calculate the information entropy values of the fracture aperture and fracture intensity:

[0187]

[0188] Among them, E j is the information entropy value corresponding to the j-th fracture basic parameter.

[0189] S3: Calculate the difference coefficients of the fracture aperture and fracture intensity:

[0190] dj = 1 - E j .

[0191] Among them, d j is the difference coefficient corresponding to the basic parameters of the j-th crack.

[0192] S4. Calculate the objective weight coefficients of the crack opening and the crack strength:

[0193]

[0194] Among them, Q j is the objective weight coefficient of the basic parameters of the j-th crack.

[0195] This embodiment includes two basic crack parameters, namely the crack opening and the crack strength. Substituting the crack opening and the crack strength obtained from the imaging logging interpretation of the oilfield block into the above formula, the relevant data corresponding to the crack opening and the crack strength as shown Figure 4 can be determined.

[0196] Step 3. Use expert decision-making to rank the importance of the crack opening and the crack strength, determine the relative importance of the two parameters, and use the analytic hierarchy process to calculate the subjective weight coefficients of the crack opening and the crack strength. Specifically, the calculation process is as follows:

[0197] S1. Use numerical scaling method as the reference basis for expert decision-making, and use expert decision-making to rank the importance of the crack opening and the crack strength, and determine the relative importance of the two parameters, specifically as shown Figure 5 .

[0198] S2. Determine the scale value according to the relative importance of the crack opening and the crack strength, and construct a complementary judgment table according to the order of the importance of the characteristic parameters, specifically as shown Figure 6 .

[0199] S3. Convert the complementary judgment table into a complementary judgment matrix:

[0200]

[0201] S4. Based on the complementary judgment matrix, calculate the weights of the crack opening and the crack strength, including but not limited to using the sum-product method, the square root method, the eigenvalue method, the logarithmic least squares method, and the least squares method, etc. In this embodiment, the sum-product method is used.

[0202] Specifically, the calculation process is as follows:

[0203] Normalize each column of the complementary judgment matrix:

[0204]

[0205] Among them, is the normalized judgment matrix, and b ij is the element in the i-th row and j-th column of the complementary judgment matrix, and b kj is the element in the k-th row and j-th column of the complementary judgment matrix.

[0206] After normalization, each column of the judgment matrix is summed by row. That is:

[0207]

[0208] Among them, is the value obtained by summing a certain row of the normalized judgment matrix by row.

[0209] Normalize the vector That is:

[0210]

[0211] Obtain the optimal weight coefficients of crack opening and crack intensity.

[0212] This embodiment includes two basic crack parameters, crack opening and crack intensity. The sum-product method is used to calculate the subjective weight coefficients of crack opening and crack intensity. The calculated subjective weight coefficient of crack opening is 0.3333, and the subjective weight coefficient of crack intensity is 0.6667.

[0213] Step 4: Use the above Spearman rank correlation coefficient to conduct a consistency test on the weight coefficients obtained by the objective method and the subjective method. This embodiment includes two indicators, crack opening and crack intensity. Substitute the objective weight coefficients of crack opening and crack intensity obtained by the entropy weight method for the oil field block and the subjective weight coefficients of crack opening and crack intensity obtained by the analytic hierarchy process into the above formula, and solve to get ρ = 0.8823. Its value is in (0, 1], indicating that the objective and subjective weighting methods are consistent and can be combined for weighting.

[0214] Step 5: After passing the consistency test, combine the objective weight coefficient and the subjective weight coefficient, including but not limited to using the addition synthesis method, multiplication synthesis method, and range maximization combination weighting, etc. This embodiment uses the Lagrange function and Euclidean distance function to construct an optimization decision model to determine the optimal weight coefficients of crack opening and crack intensity. Specifically, the calculation method is:

[0215] S1 Establish an optimization decision model:

[0216]

[0217] Among them, W j is the optimal weight coefficient of the j-th basic crack parameter. P j is the subjective weight coefficient of the j-th basic crack parameter, and Q jis the objective weight coefficient of the basic parameters of the j-th crack. α and β are the grading coefficients of the subjective weight coefficient and the objective weight coefficient respectively.

