A method for determining an optimal layer system combination and related apparatus

Abstract

The application discloses a kind of optimal layer series combination determination method and related device.The method comprises: according to the layer position number of each oil layer, obtain several layer series combinations, as initial population, calculate the fitness value of each layer series combination in initial population;According to the fitness value of each layer series combination, calculate the selection probability of each layer series combination, according to selection probability, from initial population, screen and retain the layer series combination of pre-set quantity, carry out cross processing and mutation processing to the layer series combination screened out, generate next generation population, calculate the fitness value of each layer series combination in next generation population, repeat the step of generating next generation population according to the fitness value of each layer series combination until the iteration termination condition is satisfied, then terminate iteration;After terminating iteration, from the layer series combination in initial population and the next generation population generated in iteration process, determine the optimal layer series combination.Oil layer can be efficiently and accurately divided into layer series.

Description

Technical Field

[0001] This invention relates to the field of oil extraction technology, and in particular to a method and apparatus for determining the optimal stratigraphic combination. Background Technology

[0002] Layered sandstone reservoirs are often subdivided vertically into numerous smaller layers with strong heterogeneity between them. After years of water injection development and entering the high water-cut stage, single-layer surges and interlayer interference become severe in layered sandstone reservoirs. The oil displacement efficiency and recovery rate vary greatly among layers, and the distribution of residual oil between layers and in the plane is uneven, leading to a gradual deterioration in development results. Layer reorganization and optimization play a crucial role in reducing interlayer differences, improving water drive control, and enhancing reservoir utilization in this type of reservoir. It is one of the important measures to resolve interlayer contradictions in water-driven sandstone reservoirs during the high water-cut stage, improve water injection development effects, and increase oil recovery.

[0003] Currently, methods for developing reservoir recombination schemes include conventional reservoir engineering methods, reservoir numerical simulation methods, and other mathematical methods. Each method has its limitations. For example, conventional reservoir engineering methods mainly analyze the feasibility of reservoir recombination based on factors such as utilization level, interlayers, fluid properties, and remaining reserves. While relatively quick, these methods only determine a single recombination scheme, making it difficult to differentiate and compare the effects of various combinations. Furthermore, the subjective factors of decision-makers have a significant impact. Reservoir numerical simulation methods, while comprehensively considering many factors affecting development outcomes after recombination and allowing for comparison of multiple recombination methods, are time-consuming, significantly impacting work efficiency. Other mathematical methods mostly only analyze a subset of parameters, resulting in relatively low accuracy and unsuitability for widespread application. Therefore, there is an urgent need for a method that is highly accurate, efficient, and capable of selecting the optimal recombination method from multiple options. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a method and related apparatus for determining an optimal layer combination to overcome or at least partially solve the above problems.

[0005] In a first aspect, embodiments of the present invention provide a method for determining the optimal layer combination, comprising:

[0006] Based on the stratigraphic number of each oil layer, several stratigraphic combinations are obtained as an initial population. The fitness value of each stratigraphic combination in the initial population is calculated. The stratigraphic combination includes several stratigraphic systems, and each stratigraphic system includes several stratigraphic numbers.

[0007] Based on the fitness value of each stratum combination, calculate the selection probability of each stratum combination. Based on the selection probability, select and retain a preset number of stratum combinations from the initial population. Perform crossover and mutation processing on each selected stratum combination to generate the next generation population. Calculate the fitness value of each stratum combination in the next generation population. Repeat the step of generating the next generation population based on the fitness value of each stratum combination until the iteration termination condition is met, then terminate the iteration.

[0008] After the iteration is terminated, the optimal stratification combination is determined from the stratification combination of the initial population and the next generation population generated by the iteration process.

[0009] In one embodiment, the main controlling factor of the oil layer is the physical property characteristics of at least one oil layer;

[0010] The fitness value of a layer combination is determined as follows:

[0011] Based on the data of each main control factor of each oil layer, the first variance of each main control factor corresponding to each layer in the layer combination is calculated respectively. The first variance represents the difference between oil layers corresponding to each layer number in the same layer in the layer combination.

[0012] Based on the data of each controlling factor of each oil layer, the number of layers in the layer combination, and the number of layers in each layer of the layer combination, the second variance of each controlling factor corresponding to the layer combination is calculated. The second variance represents the magnitude of the difference between the oil layers corresponding to the layer numbers of different layers in the layer combination.

[0013] The fitness value of the stratum combination is calculated based on the first variance of each controlling factor corresponding to each stratum and the second variance of each controlling factor corresponding to the stratum combination. The fitness value characterizes the excellence of the stratum combination.

