A method and system for determining balanced injection-production parameters of a reservoir based on multi-algorithm analysis

Abstract

The present invention discloses a method for determining balanced injection-production parameters of a reservoir based on multi-algorithm analysis. S1: Establish a simulation model of balanced injection-production influencing parameters through reservoir numerical simulation software; S2: Obtain the results of the prediction model through numerical simulation operations, screen out the balanced injection-production influencing parameters, and summarize the laws of various factors under different reservoir conditions; S3: Conduct a correlation analysis on reservoir data; S4: Determine the main controlling factors and their weights of balanced injection-production under different reservoir conditions. A system is also disclosed. By conducting a correlation analysis on reservoir data and determining the main controlling factors and their weights of balanced injection-production under different reservoir conditions, numerous influencing factors can be comprehensively analyzed, and the influencing factors under different reservoir conditions are considered. The core parameters affecting balanced injection-production are accurately screened out, and then their weights are determined, providing a key basis for formulating a scientific exploitation plan, effectively optimizing the reservoir exploitation process, improving the recovery rate, reducing energy waste, and thus improving the economic benefits of reservoir exploitation.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field development, and particularly to a method and system for determining reservoir balanced injection-production parameters based on multi-algorithm analysis. Background Art

[0002] During the process of reservoir production, balanced injection-production is crucial for improving the recovery factor and ensuring the long-term stable production of the reservoir. However, there are many and complex factors affecting balanced injection-production, including static geological parameters (such as permeability, porosity, etc.), dynamic development parameters (such as injection-production pressure difference, injection allocation volume, etc.), relevant fluid characteristic parameters, and controllable design elements. Currently, when determining balanced injection-production parameters, often only field experience can be relied on to select analysis factors, lacking a systematic and comprehensive method, making it difficult to accurately grasp the influence degree and mutual relationship of each factor, resulting in an insufficiently optimized production plan and affecting the reservoir production efficiency and economic benefits. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method and system for determining reservoir balanced injection-production parameters based on multi-algorithm analysis.

[0004] The purpose of the present invention is achieved through the following technical solutions: A method for determining reservoir balanced injection-production parameters based on multi-algorithm analysis includes the following steps:

[0005] S1: Establish a simulation model of balanced injection-production influence parameters through reservoir numerical simulation software;

[0006] S2: Obtain the prediction model results through numerical simulation operations, screen out the balanced injection-production influence parameters, and summarize the laws of various factors under different reservoir conditions to form each single-factor set;

[0007] S3: Conduct a correlation analysis on reservoir data;

[0008] S4: Determine the main control factors and their weights of balanced injection-production under different reservoir conditions.

[0009] Preferably, in step S3, the following steps are further included:

[0010] S31: Collect relevant data of the reservoir, and organize the data into a matrix form according to the corresponding relationship of the schemes,

[0011]

[0012] Wherein, is the reference sequence, which is the cumulative oil increment of the th group, are all comparison sequences, is the sample quantity of each influence parameter, is the quantity of influence parameters, is the th parameter value in the th comparison sequence, is the th parameter value in the reference sequence ; is the th parameter value in the comparison sequence ; ;

[0013] S32: Nondimensionalize the parameter sequence;

[0014] S33: Construct a two-way grey A-S type correlation matrix model;

[0015] S34: Sort each balanced injection-production parameter according to the correlation degree to determine the influence of each parameter.

[0016] Preferably, in step S31, the relevant data includes static geological parameters, dynamic development parameters, reservoir scheme parameters, drilling and completion process parameters, and corresponding cumulative oil increment data.

[0017] Preferably, in step S32, the specific steps for nondimensionalizing the parameter sequence are as follows:

[0018] S32.1: Initial value process the data,

[0019] ;

[0020] where is the initial value processing result of the th parameter value of the th influencing parameter, is the th original value of the th influencing parameter, is the th original value of the = 0, 1, 2,..., m;

[0021] The result after preprocessing the initial value is:

[0022]

[0023] where is the reference sequence after initial value processing, is the th parameter value after initial value processing in the comparison sequence , is the first comparison sequence after initial value processing, is the th comparison sequence after initial value processing, is the reference sequence after initialization in the th value after initialization processing, is the comparison sequence in the th value after parameter initialization processing;

[0024] S32.2: Perform starting point zeroing processing on the initialized sequence, and the formula for starting point zeroing is:

[0025] ;

[0026] Among them, is the th starting point zeroing value of the th influence parameter, is the th value after initialization processing of the th influence parameter. When = 1, ;

[0027] The sequence after starting point zeroing is:

[0028]

[0029] Among them, is the reference sequence after starting point zeroing, is the first comparison sequence after starting point zeroing, is the th comparison sequence after starting point zeroing, is the th value after starting point zeroing of the parameter in the comparison sequence after starting point zeroing, is the th value after starting point zeroing of the parameter in the comparison sequence after starting point zeroing, is the th value after starting point zeroing of the parameter in the reference sequence after starting point zeroing, is the reference sequence after starting point zeroing of the th value after starting point zeroing of the parameter.

