Discontinuous chemical flooding variable cycle combination scheme optimization method and device

Abstract

The invention relates to the field of oil reservoir engineering, and discloses a discontinuous chemical flooding variable cycle combination scheme optimization method and device, which can determine a target optimization algorithm with the highest suitability with a target oil reservoir according to a permeability variable coefficient of the target oil reservoir and an integrated optimization algorithm. And performing iterative optimization on the parameter value of the chemical agent combination variable to be optimized in the benefit-related objective function based on the constructed benefit-related objective function, the constraint condition and the objective optimization algorithm to obtain the optimized parameter value of the chemical agent combination variable. According to the method, multivariable and multi-constraint conditions in the discontinuous chemical flooding variable cycle combination optimization scheme can be comprehensively considered, iterative optimization is performed by using the target optimization algorithm with the highest suitability with the target oil reservoir, the optimization efficiency is improved, full optimization of the discontinuous chemical flooding variable cycle combination scheme is realized, and the optimization efficiency of the discontinuous chemical flooding variable cycle combination scheme is improved. The optimization efficiency and precision of the discontinuous chemical flooding variable cycle combination scheme are improved, the oil reservoir recovery efficiency is improved, and the economic benefits of oil reservoir development are improved.

Description

Technical Field

[0001] The present invention relates to the field of oil reservoir engineering, and in particular to a method and device for optimizing a discontinuous chemical flooding cycle combination scheme. Background Art

[0002] With the development of science and technology, oil reservoir exploration and production technology continues to improve.

[0003] Chemical flooding is an important means of enhancing oil recovery. By injecting chemicals (such as surfactants and polymers) into the reservoir, it improves the fluidity of the crude oil and the wettability of the rock, thereby enhancing the flooding effect. However, the design of chemical flooding cycle schemes in related technologies involves many complex factors.

[0004] However, the design of chemical flooding cycle schemes in related technologies mainly relies on experience and trial and error, and is not optimized enough, which affects the efficiency and benefits of reservoir development. Summary of the Invention

[0005] The present invention provides a method and device for optimizing a discontinuous chemical flooding cycle combination scheme, which is used to solve the defects in related technologies that the design of chemical flooding cycle schemes mainly relies on experience and trial and error, is insufficiently optimized, and affects the efficiency and benefits of oil reservoir development. The method achieves full optimization of the discontinuous chemical flooding cycle combination scheme, improves the efficiency and accuracy of the optimization of the discontinuous chemical flooding cycle combination scheme, increases the oil reservoir recovery rate, and improves the economic benefits of oil reservoir development.

[0006] In a first aspect, the present invention provides a method for optimizing a discontinuous chemical flooding cycle combination scheme, comprising: monitoring a permeability variation coefficient of a target oil reservoir, and determining a heterogeneity grade of the target oil reservoir based on the monitored permeability variation coefficient; According to the heterogeneity level of the target oil reservoir, searching for a target optimization algorithm corresponding to the heterogeneity level of the target oil reservoir in the created correspondence group; wherein the correspondence group includes a one-to-one correspondence between multiple heterogeneity levels and multiple optimization algorithms, and the target optimization algorithm is the optimization algorithm that has the highest adaptability to the heterogeneity level of the target oil reservoir; Based on the constructed benefit-related objective function, constraint conditions, and the target optimization algorithm of the discontinuous chemical flooding cycle combination scheme, iteratively optimize the parameter values ​​of the chemical agent combination variables to be optimized in the benefit-related objective function until the set iterative optimization end conditions are met, and obtain the optimized parameter values ​​of the chemical agent combination variables; The optimized parameter values ​​of the chemical agent combination variables are used as an optimized discontinuous chemical flooding cycle combination scheme.

[0007] Optionally, the heterogeneity level of the target oil reservoir is low heterogeneity, medium heterogeneity or high heterogeneity; Determining the heterogeneity level of the target oil reservoir based on the monitored permeability variation coefficient includes: When the permeability variation coefficient is less than or equal to a set first variation coefficient threshold, determining that the heterogeneity level of the target oil reservoir is low heterogeneity; When the permeability variation coefficient is greater than a first variation coefficient threshold and less than or equal to a set second variation coefficient threshold, determining that the heterogeneity level of the target oil reservoir is medium heterogeneity; When the permeability variation coefficient is greater than the second variation coefficient threshold, the heterogeneity level of the target oil reservoir is determined to be high heterogeneity.

[0008] Optionally, the multiple optimization algorithms include a particle swarm optimization algorithm, a differential evolution algorithm, and a covariance matrix adaptive evolution algorithm; When the heterogeneity level of the target reservoir is low heterogeneity, the target optimization algorithm is a covariance matrix adaptive evolutionary algorithm; When the heterogeneity level of the target reservoir is medium heterogeneity, the target optimization algorithm is a particle swarm optimization algorithm; When the heterogeneity level of the target oil reservoir is high heterogeneity, the target optimization algorithm is a differential evolution algorithm.