[0014] In one embodiment, the first variance of the controlling factor corresponding to the stratum is determined in the following manner:

[0015] Based on the data of the main controlling factors of the oil layers corresponding to each layer number in the strata, and the number of layers in the strata, the standard deviation of the main controlling factors in the strata is calculated, which is used as the first variance of the main controlling factors corresponding to the strata.

[0016] In one embodiment, the second variance of the controlling factor corresponding to the hierarchical combination is determined in the following manner:

[0017] Based on the data of the main controlling factors of each oil layer, taking each layer in the layer combination as a unit, the average value of the main controlling factors of the oil layer corresponding to each layer number in the unit is calculated, and used as the data of the main controlling factors corresponding to each layer.

[0018] Based on the data of the controlling factors corresponding to each stratum and the number of strata in the stratum combination, the standard deviation of the controlling factors in the stratum combination is calculated, which is used as the second variance of the controlling factors corresponding to the stratum combination.

[0019] In one embodiment, calculating the fitness value of the layer combination includes:

[0020] Based on the first variance of each controlling factor corresponding to each layer in the layer combination, and the number of layers in the layer combination, calculate the average value of the first variance of each controlling factor corresponding to each layer;

[0021] Based on the second variance of each controlling factor corresponding to the stratification combination, and the average of the first variance of each controlling factor corresponding to each stratum in the stratification combination, the fitness value of the stratification combination is calculated using the following formula:

[0022]

[0023] In the above formula, σ represents the fitness value of the strata combination, q represents the qth controlling factor, λ1 represents the weighting coefficient of the average first variance of the strata corresponding to the qth controlling factor in the strata combination, and λ2 represents the weighting coefficient of the second variance of the qth controlling factor corresponding to the strata combination. σ represents the average of the first variances of the q-th controlling factor in the stratum combination. qg This represents the second variance of the q-th controlling factor corresponding to the stratification combination.

[0024] In one embodiment, calculating the selection probability of each layer combination based on its fitness value includes:

[0025] Based on the fitness value of each stratum combination in the initial population and the number of stratum combinations in the initial population, the selection probability of each stratum combination is calculated.

[0026] The selected strata combinations are cross-processed, including:

[0027] A single-point crossover operator is used to perform crossover processing on each layer in the selected layer combination according to a pre-set crossover probability;

[0028] Accordingly, the selected strata combinations are subjected to mutation processing, including:

[0029] A single-point mutation algorithm is used to mutate the crossover-processed strata combinations to generate the next generation population.

[0030] In one embodiment, the controlling factor is determined in the following manner:

[0031] Obtain data on the stratigraphic factors for each oil layer;

[0032] Based on the number of oil layers and the data of the stratigraphic division factors of each oil layer, a first matrix is ​​constructed, and the first matrix is ​​standardized; the covariance matrix of the standardized first matrix is ​​calculated, and the eigenvalues ​​of the covariance matrix are calculated; based on the eigenvalues, the contribution rate of each stratigraphic division factor is calculated.

[0033] Based on the order of contribution rate, a preset number of stratification factors, starting from the one with the highest contribution rate, are selected as the main control factors.

[0034] In one embodiment, the iteration termination condition is that the number of iterations is greater than or equal to a preset iteration number threshold;

[0035] After the iteration is terminated, the optimal stratification combination is determined from the stratification combinations of the initial population and all next-generation populations generated during the iteration process, including:

[0036] Based on the order of fitness values, a predetermined number of strata combinations, starting from the highest fitness value, are selected from the strata combinations of the initial population and the next generation population generated during the iteration process as the determined optimal strata combinations.

[0037] Secondly, embodiments of the present invention provide an apparatus for determining an optimal layer combination, comprising:

[0038] The fitness value calculation module is used to obtain several stratigraphic combinations based on the stratigraphic numbers of each oil layer, which are used as an initial population, and to calculate the fitness value of each stratigraphic combination in the initial population; the stratigraphic combination includes several stratigraphic systems, and the stratigraphic system includes several stratigraphic numbers;

[0039] The generation module is used to calculate the selection probability of each stratum combination based on the fitness value of each stratum combination, select and retain a preset number of stratum combinations from the initial population based on the selection probability, perform crossover and mutation processing on each selected stratum combination to generate the next generation population, calculate the fitness value of each stratum combination in the next generation population, and repeat the step of generating the next generation population based on the fitness value of each stratum combination until the iteration termination condition is met, then the iteration is terminated.

[0040] The determination module is used to determine the optimal stratification combination from the stratification combination of the initial population and the next generation population generated during the iteration process after the iteration is terminated.

[0041] Thirdly, embodiments of the present invention provide a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method for determining the optimal layer combination.

[0042] Fourthly, embodiments of the present invention provide a terminal device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method for determining the optimal layer combination.