[0030] Preferably, in step S33, the following steps are further included:

[0031] S33.1: Construct a grey relational model based on area difference;

[0032] S33.2: Introduce the sign function to construct a bidirectional grey relational model based on slope difference;

[0033] S33.3: Calculate the correlation coefficient according to the reference sequence and the comparison sequence.

[0034] Preferably, in step S33.1, calculate the reference sequence and the comparison sequence after zeroing the starting point and the area enclosed by the coordinate axes;

[0035] ;

[0036] ;

[0037] Among them, is the area enclosed by the reference sequence and the coordinate axes, is the area enclosed by the comparison sequence and the coordinate axes;

[0038] Use the trapezoidal integration method to calculate,

[0039] ;

[0040] ;

[0041] Among them, is the value of the sequence after zeroing the starting point at the th moment, is the number of samples of each influencing parameter, is a certain moment in the sequence;

[0042] Obtain the grey relational grade based on the area difference,

[0043] ;

[0044] Among them, is the th comparison sequence.

[0045] Preferably, in step S33.2, based on the sequences and calculate the slope of adjacent two points,

[0046] ;

[0047] ;

[0048] Among them, is the slope of the reference sequence in the th scheme, is the slope of the comparison sequence in the th scheme, , is the parameter value in the reference sequence ​Calculated sign function is the reference sequence and the parameter value in The calculated sign function, when it is ; when it is ; when it is ;

[0049] Obtain the grey relational grade based on the slope difference

[0050] ;

[0051] wherein, the association direction is determined by the value of

[0052] Preferably, in step S33.3, the formula for calculating the correlation coefficient according to the reference sequence and the comparison sequence is:

[0053] ;

[0054] wherein, the association direction is determined by the approaching value of and the value range is

[0055] Preferably, in step S4, the following steps are further included:

[0056] S41: Construct a hierarchical structure model, including an objective layer, a criterion layer and a scheme layer;

[0057] S42: Construct a judgment matrix by the correlation coefficient ratio method;

[0058] For the construction of the criterion layer judgment matrix, calculate the correlation coefficient between each criterion and the cumulative oil production according to grey relational analysis , ([[]] = 1, 2,[[[]] ,[[[]] ),[[[]]

[0059] ;

[0060] wherein,[[[]] is the relative importance of criterion[[[]] relative to criterion[[[]] ,[[[]] is the number of elements in the criterion layer;[[[]]

[0061] The formula for element[[[]] is:[[[]]

[0062] ;

[0063] wherein, is the correlation coefficient;

[0064] For the construction of the scheme layer judgment matrix, calculate the correlation coefficient between each scheme and the criterion under the criterion for each criterion in the criterion layer ( , = 1, 2, …, ), and construct the scheme layer judgment matrix under this criterion accordingly,

[0065] ;

[0066] Among them, is the relative importance of scheme relative to scheme , is the number of schemes under a certain criterion;

[0067] The calculation formula for element is:

[0068] ;

[0069] S43: Calculate the weight vector of the judgment matrix,

[0070] ;

[0071] ;

[0072] ;

[0073] Among them, is the weight of the th factor, is the order of the judgment matrix, is the element obtained after column normalization of the judgment matrix, is the element in the th row and th column of the original judgment matrix, is the sum of the elements in the th column of the original judgment matrix, is the intermediate value obtained by summing each row of the normalized judgment matrix, is the number of columns of the elements taken in the original judgment matrix;

[0074] S44: Calculate the maximum eigenvalue ,

[0075] ;

[0076] Among them, is the judgment matrix, is the weight vector, is the order of the judgment matrix, is the weight of the th parameter, is the weight vector obtained after normalizing the th row of the judgment matrix;

[0077] Calculate the consistency index ,

[0078] ;

[0079] Calculate the consistency ratio ,

[0080] ;

[0081] Among them, is the random consistency index, which can be obtained from the standard table according to the order of the judgment matrix. When , the judgment matrix has satisfactory consistency. If not, adjust the judgment matrix until the consistency is satisfied;

[0082] S45: Weights of the criterion layer Denoted as:

[0083] ;

[0084] Among them, the weights of each criterion layer are respectively denoted as , and the comprehensive weight of each parameter in the scheme layer is:

[0085] ;

[0086] Among them, is the weight vector of the th criterion, and the weights of the schemes under each criterion layer are respectively denoted as , is the weight vector of the th scheme under the

[0087] S46: Obtain the influence weights of each parameter according to the correlation degree and the operation results of the analytic hierarchy process and sort them.

