Oil reservoir balanced injection-production parameter determination method and system based on multi-algorithm analysis

Abstract

The invention discloses an oil reservoir balanced injection and production parameter determination method based on multi-algorithm analysis. The method comprises the steps that S1, a balanced injection and production influence parameter simulation model is established through oil reservoir numerical simulation software; s2, obtaining a prediction model result through numerical simulation operation, screening out balanced injection and production influence parameters, and summarizing various factor rules of different reservoir conditions; s3, correlation analysis is conducted on the oil reservoir data; and S4, determining balanced injection and production main control factors and weights thereof under different reservoir conditions. The invention further discloses a system. The method comprises the following steps: performing correlation analysis on oil reservoir data, and determining main control factors and weights of balanced injection and production under different reservoir conditions, thereby comprehensively analyzing numerous influence factors, considering the influence factors under different reservoir conditions, accurately screening out core parameters influencing balanced injection and production, and then determining the weights of the core parameters. A key basis is provided for formulating a scientific exploitation scheme, the oil reservoir exploitation process can be effectively optimized, the recovery efficiency is improved, energy waste is reduced, and therefore the economic benefits of oil reservoir exploitation are improved.

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 balanced injection-production parameters of an oil reservoir based on multi-algorithm analysis. Background Art

[0002] During the process of oil reservoir exploitation, balanced injection-production is crucial for improving the recovery factor and ensuring the long-term stable production of the oil 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, etc. 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, and it is difficult to accurately grasp the influence degree and mutual relationship of each factor, resulting in an insufficiently optimized exploitation plan and affecting the oil reservoir exploitation 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 balanced injection-production parameters of an oil reservoir based on multi-algorithm analysis.

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

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

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

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

[0008] S4: Determine the main controlling factors and their weights for 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 of the oil 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 group cumulative oil increment amplitude, are all comparison sequences, is the number of samples, is the number of influencing parameters, is the th comparison sequence in the th parameter value, is the reference sequence in the th parameter value, is the comparison sequence in the th parameter value;

[0013] S32: Nondimensionalize the parameter sequence;

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

[0015] S34: Sort the equilibrium injection-production parameters 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:

[0018] S32.1: Initialize the data,

[0019] ;

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

[0021] The result after preprocessing the initialization is:

[0022] ;

[0023] where, is the reference sequence after initialization, is the th comparison sequence in the is the 1st comparison sequence after initialization, is the th comparison sequence after initialization, 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. The formula for starting point zeroing is:

[0025] ;

[0026] Among them, is the th starting point zeroing value of the th influencing parameter, is the th value after initialization processing of the th influencing 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 comparison sequence after starting point zeroing in the th value after parameter starting point zeroing, is the comparison sequence after starting point zeroing of the th value after parameter starting point zeroing, is the reference sequence after starting point zeroing of the th value after parameter starting point zeroing.

[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 The area enclosed by the coordinate axes after starting point zeroing;

[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 starting point zeroing at the th moment, is the total number of influence parameters, is a certain moment in this 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 rd scheme, is the slope of the comparison sequence in the rd scheme, , is the sign function calculated from the parameter value in the reference sequence , Is the reference sequence The parameter value in The calculated sign function, when At that time, ; When At that time, ; When At that time, ;

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

[0050] ;

[0051] Among them, the correlation 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] Among them, the correlation direction is determined by The approaching value of .

[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] Among them, Is the criterion Relative to the criterion The relative importance of Is the number of elements in the criterion layer;

[0061] Element The calculation formula of is:

[0062] ;

[0063] Among them, Is the correlation coefficient;

[0064] For the construction of the judgment matrix at the solution layer, the correlation coefficients between each solution and the criterion are calculated for each criterion in the criterion layer ( , = 1, 2, …, ), and the judgment matrix at the solution layer under this criterion is constructed accordingly

[0065] ;

[0066] Among them, is the relative importance of solution relative to solution , is the number of solutions 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 rd 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 of the judgment matrix,

[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 , where is the th criterion's weight vector, and the comprehensive weight of each parameter in the scheme layer is:

[0085] ;

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

[0087] S46: Obtain the influence weights of each parameter according to the calculation results of the correlation degree and 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 sort various data, preprocess the data, obtain the prediction model results through numerical simulation operations, and screen the balanced injection-production influence 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 the actual requirements.