[0043] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0044] The method for determining the optimal stratigraphic combination provided in this invention involves obtaining several stratigraphic combinations based on the stratigraphic number of each oil layer, forming an initial population, calculating the fitness value of each stratigraphic combination in the initial population, calculating the selection probability of each stratigraphic combination based on the fitness value, selecting a preset number of stratigraphic combinations from the initial population based on the selection probability, performing crossover and mutation processing on the selected stratigraphic combinations to generate a next-generation population, and repeating the step of generating the next-generation population until the iteration termination condition is met, at which point the iteration terminates. The optimal stratigraphic combination is determined from the stratigraphic combinations of the initial population and the next-generation population generated during the iteration process. In this invention, a genetic algorithm is used to recombine and optimize several stratigraphic combinations in each population, expanding the possibilities of more stratigraphic combinations, avoiding the problem of relying on the experience of professional personnel in the prior art, improving the accuracy of the obtained optimal stratigraphic combination for high water-cut oilfields, greatly shortening the cycle of studying the optimal stratigraphic combination of oilfields, overcoming the problem of long time consumption in the prior art, improving the efficiency of calculating the optimal stratigraphic combination, and providing a foundation for guiding the fine development of water injection in oilfields in the later stage.

[0045] In addition, based on the geological and fluid characteristics of each layer of the oilfield and parameters such as development indicators, principal component analysis was used to select the main controlling factors from several factors affecting the layer combination results. Based on the data of the main controlling factors, the fitness value of the layer combination was calculated, reducing noise data and improving the accuracy of the fitness value of the layer combination, which further improved the accuracy of the determined optimal layer combination.

[0046] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0047] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0048] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0049] Figure 1a This is one of the flowcharts for determining the optimal layer combination in an embodiment of the present invention;

[0050] Figure 1b This is the second flowchart of the method for determining the optimal layer combination in an embodiment of the present invention;

[0051] Figure 2 This is a flowchart of the method for determining the main controlling factors in an embodiment of the present invention;

[0052] Figure 3 This is one of the flowcharts for calculating the fitness value of layer combination in an embodiment of the present invention;

[0053] Figure 4 This is the second flowchart for calculating the fitness value of layer combination in an embodiment of the present invention;

[0054] Figure 5 This is a flowchart illustrating the generation of the next generation population in an embodiment of the present invention;

[0055] Figure 6 This is a schematic diagram of the structure of the device for determining the optimal layer combination in an embodiment of the present invention. Detailed Implementation

[0056] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0057] To address the problems existing in the prior art, embodiments of the present invention provide a method and related apparatus for determining the optimal layer combination.

[0058] Example

[0059] This invention provides a method for determining the optimal layer combination, the process of which is as follows: Figure 1a and 1b As shown, it includes the following steps:

[0060] Step S101: Based on the stratigraphic number of each oil layer, obtain several stratigraphic combinations as the initial population, and calculate the fitness value of each stratigraphic combination in the initial population; the stratigraphic combination includes several stratigraphic systems, and the stratigraphic system includes several stratigraphic numbers.

[0061] Step S102: Calculate the selection probability of each stratum combination based on its fitness value. Based on the selection probability, select and retain a preset number of stratum combinations from the initial population. Perform crossover and mutation processing on each selected stratum combination to generate the next generation population. Calculate the fitness value of each stratum combination in the next generation population. Repeat the step of generating the next generation population based on the fitness value of each stratum combination until the iteration termination condition is met, then terminate the iteration.

[0062] Step S103: After terminating the iteration, determine the optimal stratification combination from the stratification combinations of the initial population and the next generation population generated during the iteration process.

[0063] To facilitate understanding of this scheme, in this embodiment of the invention, the oil layer corresponding to the stratigraphic position number in the stratigraphic system is referred to as a sub-layer. For example, if an oil field has 10 layers, which is 10 sub-layers, and the stratigraphic position numbers of each oil layer are L1, L2, L3...L10, then the oil layer corresponding to L1 is the first sub-layer, and the oil layer corresponding to L2 is the second sub-layer. A specific example is used to illustrate this: taking a total of 10 sub-layers and a stratigraphic division coefficient of 3 as an example, a randomly generated stratigraphic combination is shown in Table 1 below:

[0064] Table 1:

[0065] L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 Layer 1 1 1 1 0 0 0 0 0 0 0 Layer 2 0 0 0 1 1 1 1 0 0 0 Layer 3 0 0 0 0 0 0 0 1 1 1

[0066] "1" indicates that the layer contains the layer, and "0" indicates that the layer does not contain the layer, which is the encoding method of the genetic algorithm in step S102.