[0088] A reservoir balanced injection-production parameter determination system based on multi-algorithm analysis includes the above-mentioned reservoir balanced injection-production parameter determination method based on multi-algorithm analysis, and also includes a model establishment and data processing module, a correlation algorithm module, an analytic hierarchy process module, and a parameter determination module;

[0089] The model establishment and data processing module is used to collect and organize various types of data, preprocess the data, obtain the prediction model results through numerical simulation operations, and screen the balanced injection-production influencing parameters;

[0090] The correlation algorithm module is used to analyze and calculate the influencing parameters, screen the control parameters, determine the parameter influence, and sort them;

[0091] The analytic hierarchy process module is used to construct a judgment matrix, calculate the weights and conduct a consistency test to determine the balanced injection-production factors and weights;

[0092] The parameter determination module is used to combine the correlation degree and weights, and determine the final reservoir balanced injection-production parameters based on the influence weights of the balanced injection-production factors and combined with actual requirements.

[0093] The present invention has the following advantages: By conducting a correlation analysis on reservoir data and determining the main controlling factors and their weights for balanced injection-production under different reservoir conditions, the present invention can comprehensively analyze numerous influencing factors and consider the influencing factors under different reservoir conditions, accurately screen out the core parameters affecting balanced injection-production, then determine their weights, and further provide a key basis for formulating a scientific exploitation plan, effectively optimizing the reservoir exploitation process, increasing the recovery rate, reducing energy waste, and thus improving the economic benefits of reservoir exploitation. Brief Description of the Drawings

[0094] Figure 1 is a schematic diagram of the process for determining reservoir balanced injection-production parameters;

[0095] Figure 2 is a schematic diagram of the analytic hierarchy process structure for balanced injection-production optimization;

[0096] Figure 3 is a schematic diagram of the process of the analytic hierarchy process;

[0097] Figure 4 is a schematic diagram of the comprehensive weights of balanced injection-production influencing factors. Detailed Embodiments

[0098] To make the purposes, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0099] Accordingly, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0100] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0101] It should be noted that like reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0102] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the inventive product is customarily placed during use, or the orientation or positional relationship commonly understood by those skilled in the art. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention. In addition, the terms "first", "second", etc. are only used for descriptive distinction and should not be construed as indicating or implying relative importance.

[0103] In the description of the present invention, it should also be noted that unless otherwise clearly specified and defined, the terms "set", "install", "connect", "couple" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0104] In this embodiment, as Figure 1 shown, a method for determining reservoir balanced injection-production parameters based on multi-algorithm analysis includes the following steps:

[0105] S1: Establish a simulation model of balanced injection-production influence parameters through reservoir numerical simulation software;

[0106] S2: Obtain the results of the prediction model through numerical simulation operations, screen out the parameters affecting balanced injection and production, and summarize the laws of various factors under different reservoir conditions to form each single-factor set. Specifically, the reservoir numerical simulation software establishes a simulation model of the parameters affecting balanced injection and production through existing methods without improving it here, that is, collect static geological parameters such as permeability, porosity, effective thickness of the reservoir, etc.; dynamic development parameters such as injection-production pressure difference, injection allocation volume, fluid flow rate, etc.; relevant fluid characteristic parameters such as crude oil viscosity, saturation, etc., use the numerical simulation software to perform a large number of scenario operations to obtain a large number of prediction model results, and deeply sort out these results to screen out the parameters closely related to balanced injection and production, such as fracture conductivity, fracture half-length, and permeability contrast, etc. At the same time, through the comparative analysis of the simulation results under different reservoir conditions, summarize the influence laws of various factors on balanced injection and production.

[0107] S3: Conduct a correlation analysis on the reservoir data;

[0108] S4: Determine the main controlling factors and their weights of balanced injection and production under different reservoir conditions. By conducting a correlation analysis on the reservoir data and determining the main controlling factors and their weights of balanced injection and production under different reservoir conditions, it is possible to comprehensively analyze numerous influencing factors and consider the influencing factors under different reservoir conditions, accurately screen out the core parameters affecting balanced injection and production, and then determine their weights, thereby providing a key basis for formulating a scientific exploitation plan, effectively optimizing the reservoir exploitation process, improving the recovery rate, reducing energy waste, and thus improving the economic benefits of reservoir exploitation.