[0218] Introduce the Euclidean distance function, D(P j , Q j ), and construct the consistency of the difference degree between the subjective and objective weights and the corresponding preference coefficients:

[0219]

[0220] This embodiment includes two indicators of crack aperture and crack intensity. Substitute the objective weight coefficients of crack aperture and crack intensity obtained by the entropy weight method for the oilfield block and the subjective weight coefficients of crack aperture and crack intensity obtained by the analytic hierarchy process into the above formula. The results of each weight coefficient are as Figure 7 shown.

[0221] Adopt the linear weighting method to fuse the two parameters of crack aperture and crack intensity, and construct the crack complexity index. The formula is as follows:

[0222] FCI = 0.3852×FA n + 0.6148×FS n .

[0223] Among them, FCI is the crack complexity index, and FA n is the normalized crack aperture, and FS is the normalized crack intensity.

[0224] Substitute the crack aperture and crack intensity obtained by well logging data interpretation into the above formula, and the crack complexity index profile can be obtained to characterize the complexity of the formation cracks of a single well.

[0225] This embodiment can achieve the following beneficial effects:

[0226] (1) The crack characteristic parameters are crucial in the field of oil and gas drilling and production. Based on well logging data, a crack complexity index is constructed. This index can quantitatively characterize the complexity of cracks, not only reflecting the crack distribution degree but also the internal structure differences of cracks, and has strong practicability;

[0227] (2) The crack complexity index is constructed by combining the objective entropy weight method and the subjective analytic hierarchy process for weighting, reducing the subjectivity of the analytic hierarchy process for weighting and also reducing the weight fluctuation caused by data changes in the entropy weight method, giving full play to the advantages of objective and subjective weighting methods, and ensuring the reliability of the crack complexity index method;

[0228] (3) The occurrence of lost circulation complex events during drilling is closely related to fracture characteristic parameters. Applying the fracture complexity index to the field of lost circulation risk prediction in fractured formations of drilling engineering can make up for the problem that the existing lost circulation risk prediction methods only consider fracture intensity as an index parameter, resulting in low prediction accuracy.

[0229] (4) Compressibility evaluation needs to consider fracture characteristic parameters. Fracture aperture and fracture intensity are crucial for the formation of complex fracture networks. Applying the fracture complexity index to the field of fracturing stimulation optimization design in oil and gas field development engineering can make up for the problem that the existing methods only consider a single factor of fracture intensity or fracture aperture to evaluate compressibility inadequately.

[0230] As Figure 8 shown, this embodiment proposes a device for constructing a fracture complexity index, which may include:

[0231] A data acquisition unit, configured to acquire the normalized parameter value distribution of multiple fracture basic parameters in a fractured formation, and the relative importance scale between every two fracture basic parameters; wherein, the normalized parameter value distribution of each fracture basic parameter is obtained by normalizing the parameter value distribution of the fracture basic parameter.

[0232] A first generation unit, configured to generate an objective weight coefficient for each fracture basic parameter based on the normalized parameter value distribution of each fracture basic parameter.

[0233] A second generation unit, configured to generate a subjective weight coefficient for each fracture basic parameter based on the relative importance scale between every two fracture basic parameters.

[0234] A combined weighting unit, configured to perform combined weighting on each fracture basic parameter according to the objective weight coefficient and the subjective weight coefficient of each fracture basic parameter, so as to generate an optimal weight coefficient for each fracture basic parameter.

[0235] An index construction unit, configured to perform linear weighting on each fracture basic parameter based on the optimal weight coefficient of each fracture basic parameter, so as to construct a fracture complexity index, and the fracture complexity index is used to characterize the fracture complexity of the fractured formation.

[0236] It should be noted that the processing procedures and the beneficial effects brought by the data acquisition unit, the first generation unit, the second generation unit, the combined weighting unit and the index construction unit can respectively refer to Figure 1 Steps S101 to S105 in, and will not be elaborated here.

[0237] Optionally, in the normalized parameter value distribution of each fracture basic parameter, it includes multiple normalized parameter values corresponding to different formation depths.

[0238] The first generation unit is further configured to:

[0239] For the normalized parameter value distribution of any basic crack parameter, based on each normalized parameter value in the normalized parameter value distribution, determine the index proportion data corresponding to the basic crack parameter. Based on the index proportion data corresponding to the basic crack parameter, determine the information entropy value corresponding to the basic crack parameter. Based on the information entropy value corresponding to the basic crack parameter, determine the coefficient of variation corresponding to the basic crack parameter;

[0240] Generate the objective weight coefficient of each basic crack parameter according to the coefficient of variation corresponding to each basic crack parameter.