[0067] In some optional embodiments, the oil field has various physical properties such as permeability and porosity. Each oil layer has these physical properties, and the data for the same physical property may differ between different oil layers, such as... Figure 2 As shown, the controlling factors are determined in the following way:

[0068] Step S201: Obtain data on the stratigraphic division factors for each oil layer;

[0069] The factors for stratigraphic division are the physical properties of the oilfield that affect the stratigraphic combination results. Optional factors for stratigraphic division include permeability, porosity, effective thickness, geological reserves, oil-bearing area, degree of recovery, water cut, remaining oil saturation, remaining reserves, the proportion of remaining reserves to total reserves, well section length, etc. These will not be listed one by one in the embodiments of this invention.

[0070] Step S202: Based on the number of oil layers and the data of the stratigraphic division factors of each oil layer, construct a first matrix and standardize the first matrix; calculate the covariance matrix of the standardized first matrix and calculate the eigenvalues ​​of the covariance matrix; based on the eigenvalues, calculate the contribution rate of each stratigraphic division factor.

[0071] Step S203: Select a preset number of stratification factors, starting from the one with the highest contribution rate, according to the order of contribution rate, as the determined main control factors.

[0072] Multiple stratification factors, i.e. multiple indicators that affect stratification, are selected from multiple stratification factors through principal component analysis, and then selected as the main control factors, i.e., principal components.

[0073] In step S202 above, the first matrix is ​​an n*m matrix, where n is the number of oil layers in the oil field and m is the number of stratigraphic division factors. The constructed first matrix is ​​shown below:

[0074]

[0075] Where A is the first matrix, and a is an element in matrix A, for example, a 11 For the first stratification factor, the data in the first substratum, a n1 The first stratification factor is the data in the nth sub-layer.

[0076] Then, the first matrix A is standardized to obtain the processed matrix B, as shown below:

[0077]

[0078] Where b is an element in matrix B, for example, matrix A can be standardized using the following formula:

[0079]

[0080] In formula (1), b ij Let represent the element in the i-th row and j-th column of matrix B. V represents the column mean of the j-th column in matrix A. j Represents the sample difference of matrix A;

[0081] Specifically, the column mean of the j-th column in matrix A is calculated using the following formula:

[0082]

[0083] In formula (2), n represents the number of oil layers, a ij This represents the element in the i-th row and j-th column of matrix A.

[0084] The sample difference of matrix A is calculated using the following formula:

[0085]

[0086] The parameters in formula (3) have been explained above, and will not be repeated in this embodiment of the invention.

[0087] Then, using the following formula, the covariance matrix of matrix B is obtained, as shown below:

[0088]

[0089] Wherein, the element c in matrix C ij Let be the element in the i-th row and j-th column of matrix C.

[0090]

[0091] In formula (5), k represents a positive integer from 1 to n;

[0092] In step S202 above, the eigenvalues ​​β of matrix C are calculated. It should be noted that each stratification factor corresponds to an eigenvalue β. The eigenvalues ​​β corresponding to each stratification factor are arranged in descending order, and the sorting results are shown below:

[0093] β1≥β2≥…≥β M ;

[0094] Where, β M Let M be the eigenvalue that is the Mth largest in the eigenvalue sequence, where M and m have the same numerical value.

[0095] Then, based on the characteristic values ​​corresponding to each stratification factor, the contribution rate of each stratification factor is calculated using the following formula:

[0096]

[0097] In formula (6), u i Indicates β i The contribution rate of the corresponding stratification factors, and the other parameters in formula (6) have been explained above, and will not be repeated here in the embodiments of the present invention.

[0098] The cumulative contribution rate is obtained by summing the contribution rates of each stratum division factor using the following formula:

[0099]

[0100] The parameters in formula (7) have been explained above, and will not be repeated here in the embodiments of the present invention.

[0101] Then, based on the M eigenvalues ​​obtained above, the eigenvector corresponding to each eigenvalue of matrix C is calculated, as shown below:

[0102]

[0103] In this embodiment, the eigenvector d1 corresponds to the eigenvalue β1, the eigenvector d2 corresponds to the eigenvalue β2, and so on. This will not be elaborated further in this embodiment of the invention.

[0104] Based on the cumulative contribution rate calculated above, the 1st, 2nd, ..., rth (r≤M) hierarchical partitioning factors corresponding to the feature values ​​with a cumulative contribution rate exceeding 80% are selected to obtain r principal components (principal components are the main controlling factors), as shown below:

[0105]

[0106] Wherein, the i-th principal component is: H i＝ d 1i b1+d 2i b2+…+d mi b m (i = 1, 2, ..., r);

[0107] In some optional embodiments, the main controlling factor of the oil layer is the physical property characteristics of at least one oil layer; in step S101 above, the fitness value of the layer combination, such as Figure 3 As shown, it can be determined in the following way:

[0108] Step S301: Based on the data of each main control factor of each oil layer, calculate the first variance of each main control factor corresponding to each layer in the layer combination. The first variance represents the magnitude of the difference between oil layers corresponding to each layer number in the same layer combination.