[0093] The present invention has the following advantages: By analyzing the correlation degree of reservoir data and determining the main control factors and their weights of 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 purpose, 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 drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Usually, the components of the embodiments of the present invention described and shown here can be arranged and designed in various different configurations.

[0099] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents the 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 refer to like items in the following figures, and thus, once an item is defined in one figure, it will not be necessary to further define and explain it in subsequent figures.

[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 product of the present invention is usually 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 to the present invention. In addition, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0103] In the description of the present invention, it should also be noted that unless otherwise clearly defined and limited, the terms "set", "installed", "connected", "connected" 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 situations.

[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 sets of single factors. Specifically, the reservoir numerical simulation software establishes a simulation model for 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 results of the prediction model, 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. 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 for 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 for 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, then determine their weights, and further provide 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] Further, 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 number of samples, is the number of influencing parameters, is the rd comparison sequence the th parameter value in 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 contrast, 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, the first original value, = 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 in the th parameter value after initialization processing, is the first comparison sequence after initialization, is the th comparison sequence after initialization, is the reference sequence after initialization in the th parameter value after initialization processing, is the comparison sequence in the th parameter value after initialization processing;

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

[0121] ;

[0122] where, 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, ;

[0123] The sequence after starting point zeroing is:

[0124] ;

[0125] where, 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 in the reference sequence after starting point zeroing, is the reference sequence after starting point zeroing, is the th parameter in the reference sequence after starting point zeroing.

[0126] S33: Construct a two-way 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 after starting point zeroing and the coordinate axes;

[0128] ;

[0129] ;

[0130] where, 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] where, is the value of the sequence after starting point zeroing at the th moment, is the total number of influence parameters, is a certain moment in the sequence;

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

[0136] ;

[0137] where, is the th comparison sequence.

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

[0139] ;

[0140] ;

[0141] where, 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 slope difference,

[0143] ;

[0144] Among them, the correlation direction is determined by the value of, that is, 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 correlation degree is stronger; when the value of is closer to 0, it indicates that the difference of the sequence in the slope change direction is larger and the correlation degree is weaker.

[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 correlation direction is determined by the approaching value of, and the value range is , when approaching 0, it indicates that the reference sequence and the comparison sequence have extremely weak correlation and are almost uncorrelated. When approaching 1, it indicates that the reference sequence and the comparison sequence have stronger correlation. Specifically, the correlation degrees of each factor with the incremental oil production amplitude calculated according to the above method are shown in Table 1,

[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 with 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 incremental oil production amplitude; 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 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 criterion layer judgment matrix, the correlation coefficients between each criterion and the cumulative oil production are calculated according to the grey relational analysis , ([[]]END]] = 1, 2, , ),

[0157] ;

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

[0159] The calculation formula for element is:

[0160] ;

[0161] Among them, is the correlation coefficient;

[0162] For the construction of the scheme layer judgment matrix, for each criterion in the criterion layer, the correlation coefficients between each scheme and the criterion are calculated ([[]]END]] , = 1, 2,..., ), and the scheme layer judgment matrix under this criterion is constructed accordingly,

[0163] ;

[0164] Among them, is the relative importance of scheme relative to scheme , is the number of schemes 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 th parameter weight, 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 , where is the th criterion weight vector, and the comprehensive weight of each parameter in the scheme layer is:

[0183] ;

[0184] Among them, the weights of the solutions under each criterion layer are respectively denoted as , being 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 process 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, porosity is 0.429;

[0192] Construct a judgment matrix, and the matrix elements are the ratio of the correlation degree between the core parameter items under each criterion and the target item. 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 row average weights are:

[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 is 34.1%, injection-production pressure difference is 35.7%, and pressure is 30.2%.