[0109] Furthermore, in step S3, the following steps are also included:

[0110] S31: Collect the relevant data of the reservoir and organize the data into a matrix form according to the corresponding relationship of the scenarios,

[0111] ;

[0112] Among them, is the reference sequence, which is the cumulative oil increment of the rd group, are all comparison sequences, is the sample quantity of each influencing parameter, is the quantity of influencing parameters, is the rd comparison sequence the th parameter value in it, is the th parameter value in the reference sequence , is the th parameter value in the comparison sequence A parameter value; preferably, in step S31, the relevant data includes static geological parameters, dynamic development parameters, reservoir scheme parameters, drilling and completion process parameters, and corresponding cumulative oil increment data. Specifically, the static geological parameters include permeability ratio, porosity, permeability, effective thickness, and crude oil viscosity; the dynamic development parameters include injection volume, pressure, and injection-production pressure difference; the reservoir scheme parameters include development mode and well spacing; the drilling and completion process parameters include fracture half-length, fracture conductivity, fracture height, fracture width, and variable density perforation.

[0113] S32: Nondimensionalize the parameter sequence; specifically, since the dimensions and orders of magnitude of different parameters are different, different parameter sequences are not comparable. Therefore, the collected parameter sequence is nondimensionalized to eliminate the influence of dimensional differences on the analysis results. In this embodiment, the specific steps for nondimensionalizing the parameter sequence are as follows:

[0114] S32.1: Initialize the data,

[0115] ;

[0116] Among them, is the initialization result of the th parameter value of the th influencing parameter, is the th influencing parameter, th original value, is the th influencing parameter, = 0, 1, 2,..., m;

[0117] The result after pretreatment after initialization is:

[0118]

[0119] Among them, is the reference sequence after initialization, is the comparison sequence the th parameter value after initialization processing in, is the first comparison sequence after initialization, is the th comparison sequence after initialization, is the reference sequence after initialization the th parameter value after initialization processing in, is the comparison sequence the th parameter value after initialization processing in;

[0120] S32.2: Perform the starting point zeroing process on the initialized sequence. The formula for starting point zeroing is as follows:

[0121] ;

[0122] Among them, is the th starting point zeroing value of the th influence parameter, is the th value after the initialization process of the th influence parameter. When = 1, ;

[0123] The sequence after starting point zeroing is:

[0124]

[0125] Among them, is the reference sequence after starting point zeroing, is the first comparison sequence after starting point zeroing, is the th comparison sequence after starting point zeroing, is the value after starting point zeroing of the th parameter in the comparison sequence after starting point zeroing, is the value after starting point zeroing of the th parameter in the comparison sequence after starting point zeroing, is the th parameter value after starting point zeroing of the reference sequence after starting point zeroing, is the th parameter value after starting point zeroing of the reference sequence after starting point zeroing, is the value after starting point zeroing of the

[0126] S33: Construct a bidirectional grey A-S type correlation matrix model; further, it also includes the following steps:

[0127] S33.1: Construct a grey correlation model based on area difference; specifically, calculate the areas enclosed by the reference sequence and the comparison sequence with the coordinate axes after starting point zeroing;

[0128] ;

[0129] ;

[0130] Among them, is the area enclosed by the reference sequence and the coordinate axes, is the area enclosed by the comparison sequence and the coordinate axes;

[0131] Calculate using the trapezoidal integration method,

[0132] ;

[0133] ;

[0134] wherein, is the value of the sequence after initial point zeroing at the th moment, is the number of samples of each influencing parameter, is a certain moment in this sequence;

[0135] Obtain the grey relational grade based on the area difference,

[0136] ;

[0137] wherein, is the th comparison sequence.