[0109] The first variance of the controlling factor can also be called the intra-stratum variance of the controlling factor.

[0110] Step S302: Based on the data of each main control factor of each oil layer, the number of layers in the layer combination, and the number of layers in each layer in the layer combination, calculate the second variance of each main control factor corresponding to the layer combination. The second variance represents the difference in the oil layer corresponding to the layer number between different layers in the layer combination.

[0111] The second variance of the controlling factor can also be called the inter-stratum variance of the controlling factor.

[0112] Step S303: Calculate the fitness value of the stratum combination based on the first variance of each controlling factor corresponding to each stratum and the second variance of each controlling factor corresponding to the stratum combination. The fitness value characterizes the excellence of the stratum combination.

[0113] In some optional embodiments, in step S301 above, the first variance of the controlling factor corresponding to the stratum is determined in the following manner:

[0114] Based on the data of the main controlling factors of the oil layers corresponding to each layer number in the stratigraphic system, and the number of layers in the stratigraphic system, the standard deviation of the main controlling factors in the stratigraphic system is calculated, which is used as the first variance of the main controlling factors corresponding to the stratigraphic system.

[0115] Specifically, the first variance of the controlling factor corresponding to each stratum is calculated using the following formula, which is the within-stratum variance of the controlling factor corresponding to each stratum:

[0116]

[0117] In the above formula, σ qli Let q represent the first variance of the controlling factor corresponding to the stratum, i represent the i-th stratum in the stratum combination, n represent the number of strata in that stratum, q represent the q-th controlling factor, and j represent the j-th stratum in the i-th stratum. ij This represents the data of the q-th controlling factor in the j-th layer of the i-th system, where q ia This represents the average value of the q-th controlling factor in the i-th stratum.

[0118] The average value of the q-th controlling factor in the i-th stratum is calculated using the following formula:

[0119]

[0120] The parameters in formula (9) have been explained above, and will not be repeated here in the embodiments of the present invention.

[0121] In some optional embodiments, in step S302 above, the second variance of the controlling factor corresponding to the layer combination is determined in the following manner:

[0122] (1) Based on the data of the main control factors of each oil layer, take each layer in the layer combination as a unit, calculate the average value of the main control factors of each oil layer corresponding to each layer number in the unit, and use it as the data of the main control factors corresponding to each layer.

[0123] Specifically, the data for the main controlling factors corresponding to each stratum are calculated according to the following formula:

[0124]

[0125] In formula (10), S represents the number of strata in the strata combination, and q a q represents the q-th controlling factor within S hierarchical levels. ia The average value.

[0126] (2) Based on the data of the main control factors corresponding to each stratum and the number of stratums in the stratum combination, calculate the standard deviation of the main control factors in the stratum combination, and use it as the second variance of the main control factors corresponding to the stratum combination.

[0127] Specifically, the second variance of the controlling factor corresponding to the stratum combination is calculated according to the following formula:

[0128]

[0129] In formula (11), σ qg This represents the second variance of the controlling factor corresponding to the strata combination.

[0130] In some optional embodiments, step S303 above, calculating the fitness value of the layer combination, as shown in 4, can be implemented in the following way:

[0131] Step S401: Based on the first variance of each controlling factor corresponding to each layer in the layer combination and the number of layers in the layer combination, calculate the average value of the first variance of each controlling factor corresponding to each layer.

[0132] Specifically, the average first variance of each controlling factor for each stratum is calculated using the following formula;

[0133]

[0134] In formula (12), This represents the average of the first variance of each controlling factor. Other parameters have been explained above and will not be repeated here in the embodiments of the present invention.

[0135] Based on the principle of stratified reorganization, it is desirable to minimize the intra-stratum variance of each controlling factor and maximize the inter-stratum variance. The smaller the better, σ qg The bigger the better, in other words... This represents the average variance of the q-th controlling factor within the same stratigraphic system, characterizing the differences between oil layers corresponding to different stratigraphic positions within the same stratigraphic system. The smaller the value of σ, the smaller the difference between oil layers within the same layer system. qg σ represents the variance of the q-th controlling factor among different stratigraphic units within a stratigraphic assemblage, characterizing the differences in oil layers corresponding to different stratigraphic unit numbers. qg The larger the value, the greater the difference between oil layers in different strata.