[0221] Calculate the weights of 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 a 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 of 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 a consistency test. Taking the static geological reservoir factors as an example, the consistency index , 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. The 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, and 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, and 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 balanced injection-production factor influence weight and actual requirements.

[0245] Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described 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 and production parameters based on multi-algorithm analysis, characterized in that: The following steps are involved: S1: Establish a simulation model of balanced injection and production influencing parameters through reservoir numerical simulation software; S2: Obtain the prediction model results 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 a set of single factors; S3: Perform correlation analysis on reservoir data; S4: Determine the main controlling factors and their weights for balanced injection and production under different reservoir conditions; The step S3 further includes the following steps: S31: Collect relevant data of oil reservoirs and organize the data into a matrix form according to the corresponding relationship of the scheme. ； in, is the reference series, The cumulative oil increase of the group, All are comparison sequences. is the sample size, is the number of influencing parameters, For the Comparison sequence The parameter values, For reference sequence The parameter values, To compare the sequence The parameter values; S32: non-dimensionalize the parameter sequence; S33: construct a bidirectional grey AS type correlation matrix model; S34: Sort the balanced injection-production parameters according to the correlation degree to determine the influence of each parameter.

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

3. The method for determining reservoir balanced injection and production parameters based on multi-algorithm analysis according to claim 2, characterized in that: In step S32, the specific steps of dimensionlessizing the parameter sequence are: S32.1: Initialize the data. ； in, For the The first parameter that affects The result of initializing the parameter values ​​is: For the The first parameter that affects The original value, For the The first original value of the influencing parameter, =0, 1, 2, ..., m; The result of preprocessing after initialization is: ； in, is the reference sequence after initialization, To compare the sequence The The value after the parameter initialization processing, is the first comparison sequence after initialization, For the After initialization, the comparison sequence is is the reference sequence after initialization The After initialization, To compare the sequence The The value after the parameter initialization processing; S32.2: Perform zeroing of the initial point on the sequence after initialization. The formula for zeroing of the initial point is: ； in, For the The first parameter that affects The starting point zero value, For the The first parameter that affects The initial value is =1, ; The sequence after the initial point is zeroed is: ； in, is the reference sequence after the initial point is zeroed, is the comparison sequence after the first starting point is zeroed, For the A comparison sequence after the starting point is zeroed, Compare the sequences after zeroing the starting point The The value of the parameter after zeroing. Compare the sequences after zeroing the starting point No. The value of the parameter after zeroing. The reference sequence after zeroing the starting point No. The initial value of each parameter is zeroed.

4. The method for determining reservoir balanced injection and production parameters based on multi-algorithm analysis according to claim 3 is characterized in that: The step S33 further includes the following steps: S33.1: Constructing grey relational model based on area difference; S33.2: Introducing symbolic functions Construct a bidirectional grey relational model based on slope difference; S33.3: Calculate the correlation coefficient based on the reference series and the comparison series.

5. The method for determining reservoir balanced injection and production parameters based on multi-algorithm analysis according to claim 4, characterized in that: In step S33.1, the reference sequence is calculated and compare sequences The area enclosed by the coordinate axis after the initial point is zeroed; ； ； in, For reference sequence The area enclosed by the coordinate axes, To compare the sequence The area enclosed by the coordinate axes; Using the trapezoidal integration method, ； ； in, The sequence after zeroing the starting point In the The value of the moment, is the total number of influencing parameters, for a moment in the sequence; Get the grey relational degree based on area difference, ； in, For the A comparison sequence.