[0138] S33.2: Introduce the sign function to construct a two-way grey relational model based on the slope difference; specifically, based on the sequences and , calculate the slope of adjacent two points,

[0139] ;

[0140] ;

[0141] wherein, is the slope of the reference sequence at the th scheme, is the slope of the comparison sequence at the th scheme, , is the sign function calculated from the parameter value in the reference sequence , is the sign function calculated from the parameter value in the reference sequence , when , ; when , ; when , ;

[0142] Obtain the grey relational grade based on the slope difference,

[0143] ;

[0144] Among them, the association direction is determined by the value of . That is to say, when the value of is closer to 1, it indicates that the consistency of the sequence in the slope change direction is higher and the association degree is stronger; when the value of

[0145] S33.3: Calculate the correlation coefficient according to the reference sequence and the comparison sequence. Specifically, the formula for calculating the correlation coefficient according to the reference sequence and the comparison sequence is:

[0146] ;

[0147] Among them, the association direction is determined by the approaching value of , and the value range is . When it approaches 0, it indicates that the reference sequence has a very weak association degree with the comparison sequence , almost no association. When it approaches 1, it indicates that the reference sequence has a stronger association degree with the comparison sequence

[0148] Table 1

[0149]

[0150] S34: Sort each balanced injection-production parameter according to the correlation degree to determine the influence of each parameter. Specifically, establish the correlation degree of each parameter to balanced injection-production through the grey correlation method to clarify the influence degree of each parameter. Here, the first 15 parameters are selected as important parameters for subsequent analysis, as shown in Table 2

[0151] Table 2

[0152]

[0153] In this embodiment, as Figure 3 shown, in step S4, the following steps are further included:

[0154] S41: Construct a hierarchical structure model, including an objective layer, a criterion layer, and a scheme layer; specifically, the objective layer determines the main control factors affecting balanced injection-production and the measurement standard of weights through the cumulative oil increment; the criterion layer is grouped according to static geological reservoir factors, dynamic development factors, reservoir scheme parameters, and drilling and completion process parameters; the scheme layer is composed of the core parameters screened by grey correlation analysis.

[0155] S42: Construct a judgment matrix through the correlation coefficient ratio method;

[0156] For the construction of the judgment matrix at the criterion level, the correlation coefficients between each criterion and the cumulative oil production are calculated according to the grey relational analysis , ([[]] i = 1, 2,[[[]] ,[[[]] ),[[[]]

[0157] ;[[[]]

[0158] Among them,[[[]] is the relative importance of criterion[[[]] relative to criterion[[[]] ,[[[]] is the number of elements at the criterion level;[[[]]

[0159] The calculation formula for element[[[]] is:[[[]]

[0160] ;[[[]]

[0161] Among them,[[[]] is the correlation coefficient;[[[]]

[0162] For the construction of the judgment matrix at the alternative level, the correlation coefficients between each alternative and the corresponding criterion are calculated for each criterion at the criterion level[[[]] ([[]] ,[[[]] j = 1, 2, …,[[[]] ), and the judgment matrix at the alternative level under this criterion is constructed accordingly,[[[]]

[0163] ;[[[]]

[0164] Among them,[[[]] is the relative importance of alternative[[[]] relative to alternative[[[]] ,[[[]] is the number of alternatives under a certain criterion;[[[]]

[0165] The calculation formula for element[[[]] is:[[[]]

[0166] ;[[[]]

[0167] S43: Calculate the weight vector of the judgment matrix,[[[]]

[0168] ;[[[]]

[0169] ;[[[]]

[0170] ;[[[]]

[0171] Among them,[[[]] is the weight of the[[[]] th factor,[[[]] is the order of the judgment matrix, is the element obtained after column normalization of the judgment matrix, is the th row and th column element in the original judgment matrix, is the sum of the elements in the th column of the original judgment matrix, is the intermediate value obtained by summing each row of the normalized judgment matrix, is the number of columns of the elements taken in the original judgment matrix;

[0172] S44: Calculate the maximum eigenvalue of the judgment matrix ,

[0173] ;

[0174] Among them, is the judgment matrix, is the weight vector, is the order of the judgment matrix, is the weight of the th parameter, is the weight vector obtained after row normalization of the th row of the judgment matrix;

[0175] Calculate the consistency index ,

[0176] ;

[0177] Calculate the consistency ratio ,

[0178] ;

[0179] Among them, is the random consistency index, which can be obtained from the standard table according to the order of the judgment matrix. When , the judgment matrix has satisfactory consistency. If not, adjust the judgment matrix until the consistency is satisfied;

[0180] S45: Weights of the criterion layer Denoted as:

[0181] ;

[0182] Among them, the weights of each criterion layer are respectively denoted as , and the comprehensive weight of each parameter in the scheme layer is:

[0183] ;

[0184] Among them, is the weight vector of the th criterion, and the weights of the solutions under each criterion layer are respectively denoted as , is the weight vector of the solution layer under the th criterion;