[0136] Specifically, the following formula is used for... Normalization is performed:

[0137]

[0138] Step S402: Based on the second variance of each controlling factor corresponding to the stratum combination, and the average value of the first variance of each controlling factor corresponding to each stratum in the stratum combination, calculate the fitness value of the stratum combination using the following formula:

[0139]

[0140] In formula (12), σ represents the fitness value of the strata combination, q represents the qth controlling factor, λ1 represents the weighting coefficient of the average first variance of the strata corresponding to the qth controlling factor in the strata combination, λ2 represents the weighting coefficient of the second variance of the qth controlling factor corresponding to the strata combination, and α q This represents the weight coefficient of the q-th controlling factor. Other parameters have been explained above and will not be repeated here in this embodiment of the invention.

[0141] Where the sum of λ1 and λ2 is 1,

[0142] In some optional embodiments, the following parameters are preset: 1) Population size G, i.e., the number of strata combinations in the population; 2) Total number of strata S; 3) Number of chromosomes N, i.e., the total number of sub-strata; 4) Termination generation T, i.e., the maximum number of iterations; 5) Crossover probability P. c 6) Probability of mutation P m In step S102 above, the selection probability of each stratum combination is calculated based on its fitness value. Based on the selection probability, a preset number of stratum combinations are selected and retained from the initial population. Crossover and mutation processes are then performed on each selected stratum combination to generate the next generation population. (Refer to...) Figure 5 As shown, this can be achieved in the following way:

[0143] Step S501: Calculate the selection probability of each stratum combination based on the fitness value of each stratum combination in the initial population and the number of stratum combinations in the initial population. Based on the selection probability, select and retain a preset number of stratum combinations from the initial population.

[0144] Specifically, the probability of choosing a stratum combination is calculated according to the following formula:

[0145]

[0146] In formula (14), G represents the number of strata combinations in the population.

[0147] There are many methods for selecting a preset number of layer combinations based on selection probabilities. For example, a random number between [0,1] can be generated as a selection operator. From the layer combinations in the initial population, layer combinations with selection probabilities greater than the selection operator are selected. It is then determined whether the number of selected layer combinations is consistent with the number of layer combinations in the initial population. If so, the selected layer combinations are used as the parent for the next crossover process. If not, a selection operator is generated again, and layer combinations with selection probabilities greater than the second selection operator are selected from the layer combinations in the initial population. The results of the two selections are then compared. If the number of selected strata is consistent with the number of strata combinations in the initial population, then the strata combinations selected in the two selections are used as the parent for the next crossover process. If not, the process continues to generate selection operators and select strata combinations according to the selected operators until the number of all selected strata combinations is consistent with the number of strata combinations in the initial population. At this point, no more random selection operators are generated, and all selected strata combinations are used as the parent for the next crossover process. A concrete example is given: For instance, if the initial population has 5 strata combinations: ABCDE, the selection probabilities of these 5 strata are shown in Table 2 below.

[0148] Table 2:

[0149] Layer combination A B C D E Choose probability 0.73 0.85 0.58 0.32 0.21

[0150] The first random selection operator, for example, 0.8, selects B, filtering out 1 stratum combination, which is less than 5. The second random selection operator, for example, 0.7, selects B and A. The stratum combinations selected in the two rounds are BBA, which has 3 combinations, less than 5. The third random selection operator, for example, 0.71, selects B and A. The stratum combinations selected in the three rounds are BBABA, which has 5 combinations, consistent with the number of stratum combinations in the initial population. Therefore, no further selection operators are generated, and the selection process ends.

[0151] The embodiments of the present invention do not limit the method of selecting and retaining a preset number of strata combinations from the initial population based on the selection probability.

[0152] Step S502: Using a single-point crossover operator, crossover processing is performed on each layer in the selected layer combination according to the pre-set crossover probability;

[0153] For each of the selected G layer combinations, random pairings are performed between the layers within each layer. A crossover position is randomly selected, and then the pairings are performed according to the set crossover probability P. c They are paired up at the intersection.

[0154] Step S503: Use a single-point mutation algorithm to mutate each layer combination after crossover to generate the next generation population.

[0155] Randomly select the mutation location; randomly generate comparison probabilities; if the comparison probability is less than the mutation probability P... m If the mutation occurs, the 1 at the mutation location will either become 0 or the 0 will become 1. It is important to note that if a sublayer changes from 1 to 0, then in another sublayer, that sublayer must change from 0 to 1. Similarly, if a sublayer changes from 0 to 1, then in another sublayer, that sublayer must change from 1 to 0.