6. The method for determining reservoir balanced injection and production parameters based on multi-algorithm analysis according to claim 5, characterized in that: In step S33.2, based on the sequence and , calculate the slope of two adjacent points, ； ； in, The reference sequence is The slope of the scheme, To compare the sequence The slope of the scheme, , For reference sequence Medium parameter value The symbolic function of the calculation, For reference sequence Medium parameter value The symbolic function of the calculation is hour, ;when hour, ;when hour, ; Get the grey relational degree based on slope difference, ； The direction of association is The value of is determined.

7. The method for determining reservoir balanced injection and production parameters based on multi-algorithm analysis according to claim 6, characterized in that: In step S33.3, the formula for calculating the correlation coefficient based on the reference sequence and the comparison sequence is: ； The direction of association is The approximate value of is determined, and the value range is .

8. The method for determining reservoir balanced injection and production parameters based on multi-algorithm analysis according to claim 7, characterized in that: The step S4 further includes the following steps: S41: Construct a hierarchical model, including the goal layer, the criterion layer, and the solution layer; S42: Constructing a judgment matrix by using the correlation coefficient ratio method; For the construction of the criterion layer judgment matrix, the correlation coefficient between each criterion and the cumulative oil production is calculated based on the grey correlation analysis. , ( = 1, 2, , ), ； in, For the criteria Relative to the criteria The relative importance of is the number of elements in the criterion layer; element The calculation formula is: ； in, is the correlation coefficient; For the construction of the judgment matrix at the scheme level, for each criterion at the criterion level, the correlation coefficient between each scheme and the criterion is calculated. ( , = 1, 2, …, ), in order to construct the scheme-level judgment matrix under this criterion, ； in, For the plan Relative to the plan The relative importance of is the number of options under a certain criterion; element The calculation formula is: ； S43: Calculate the weight vector of the judgment matrix, ； ； ； in, For the The weight of the factors, is the order of the judgment matrix, is the element obtained after the judgment matrix is ​​column normalized. is the first Line The elements of the column, is the original judgment matrix The sum of the column elements, is the middle value obtained by summing each row of the normalized judgment matrix, is the number of columns of elements taken from the original judgment matrix; S44: Calculate the maximum eigenvalue of the judgment matrix , ； in, is the judgment matrix, is the weight vector, is the order of the judgment matrix, For the The weight of the parameters, is the judgment matrix The weight vector obtained after row normalization; Calculate consistency index , ； Calculate the consistency ratio , ； in, is a random consistency index, which can be obtained from the standard table by judging the order of the matrix. When , the judgment matrix has satisfactory consistency. If not, adjust the judgment matrix until it meets the consistency. S45: Criteria layer weights Denoted as: ； Among them, the weight of each criterion layer is recorded as ,in No. The weight vector of the criteria, the comprehensive weight of each parameter at the solution level for: ； Among them, the weights of the schemes under each criterion layer are recorded as , For the The weight vector of the solution layer under each criterion; S46: According to the correlation degree and the result of the analytic hierarchy process, the influence weight of each parameter is obtained and sorted.

9. A system for determining reservoir balanced injection and production parameters based on multi-algorithm analysis, comprising the method for determining reservoir balanced injection and production parameters based on multi-algorithm analysis according to any one of claims 1 to 8, characterized in that: It also includes a model building and data processing module, an association algorithm module, a hierarchical analysis module and a parameter determination module; The model building and data processing module is used to collect and organize various types of data, pre-process the data, obtain the prediction model results through numerical simulation operations, and screen the parameters affecting balanced injection and production; The association algorithm module is used to analyze and calculate the influencing parameters, screen the control parameters, determine the parameter influence, and sort them; The hierarchical analysis module is used to construct a judgment matrix, calculate weights and perform consistency checks, and determine balanced injection and production factors and weights; The parameter determination module is used to determine the final reservoir balanced injection and production parameters in accordance with the influence weights of the balanced injection and production factors and in combination with actual needs in combination with the correlation degree and weights.

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