[0185] S46: According to the operation results of the correlation degree and the analytic hierarchy process, obtain the influence weights of each parameter and sort them. Specifically, as Figure 2 shown, the parameters included in each criterion layer are as follows:

[0186] B1: Static geological parameters: C1 permeability ratio (0.658), C2 porosity (0.429), C3 permeability (0.757), C4 effective thickness (0.488), and C5 crude oil viscosity (0.650);

[0187] B2: Dynamic development parameters: C6 injection volume (0.436), C7 pressure (0.385), and C8 injection-production pressure difference (0.456);

[0188] B3: Reservoir scheme parameters: C9 development mode (0.561) and C10 well spacing (0.433);

[0189] B4: Drilling and completion technology parameters: C11 fracture half-length (0.557), C12 fracture conductivity (0.612), C13 fracture height (0.403), C14 fracture width (0.394), and C15 variable density perforation (0.388);

[0190] Then, use the correlation coefficient ratio method to construct a judgment matrix, specifically:

[0191] Criterion layer parameters: Permeability, permeability ratio, crude oil viscosity, effective thickness, and porosity; Parameter correlation degree: Permeability is 0.757, permeability ratio is 0.658, crude oil viscosity is 0.650, effective thickness is 0.488, and porosity is 0.429;

[0192] Construct a judgment matrix, and the matrix elements are the ratio of the correlation degree between the core parameter item and the target item under each criterion. The calculation formula for the correlation coefficient ratio is as follows:

[0193] ;

[0194] ;

[0195] Taking the static geological reservoir factors as an example: Permeability vs. permeability ratio: 0.757 / 0.658 ≈ 1.15; Crude oil viscosity vs. porosity: 0.650 / 0.429 ≈ 1.515,

[0196] The complete matrix is as follows:

[0197] ;

[0198] Calculate the weights of each factor. For the static geological reservoir factors in the B1 criterion layer, the weight calculation is as follows:

[0199] Sum the columns to calculate ;

[0200] Column 1: 1 + 0.869 + 0.859 + 0.645 + 0.567 = 3.940;

[0201] Column 2: 1.15 + 1 + 0.988 + 0.742 + 0.652 = 4.532;

[0202] Column 3: 1.165 + 1.012 + 1 + 0.751 + 0.660 = 4.588;

[0203] Column 4: 1.551 + 1.348 + 1.332 + 1 + 0.879 = 6.110;

[0204] Column 5: 1.764 + 1.533 + 1.515 + 1.137 + 1 = 6.949;

[0205] Based on the formula: Obtain the following matrix:

[0206] ;

[0207] Among them, the average row weight is:

[0208] 25.4% × 5 / 5 = 25.4%, corresponding to permeability;

[0209] 22.1% × 5 / 5 = 22.1%, corresponding to permeability ratio;

[0210] 21.8% × 5 / 5 = 21.8%, corresponding to crude oil viscosity;

[0211] 16.4% × 5 / 5 = 16.4%, corresponding to effective thickness;

[0212] 14.4% × 5 / 5 = 14.4%, corresponding to porosity.

[0213] For the dynamic development factors in the B2 criterion layer, the weight calculation is as follows:

[0214] Parameter correlation degree: injection volume is 0.436, injection-production pressure difference is 0.456, and pressure is 0.385. Based on this, construct a judgment matrix:

[0215] ;

[0216] Column normalization and weight calculation:

[0217] Column sum: 2.929, 2.800, 3.316

[0218] Normalized matrix:

[0219] ;

[0220] Weights: Injection volume ratio is 34.1%, injection-production pressure difference is 35.7%, and pressure is 30.2%.

[0221] Calculate the weights for the reservoir scheme factors in the B3 criterion layer, specifically:

[0222] Parameter correlation degree: Development mode is 0.561, well spacing is 0.433;

[0223] Construct the judgment matrix:

[0224] ;

[0225] Normalized matrix:

[0226] ;

[0227] Weight calculation: Development mode is 56.4%, well spacing is 43.6%, CR = 0 (naturally consistent for a second-order matrix).

[0228] Calculate the weights for the drilling and completion technology factors in the B4 criterion layer, specifically:

[0229] Judgment matrix:

[0230] ;

[0231] Normalized matrix:

[0232] ;

[0233] Weight calculation: Fracture conductivity is 26.0%, fracture half-length is 23.7%, fracture height is 17.1%, fracture width is 16.7%, and variable density perforation is 16.5%.