[0156] In some optional embodiments, the iteration termination condition is that the number of iterations is greater than or equal to a preset iteration number threshold. After the iteration is terminated, the optimal layer combination can be determined in the following manner:

[0157] In descending order of fitness value, from the strata combinations of the initial population and the next generation population generated during the iteration process (including the strata combinations of the next generation population generated in each iteration during the iteration process, i.e., all strata combinations of the next generation population generated during the iteration process), a predetermined number of strata combinations starting from the highest fitness value are selected as the determined optimal strata combinations; for example, the strata combination with the highest fitness value can be selected as the determined optimal strata combination, or optionally, 10 strata combinations starting from the highest fitness value can be selected as the determined optimal strata combination, and this embodiment of the present invention does not limit this.

[0158] Based on the same inventive concept, embodiments of the present invention also provide a device for determining the optimal layer combination, the structure of which is as follows: Figure 6 As shown, it includes:

[0159] The fitness value calculation module 601 is used to obtain several stratigraphic combinations based on the stratigraphic number of each oil layer, as an initial population, and to calculate the fitness value of each stratigraphic combination in the initial population; the stratigraphic combination includes several stratigraphic systems, and the stratigraphic system includes several stratigraphic numbers.

[0160] The generation module 602 is used to calculate the selection probability of each stratum combination based on the fitness value of each stratum combination, select and retain a preset number of stratum combinations from the initial population based on the selection probability, perform crossover and mutation processing on each selected stratum combination to generate the next generation population, calculate the fitness value of each stratum combination in the next generation population, and repeat the step of generating the next generation population based on the fitness value of each stratum combination until the iteration termination condition is met, then the iteration is terminated.

[0161] The determination module 603 is used to determine the optimal stratification combination from the stratification combination of the initial population and the next generation population generated during the iteration process after the iteration is terminated.

[0162] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0163] Based on the same inventive concept, embodiments of the present invention also provide a computer storage medium, wherein computer executable instructions are stored in the computer storage medium, and the computer executable instructions, when executed by a processor, implement the above-mentioned method for determining the optimal layer combination.

[0164] Based on the same inventive concept, embodiments of the present invention also provide a terminal device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method for determining the optimal layer combination.

[0165] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0166] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0167] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.

[0168] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0169] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0170] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0171] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term "comprising" as used in the specification or claims is interpreted in a manner similar to the term "including," as interpreted when used as a conjunction in the claims. Additionally, the use of any term "or" in the specification of the claims is intended to mean "non-exclusive or."

Claims

1. A method for determining an optimal layer stack combination, characterized in that include: Based on the stratigraphic number of each oil layer, several stratigraphic combinations are obtained as an initial population. The fitness value of each stratigraphic combination in the initial population is calculated. The stratigraphic combination includes several stratigraphic systems, and each stratigraphic system includes several stratigraphic numbers. Based on the fitness value of each stratum combination, calculate the selection probability of each stratum combination. Based on the selection probability, select and retain a preset number of stratum combinations from the initial population. Perform crossover and mutation processing on each selected stratum combination to generate the next generation population. Calculate the fitness value of each stratum combination in the next generation population. Repeat the step of generating the next generation population based on the fitness value of each stratum combination until the iteration termination condition is met, then terminate the iteration. After the iteration is terminated, the optimal stratification combination is determined from the stratification combination of the initial population and the next generation population generated by the iteration process; The primary controlling factor for an oil reservoir is the physical properties of at least one reservoir; the fitness value of the layer assemblage is determined as follows: Based on the data of each main control factor of each oil layer, the first variance of each main control factor corresponding to each layer in the layer combination is calculated respectively. The first variance represents the difference between oil layers corresponding to each layer number in the same layer in the layer combination. Based on the data of each controlling factor of each oil layer, the number of layers in the layer combination, and the number of layers in each layer of the layer combination, the second variance of each controlling factor corresponding to the layer combination is calculated. The second variance represents the magnitude of the difference between the oil layers corresponding to the layer numbers of different layers in the layer combination. The fitness value of the stratum combination is calculated based on the first variance of each controlling factor corresponding to each stratum and the second variance of each controlling factor corresponding to the stratum combination. The fitness value characterizes the excellence of the stratum combination.

2. The method of claim 1, wherein, The first variance of the controlling factor corresponding to the stratum is determined as follows: Based on the data of the main controlling factors of the oil layers corresponding to each layer number in the strata, and the number of layers in the strata, the standard deviation of the main controlling factors in the strata is calculated, which is used as the first variance of the main controlling factors corresponding to the strata.

3. The method of claim 1, wherein, The second variance of the controlling factor corresponding to the stratum combination is determined in the following way: Based on the data of the main controlling factors of each oil layer, taking each layer in the layer combination as a unit, the average value of the main controlling factors of the oil layer corresponding to each layer number in the unit is calculated, and used as the data of the main controlling factors corresponding to each layer. Based on the data of the controlling factors corresponding to each stratum and the number of strata in the stratum combination, the standard deviation of the controlling factors in the stratum combination is calculated, which is used as the second variance of the controlling factors corresponding to the stratum combination.