[0241] Optionally, the first generating unit is further configured to:

[0242] Sum each normalized parameter value in the normalized parameter value distribution to obtain the total parameter;

[0243] Divide each normalized parameter value in the normalized parameter value distribution by the total parameter respectively to obtain the proportion corresponding to each normalized parameter value in the normalized parameter value distribution, and use it as the index proportion data corresponding to the basic crack parameter as a whole;

[0244] Optionally, the first generating unit is further configured to:

[0245] Input the index proportion data corresponding to the basic crack parameter into the established information entropy value model for calculation to obtain the information entropy value corresponding to the basic crack parameter; wherein, the information entropy value model is:

[0246]

[0247] E is the information entropy value corresponding to the basic crack parameter; m is the total number of proportions in the index proportion data, i is the serial number of the proportion in the index proportion data, and A i is the proportion with the serial number i in the index proportion data.

[0248] Optionally, the first generating unit is further configured to:

[0249] For the information entropy value corresponding to any basic crack parameter, calculate the difference obtained by subtracting the information entropy value from 1, and use the difference as the coefficient of variation corresponding to the basic crack parameter;

[0250] Optionally, the first generating unit is further configured to:

[0251] Sum the coefficients of variation corresponding to each basic crack parameter to obtain the total coefficient;

[0252] Divide the coefficient of variation corresponding to each basic crack parameter by the total coefficient respectively to obtain the coefficient ratio corresponding to each basic crack parameter, and use them as the objective weight coefficients of each basic crack parameter respectively.

[0253] Optionally, the second generating unit is further configured to:

[0254] Generate a corresponding complementary judgment matrix based on the relative importance scales between pairwise crack basic parameters; wherein, in each row element and each column element of the complementary judgment matrix, there is included a relative importance scale between one crack basic parameter and each crack basic parameter;

[0255] Normalize each column element in the complementary judgment matrix to obtain a normalized matrix;

[0256] Generate a subjective weight coefficient for each crack basic parameter based on the normalized matrix.

[0257] Optionally, the second generating unit is further configured to:

[0258] For any column element in the complementary judgment matrix, calculate the sum of each relative importance scale in the column element to obtain a column scale sum value, divide each relative importance scale in the column element by the column scale sum value to obtain a normalized scale corresponding to each relative importance scale in the column element, and arrange the normalized scales corresponding to each relative importance scale according to the arrangement order of each relative importance scale in the column element to obtain a normalized column element corresponding to the column element;

[0259] Arrange the normalized column elements corresponding to each column element according to the arrangement order of each column element in the complementary judgment matrix to obtain a normalized matrix.

[0260] Optionally, each normalized row element in the normalized matrix corresponds one-to-one to each crack basic parameter; the second generating unit is further configured to:

[0261] For any normalized row element, sum each normalized scale in the normalized row element to obtain a row scale sum value;

[0262] Sum each row scale sum value to obtain a scale total;

[0263] Divide each row scale sum value by the scale total respectively to obtain a scale ratio corresponding to each row scale sum value, and use them as the subjective weight coefficients of the corresponding crack basic parameters respectively.

[0264] Optionally, the combined weighting unit is further configured to:

[0265] Perform a consistency test on the objective weight coefficient and the subjective weight coefficient of each crack basic parameter;

[0266] When it is determined that the consistency test passes, then perform combined weighting on each crack basic parameter according to the objective weight coefficient and the subjective weight coefficient of each crack basic parameter to generate an optimal weight coefficient for each crack basic parameter;

[0267] The above device further includes:

[0268] An optimization unit, configured to, after performing a consistency test on the objective weight coefficient and the subjective weight coefficient of each basic crack parameter, when it is determined that the consistency test fails, optimize the objective weight coefficient and the subjective weight coefficient of each basic crack parameter to obtain the optimized objective weight coefficient and the optimized subjective weight coefficient of each basic crack parameter;

[0269] Continue to perform a consistency test on the optimized objective weight coefficient and the optimized subjective weight coefficient of each basic crack parameter until the consistency test passes, and obtain the optimal objective weight coefficient and the optimal subjective weight coefficient of each basic crack parameter;

[0270] Perform combined weighting on each basic crack parameter based on the optimal objective weight coefficient and the optimal subjective weight coefficient of each basic crack parameter to generate the optimal weight coefficient of each basic crack parameter.