[0234] Conduct consistency test. Taking the static geological reservoir factors as an example, the consistency index , and according to the standard table, the random consistency index corresponding to the order of the judgment matrix is RI = 1.12, then = 0.00025 / 1.12 = 0.00022. When CR < 0.1, the judgment matrix has consistency and passes the test. Other consistency test situations are shown in Table 3,

[0235] Table 3

[0236]

[0237] As Figure 4 shown, finally calculate the comprehensive weight and sort. The specific results are shown in Table 4,

[0238] Table 4

[0239]

[0240] In this embodiment, a reservoir balanced injection-production parameter determination system based on multi-algorithm analysis includes the above-mentioned reservoir balanced injection-production parameter determination method based on multi-algorithm analysis, and further includes a model establishment and data processing module, a correlation algorithm module, an analytic hierarchy process module, and a parameter determination module;

[0241] The model establishment and data processing module is used to collect and sort various data, preprocess the data, obtain the prediction model results through numerical simulation operations, and screen the balanced injection-production influencing parameters;

[0242] The correlation algorithm module is used to analyze and calculate the influencing parameters, screen the control parameters, determine the parameter influence, and sort;

[0243] The analytic hierarchy process module is used to construct a judgment matrix, calculate the weight and conduct a consistency test to determine the balanced injection-production factors and weights;

[0244] The parameter determination module is used to combine the correlation degree and weight, and determine the final reservoir balanced injection-production parameters according to the influence weight of the balanced injection-production factors and in combination with actual requirements.

[0245] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for determining reservoir balanced injection-production parameters based on multi-algorithm analysis, characterized in that: It includes the following steps: S1: Establish a simulation model of balanced injection-production influence parameters through reservoir numerical simulation software; S2: Obtain the results of the prediction model through numerical simulation operations, screen out the balanced injection-production influence parameters, summarize the laws of various factors under different reservoir conditions, and form each single-factor set; S3: Conduct a correlation analysis on reservoir data; S4: Determine the main control factors and their weights of balanced injection-production under different reservoir conditions; In the step S3, the following steps are further included: S31: Collect relevant data of the reservoir, and organize the data into a matrix form according to the corresponding relationship of the plan, ； Among them, is the reference sequence, which is the cumulative oil increment amplitude of the group. are all comparison sequences, is the number of samples for each influencing parameter, is the number of influencing parameters, is the th parameter value in the th comparison sequence, is the th parameter value in the reference sequence is the th parameter value in the comparison sequence S32: Nondimensionalize the parameter sequence; S33: Construct a two-way grey A-S type correlation matrix model; S34: Sort each balanced injection-production parameter according to the correlation degree to determine the influence of each parameter; In the step S33, the following steps are further included: S33.1: Construct a grey correlation model based on area difference; S33.2: Introduction of sign function Construct a two-way grey relational model based on slope difference; S33.3: Calculate the correlation coefficient according to the reference sequence and the comparison sequence; In the step S33.2, based on the sequences and , calculate the slope between adjacent points. ； ； Among them, is the slope of the reference sequence in the th solution, is the slope of the comparison sequence in the th solution, , is the sign function calculated from the parameter value in the reference sequence . When is the sign function calculated from the parameter value in the reference sequence . When , ; when , ; when , ; Obtain the grey correlation degree based on slope difference, ； Among them, the association direction is determined by the value.

2. The method for determining reservoir balanced injection-production parameters based on multi-algorithm analysis according to claim 1, wherein: In the step S31, the relevant data includes static geological parameters, dynamic development parameters, reservoir plan parameters, drilling and completion process parameters, and corresponding cumulative oil increment data.

3. The method for determining reservoir balanced injection-production parameters based on multi-algorithm analysis according to claim 2, wherein: In the step S32, the specific steps for nondimensionalizing the parameter sequence are as follows: S32.1: Initialize the data, ； Among them, is the initialization result of the th parameter value of the th influencing parameter, is the th original value of the th influencing parameter, is the first original value of the th influencing parameter, = 0, 1, 2, ……, m; The result of preprocessing after initialization is: ； Among them, is the reference sequence after initialization, is the comparison sequence the value after initialization processing of the is the first comparison sequence after initialization, is the th comparison sequence after initialization, is the reference sequence after initialization the value after initialization processing of the is the comparison sequence the value after initialization processing of the parameter; S32.2: Conduct starting point zeroing processing on the initialized sequence, and the formula for starting point zeroing is: ； Among them, is the th starting point zeroing value of the th influence parameter, is the th value after initialization processing of the th influence parameter. When = 1, ; The sequence after starting point zeroing is: ； Among them, is the reference sequence after zeroing the starting point, is the first comparison sequence after zeroing the starting point, is the th comparison sequence after zeroing the starting point, is the value after zeroing the th parameter in the comparison sequence after zeroing the starting point, is the value after zeroing the th parameter in the comparison sequence after zeroing the starting point, is the th parameter value after zeroing the starting point, is the th parameter value after zeroing the starting point in the reference sequence after zeroing the starting point, is the 4. The method for determining reservoir balanced injection-production parameters based on multi-algorithm analysis according to claim 3, wherein: In the step S33.1, calculate the area enclosed by the reference sequence and the comparison sequence with the coordinate axis after initial zeroing at the starting point; ； ； Among them, is the reference sequence and the area enclosed by the coordinate axes, is the comparison sequence and the area enclosed by the coordinate axes; Calculate using the trapezoidal integration method, ； ； Among them, is the sequence after starting point zeroing at the value at the th moment, is the number of samples of each influencing parameter, is a certain moment in this sequence; Obtain the grey correlation degree based on area difference, ； Among them, is the th comparison sequence.