4. The method of claim 1, wherein, The calculation of the fitness value of the layer combination includes: Based on the first variance of each controlling factor corresponding to each layer in the layer combination, and the number of layers in the layer combination, calculate the average value of the first variance of each controlling factor corresponding to each layer; Based on the second variance of each controlling factor corresponding to the stratification combination, and the average of the first variance of each controlling factor corresponding to each stratum in the stratification combination, the fitness value of the stratification combination is calculated using the following formula: In the above formula, σ represents the fitness value of the layer system combination, q represents the qth principal control factor, λ1 represents the weight coefficient of the average value of the first variance of the layer system in the layer system combination corresponding to the qth principal control factor, λ2 represents the weight coefficient of the second variance of the layer system combination corresponding to the qth principal control factor, represents the average value of the first variance of the layer system in the layer system combination corresponding to the qth principal control factor, σ qg represents the second variance of the layer system combination corresponding to the qth principal control factor, α q represents the weight coefficient of the qth principal control factor.

5. The method of claim 1, wherein, The step of calculating the selection probability of each layer combination based on its fitness value includes: Based on the fitness value of each stratum combination in the initial population and the number of stratum combinations in the initial population, the selection probability of each stratum combination is calculated. The selected strata combinations are cross-processed, including: A single-point crossover operator is used to perform crossover processing on each layer in the selected layer combination according to a pre-set crossover probability; Accordingly, the selected strata combinations are subjected to mutation processing, including: A single-point mutation algorithm is used to mutate the crossover-processed strata combinations to generate the next generation population.

6. The method according to any one of claims 1 to 5, wherein, The controlling factors are determined in the following way: Obtain data on the stratigraphic factors for each oil layer; Based on the number of oil layers and the data of the stratigraphic division factors of each oil layer, a first matrix is ​​constructed, and the first matrix is ​​then standardized. Calculate the covariance matrix of the first matrix after standardization, and calculate the eigenvalues ​​of the covariance matrix. Based on the eigenvalues, calculate the contribution rate of each stratification factor. Based on the order of contribution rate, a preset number of stratification factors, starting from the one with the highest contribution rate, are selected as the main control factors.

7. The method of claim 1, wherein, The iteration termination condition is that the number of iterations is greater than or equal to a preset iteration number threshold; After the iteration is terminated, the optimal stratification combination is determined from the stratification combinations of the initial population and the next generation population generated during the iteration process, including: Based on the order of fitness values, a predetermined number of strata combinations, starting from the highest fitness value, are selected from the strata combinations of the initial population and the next generation population generated during the iteration process as the determined optimal strata combinations.

8. A device for determining the optimal layer combination, characterized in that, include: The fitness value calculation module is used to obtain several stratigraphic combinations based on the stratigraphic numbers of each oil layer, which are used as an initial population, and to calculate the fitness value of each stratigraphic combination in the initial population; the stratigraphic combination includes several stratigraphic systems, and the stratigraphic system includes several stratigraphic numbers; The generation module is used to calculate the selection probability of each stratum combination based on the fitness value of each stratum combination, select and retain a preset number of stratum combinations from the initial population based on the selection probability, perform crossover and mutation processing on each selected stratum combination to generate the next generation population, calculate the fitness value of each stratum combination in the next generation population, and repeat the step of generating the next generation population based on the fitness value of each stratum combination until the iteration termination condition is met, then the iteration is terminated. The determination module is used to determine the optimal stratification combination from the stratification combination of the initial population and the next generation population generated during the iteration process after the iteration is terminated; The primary controlling factor for an oil reservoir is the physical properties of at least one reservoir; the fitness value of the layer assemblage is determined as follows: Based on the data of each main control factor of each oil layer, the first variance of each main control factor corresponding to each layer in the layer combination is calculated respectively. The first variance represents the difference between oil layers corresponding to each layer number in the same layer in the layer combination. Based on the data of each controlling factor of each oil layer, the number of layers in the layer combination, and the number of layers in each layer of the layer combination, the second variance of each controlling factor corresponding to the layer combination is calculated. The second variance represents the magnitude of the difference between the oil layers corresponding to the layer numbers of different layers in the layer combination. The fitness value of the stratum combination is calculated based on the first variance of each controlling factor corresponding to each stratum and the second variance of each controlling factor corresponding to the stratum combination. The fitness value characterizes the excellence of the stratum combination.

9. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the method for determining the optimal layer combination as described in any one of claims 1-7.

10. A terminal device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method for determining the optimal layer combination as described in any one of claims 1-7.

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