[0271] Optionally, the combined weighting unit is further configured to:

[0272] For the objective weight coefficient and the subjective weight coefficient of any basic crack parameter, calculate the coefficient difference between the objective weight coefficient and the subjective weight coefficient of the basic crack parameter, and calculate the square of the coefficient difference to obtain the squared coefficient difference corresponding to the basic crack parameter;

[0273] Sum up the squared coefficient differences of each basic crack parameter to obtain the corresponding sum of squared values, and use it as the relative distance between the subjective weight coefficient and the objective weight coefficient;

[0274] Based on the relative distance, construct a first equation and a second equation; wherein, the first equation is that the sum of the first preference degree coefficient of the subjective weight coefficient and the second preference degree coefficient of the objective weight coefficient is equal to 1, and the second equation is that the squared value of the target difference is equal to the relative distance, and the target difference is the difference between the first preference degree coefficient and the second preference degree coefficient;

[0275] Solve the first equation and the second equation to obtain the first preference degree coefficient and the second preference degree coefficient;

[0276] For the objective weight coefficient and the subjective weight coefficient of any basic crack parameter, perform weighted summation on the objective weight coefficient and the subjective weight coefficient of the basic crack parameter based on the first preference degree coefficient and the second preference degree coefficient to obtain the optimal weight coefficient corresponding to the basic crack parameter.

[0277] The device for constructing the fracture complexity index proposed in this embodiment can obtain the normalized parameter value distribution of multiple fracture basic parameters in a fractured formation, as well as the relative importance scale between every two fracture basic parameters; wherein, the normalized parameter value distribution of each fracture basic parameter is obtained by normalizing the parameter value distribution of the fracture basic parameter. Based on the normalized parameter value distribution of each fracture basic parameter, an objective weight coefficient of each fracture basic parameter is generated; and, based on the relative importance scale between every two fracture basic parameters, a subjective weight coefficient of each fracture basic parameter is generated. According to the objective weight coefficient and the subjective weight coefficient of each fracture basic parameter, combined weighting is performed on each fracture basic parameter to generate an optimal weight coefficient of each fracture basic parameter. Based on the optimal weight coefficient of each fracture basic parameter, linear weighting is performed on each fracture basic parameter to construct the fracture complexity index. The fracture complexity index constructed in this embodiment can be used to comprehensively characterize the fracture complexity of a fractured formation.

[0278] The device for constructing the fracture complexity index in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0279] The embodiment of the present invention also provides a computer device having the above Figure 8 shown device for constructing the fracture complexity index.

[0280] Please refer to Figure 9 , a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. The computer device includes: one or more processors 10, a memory 20, and an interface for connecting each component, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 9 Taking one processor 10 as an example in

[0281] The processor 10 may be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 may further include a hardware chip. The above-mentioned hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device may be a complex programmable logic device, a field programmable gate array, a generic array logic, or any combination thereof.

[0282] Among them, the memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiments.

[0283] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the computer device and the like. In addition, the memory 20 may include a high-speed random access memory, and may further include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely disposed relative to the processor 10, and these remote memories may be connected to the computer device through a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0284] The memory 20 may include a volatile memory, such as a random access memory. The memory may also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive. The memory 20 may also include a combination of the above types of memories.

[0285] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0286] The embodiments of the present invention also provide a computer-readable storage medium. The method according to the embodiments of the present invention may be implemented in hardware, firmware, or may be implemented as computer code recorded on a storage medium, or may be implemented as computer code originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored in such software processed on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium may further include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component capable of storing or receiving software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0287] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; 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 they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a fracture complexity index, characterized in that Including: Obtaining the normalized parameter value distribution of multiple basic fracture parameters in a fractured formation, and the relative importance scale between every two of the basic fracture parameters; wherein, the normalized parameter value distribution of each basic fracture parameter is obtained by normalizing the parameter value distribution of the basic fracture parameter; Generating an objective weight coefficient for each basic fracture parameter based on the normalized parameter value distribution of each basic fracture parameter; and generating a subjective weight coefficient for each basic fracture parameter based on the relative importance scale between every two of the basic fracture parameters; Performing combined weighting on each basic fracture parameter according to the objective weight coefficient and the subjective weight coefficient of each basic fracture parameter to generate an optimal weight coefficient for each basic fracture parameter; Performing linear weighting on each basic fracture parameter based on the optimal weight coefficient of each basic fracture parameter to construct a fracture complexity index, and the fracture complexity index is used to characterize the fracture complexity of the fractured formation.