5. The method for determining reservoir balanced injection-production parameters based on multi-algorithm analysis according to claim 4, wherein: In the step S33.3, the formula for calculating the correlation coefficient according to the reference sequence and the comparison sequence is: ； Among them, the association direction is determined by the approach value, and the value range is .

6. The method for determining reservoir balanced injection-production parameters based on multi-algorithm analysis according to claim 5, characterized in that: In the step S4, the following steps are further included: S41: Construct a hierarchical structure model, including an objective layer, a criterion layer, and a scheme layer; S42: Construct a judgment matrix through the correlation coefficient ratio method; For the construction of the criterion layer judgment matrix, calculate the gray correlation coefficient between the criterion and the cumulative oil production , = 1, 2, , , ； Among them, is the criterion Relative to the criterion The relative importance of is the number of elements in the criterion layer; Element The calculation formula is as follows: ； For the construction of the scheme layer judgment matrix, for each criterion in the criterion layer, calculate the correlation coefficient between the scheme and this criterion , = 1, 2, …, , and thus construct the scheme layer judgment matrix under this criterion. ； Among them, is the plan Relative to the plan of relative importance is the number of plans under a certain criterion; Element The calculation formula is as follows: ； S43: Calculate the weight vector of the judgment matrix, ； ； ； Among them, is the weight of the th factor, is the order of the judgment matrix, is the element obtained after column normalization of the judgment matrix, is the th row and th column element in the original judgment matrix, is the sum of the elements in the th column of the original judgment matrix, is the intermediate value obtained by summing each row of the normalized judgment matrix, is the number of columns of the elements selected in the original judgment matrix; S44: Calculate the maximum eigenvalue of the judgment matrix , ； Among them, is the judgment matrix, is the weight vector, is the order of the judgment matrix, is the weight of the th parameter, is the weight vector obtained after normalizing the th row of the judgment matrix; Calculate the consistency index , ； Calculation of consistency ratio , ； Among them, is the random consistency index, which can be obtained from the standard table according to the order of the judgment matrix. When , the judgment matrix has satisfactory consistency. If not, adjust the judgment matrix until the consistency is satisfied; S45: Weights of the criterion layer Denoted as: ； Among them, the weights of each criterion layer are respectively denoted as , and the comprehensive weight of each parameter in the scheme layer is as follows: ； Among them, is the weight vector of the th criterion. The weights of the solutions under each criterion layer are respectively denoted as , is the weight vector of the solution layer under the th criterion; S46: Obtain the influence weights of each parameter and sort them according to the correlation degree and the operation results of the analytic hierarchy process.

7. A reservoir balanced injection-production parameter determination system based on multi-algorithm analysis, comprising the reservoir balanced injection-production parameter determination method based on multi-algorithm analysis according to any one of claims 1 to 6, characterized in that: It also includes a model establishment and data processing module, a correlation algorithm module, an analytic hierarchy process module, and a parameter determination module; The model establishment and data processing module is used to collect and organize various data, preprocess the data, obtain the results of the prediction model through numerical simulation operations, and screen out the balanced injection-production influence parameters; The correlation algorithm module is used to analyze and calculate the influence parameters, screen out the control parameters, determine the influence of the parameters, and sort them; The analytic hierarchy process module is used to construct a judgment matrix, calculate the weights and conduct a consistency test to determine the balanced injection-production factors and weights; The parameter determination module is used to combine the correlation degree and the weights, and determine the final reservoir balanced injection-production parameters according to the influence weights of the balanced injection-production factors and the actual requirements.

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