2. The method according to claim 1, wherein In the normalized parameter value distribution of each basic fracture parameter, it includes multiple normalized parameter values corresponding to different formation depths; The generating an objective weight coefficient for each basic fracture parameter based on the normalized parameter value distribution of each basic fracture parameter includes: For the normalized parameter value distribution of any one of the basic fracture parameters, based on each of the normalized parameter values in the normalized parameter value distribution, determining the index proportion data corresponding to the basic fracture parameter, based on the index proportion data corresponding to the basic fracture parameter, determining the information entropy value corresponding to the basic fracture parameter, and based on the information entropy value corresponding to the basic fracture parameter, determining the difference coefficient corresponding to the basic fracture parameter; Generating an objective weight coefficient for each basic fracture parameter according to the difference coefficient corresponding to each basic fracture parameter.

3. The method according to claim 2, wherein The determining the index proportion data corresponding to the basic fracture parameter based on each of the normalized parameter values in the normalized parameter value distribution includes: Summing each of the normalized parameter values in the normalized parameter value distribution to obtain a parameter total; Dividing each of the normalized parameter values in the normalized parameter value distribution by the parameter total respectively to obtain the proportion corresponding to each of the normalized parameter values in the normalized parameter value distribution, and taking the whole as the index proportion data corresponding to the basic fracture parameter; The determining the information entropy value corresponding to the basic fracture parameter based on the index proportion data corresponding to the basic fracture parameter includes: Inputting the index proportion data corresponding to the basic fracture parameter into the established information entropy value model for calculation to obtain the information entropy value corresponding to the basic fracture parameter.

4. The method according to claim 2, characterized in that, The determining the difference coefficient corresponding to the basic fracture parameter based on the information entropy value corresponding to the basic fracture parameter includes: For the information entropy value corresponding to any one of the basic fracture parameters, calculating the difference obtained by subtracting the information entropy value from 1, and taking the difference as the difference coefficient corresponding to the basic fracture parameter; Generating an objective weight coefficient for each of the basic crack parameters according to the difference coefficient corresponding to each of the basic crack parameters, includes: Summing the difference coefficients corresponding to each of the basic crack parameters to obtain a total coefficient; Dividing the difference coefficient corresponding to each of the basic crack parameters by the total coefficient respectively to obtain a coefficient ratio corresponding to each of the basic crack parameters, and respectively using them as the objective weight coefficients for each of the basic crack parameters.

5. The method according to claim 1, wherein Generating a subjective weight coefficient for each of the basic crack parameters based on the relative importance scale between every two of the basic crack parameters, includes: Generating a corresponding complementary judgment matrix based on the relative importance scale between every two of the basic crack parameters; wherein, in each row element and each column element of the complementary judgment matrix, it includes the relative importance scale between one of the basic crack parameters and each of the basic crack parameters respectively; Normalizing each column element in the complementary judgment matrix respectively to obtain a normalized matrix; Generating a subjective weight coefficient for each of the basic crack parameters based on the normalized matrix.

6. The method according to claim 5, characterized in that The step of normalizing each column element in the complementary judgment matrix respectively to obtain a normalized matrix, includes: For any column element in the complementary judgment matrix, calculating the sum of each of the relative importance scales in the column element to obtain a column scale sum value, dividing each of the relative importance scales in the column element by the column scale sum value respectively to obtain a normalized scale corresponding to each of the relative importance scales in the column element, and arranging the normalized scales corresponding to each of the relative importance scales in the order of arrangement of each of the relative importance scales in the column element to obtain a normalized column element corresponding to the column element; Arranging the normalized column elements corresponding to each column element in the order of arrangement of each column element in the complementary judgment matrix to obtain the normalized matrix.

7. The method according to claim 6, wherein Each normalized row element in the normalized matrix corresponds one by one to each of the basic crack parameters; Generating a subjective weight coefficient for each of the basic crack parameters based on the normalized matrix, includes: For any normalized row element, summing each of the normalized scales in the normalized row element to obtain a row scale sum value; Summing each of the row scale sum values to obtain a total scale value; Dividing each of the row scale sum values by the total scale value respectively to obtain a scale ratio corresponding to each of the row scale sum values, and respectively using them as the subjective weight coefficients corresponding to the corresponding basic crack parameters.

8. The method according to claim 1, characterized in that Combining weights for each of the basic crack parameters according to the objective weight coefficient and subjective weight coefficient of each of the basic crack parameters to generate an optimal weight coefficient for each of the basic crack parameters, includes: Performing a consistency test on the objective weight coefficient and subjective weight coefficient of each of the basic crack parameters; When it is determined that the consistency test passes, then based on the objective weight coefficient and the subjective weight coefficient of each of the basic crack parameters, combined weighting is performed on each of the basic crack parameters to generate the optimal weight coefficient of each of the basic crack parameters; After performing the consistency test on the objective weight coefficient and the subjective weight coefficient of each of the basic crack parameters, the method further includes: When it is determined that the consistency test fails, optimize the objective weight coefficient and the subjective weight coefficient of each of the basic crack parameters to obtain the optimized objective weight coefficient and the optimized subjective weight coefficient of each of the basic crack parameters; Continue to perform the consistency test on the optimized objective weight coefficient and the optimized subjective weight coefficient of each of the basic crack parameters until the consistency test passes, and obtain the optimal objective weight coefficient and the optimal subjective weight coefficient of each of the basic crack parameters; Based on the optimal objective weight coefficient and the optimal subjective weight coefficient of each of the basic crack parameters, combined weighting is performed on each of the basic crack parameters to generate the optimal weight coefficient of each of the basic crack parameters.

9. The method according to any one of claims 1 to 8, characterized in that, The combined weighting of each of the basic crack parameters based on the objective weight coefficient and the subjective weight coefficient of each of the basic crack parameters to generate the optimal weight coefficient of each of the basic crack parameters includes: For the objective weight coefficient and the subjective weight coefficient of any one of the basic crack parameters, calculate the coefficient difference between the objective weight coefficient and the subjective weight coefficient of the basic crack parameter, and calculate the square of the coefficient difference to obtain the square of the coefficient difference corresponding to the basic crack parameter; Sum the squares of the coefficient differences of each of the basic crack parameters to obtain the corresponding sum of squares value, and use it as the relative distance between the subjective weight coefficient and the objective weight coefficient; Based on the relative distance, construct a first equation and a second equation; wherein, the first equation is that the sum of the first preference degree coefficient of the subjective weight coefficient and the second preference degree coefficient of the objective weight coefficient is equal to 1, and the second equation is that the square value of the target difference is equal to the relative distance, and the target difference is the difference between the first preference degree coefficient and the second preference degree coefficient; Solve the first equation and the second equation to obtain the first preference degree coefficient and the second preference degree coefficient; For the objective weight coefficient and the subjective weight coefficient of any one of the basic crack parameters, based on the first preference degree coefficient and the second preference degree coefficient, perform weighted summation on the objective weight coefficient and the subjective weight coefficient of the basic crack parameter to obtain the optimal weight coefficient corresponding to the basic crack parameter.

10. A device for constructing a fracture complexity index, characterized in that, including: A data acquisition unit, configured to acquire the normalized parameter value distribution of multiple basic crack parameters in a fractured formation, and the relative importance scale between every two of the basic crack parameters; wherein, the normalized parameter value distribution of each of the basic crack parameters is obtained by normalizing the parameter value distribution of the basic crack parameter; A first generation unit, configured to generate the objective weight coefficient of each of the basic crack parameters based on the normalized parameter value distribution of each of the basic crack parameters; A second generation unit, configured to generate a subjective weight coefficient for each of the basic fracture parameters based on the relative importance scales between every two of the basic fracture parameters; A combined weighting unit, configured to perform combined weighting on each of the basic fracture parameters according to the objective weight coefficient and the subjective weight coefficient of each of the basic fracture parameters, so as to generate an optimal weight coefficient for each of the basic fracture parameters; An index construction unit, configured to perform linear weighting on each of the basic fracture parameters based on the optimal weight coefficient of each of the basic fracture parameters, so as to construct a fracture complexity index, where the fracture complexity index is used to characterize the fracture complexity of the fractured formation.

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