[0009] Optionally, the benefit-related objective function is: ; ; ; in, NPV is the cumulative incremental net present value, T For the project life cycle, For the Total annual income; For the The total annual cost includes the purchase cost of various chemicals, injection and production operating costs, equipment depreciation and maintenance costs; r is the discount rate; For the Crude oil prices in the year; For the Annual crude oil production is related to the mixing effect of various chemicals; n is the number of types of chemical agents; For the i Unit price of the chemical; For the t Year iThe amount of chemical used; For the t Annual operating costs; For the t annual equipment depreciation and maintenance costs; The annual environmental protection and governance costs.

[0010] Optionally, the chemical combination variables include concentrations, injection volumes and injection rates of multiple chemicals, and the constraints include a total concentration upper limit constraint, a total injection volume upper limit constraint, a total injection rate upper limit constraint and a chemical compatibility constraint.

[0011] Optionally, based on the constructed benefit-related objective function, constraint conditions, and target optimization algorithm of the discontinuous chemical flooding cycle combination scheme, iteratively optimize the parameter values ​​of the chemical combination variables to be optimized in the benefit-related objective function until the set iterative optimization end conditions are met, and obtain the optimized parameter values ​​of the chemical combination variables, including: Extracting function-related production data of the benefit-related objective function from the operating production data of the target oil reservoir; The function-related production data, the benefit-related objective function and the constraint conditions are input into the target optimization algorithm so that the target optimization algorithm: takes maximizing the function value of the benefit-related objective function as the goal, and under the condition of satisfying the constraint conditions, iteratively optimizes the concentration, injection amount and injection rate of each chemical agent in the benefit-related objective function based on the function-related production data until the set iterative optimization end conditions are met, and obtains the optimized concentration, optimized injection amount and optimized injection rate of each chemical agent.

[0012] Optionally, before searching for a target optimization algorithm corresponding to the heterogeneity level of the target oil reservoir in the created correspondence relationship group according to the heterogeneity level of the target oil reservoir, the method further includes: Create multiple numerically simulated reservoirs with different levels of heterogeneity; For any of the optimization algorithms, based on the optimization algorithm, the discontinuous chemical flooding cycle combination scheme is optimized for each of the numerical simulation reservoirs to obtain the optimized combination scheme of each of the numerical simulation reservoirs, and the recovery rate improvement value of each of the numerical simulation reservoirs is determined according to the optimized combination scheme of each of the numerical simulation reservoirs. The maximum recovery rate improvement value is determined among the recovery rate improvement values ​​of each of the numerical simulation reservoirs, and the heterogeneity level of the numerical simulation reservoir corresponding to the maximum recovery rate improvement value is determined as the heterogeneity level with the highest adaptability to the optimization algorithm.

[0013] Optionally, after using the optimized parameter values ​​of the chemical combination variables as the optimized discontinuous chemical flooding cycle combination scheme, the method further includes: Optimize injection and production of the target reservoir according to the optimized discontinuous chemical flooding cycle combination scheme, and monitor key production data of the target reservoir; Based on the key production data of the target oil reservoir, determine whether to continue executing the step of optimizing the injection and production of the target oil reservoir according to the optimized discontinuous chemical flooding cycle combination scheme, or return to execute the step of monitoring the permeability variation coefficient of the target oil reservoir to re-optimize the discontinuous chemical flooding cycle combination scheme of the target oil reservoir.

[0014] Optionally, when the key production data of the target oil reservoir includes the water cut rise rate and the pressure fluctuation, determining, based on the key production data of the target oil reservoir, whether to continue executing the step of optimizing the injection and production of the target oil reservoir according to the optimized discontinuous chemical flooding cycle combination scheme, or returning to executing the step of monitoring the permeability variation coefficient of the target oil reservoir to re-optimize the discontinuous chemical flooding cycle combination scheme of the target oil reservoir, includes: When it is determined that the water cut increase rate of the target oil reservoir is greater than a first set threshold, or the pressure fluctuation is greater than a second set threshold, returning to the step of monitoring the permeability variation coefficient of the target oil reservoir to re-optimize the discontinuous chemical flooding cycle combination scheme of the target oil reservoir; When it is determined that the water cut increase rate of the target oil reservoir is not greater than the first set threshold and the pressure fluctuation is not greater than the second set threshold, continue to execute the step of optimizing injection and production of the target oil reservoir according to the optimized discontinuous chemical flooding cycle combination scheme.

[0015] In a second aspect, the present invention provides a discontinuous chemical flooding cycle combination scheme optimization device, comprising: A monitoring unit for monitoring the permeability variation coefficient of the target reservoir; a determining unit, configured to determine a heterogeneity grade of the target oil reservoir based on the monitored permeability variation coefficient; a search unit configured to search, based on the heterogeneity level of the target oil reservoir, for a target optimization algorithm corresponding to the heterogeneity level of the target oil reservoir in the created correspondence group; wherein the correspondence group includes a one-to-one correspondence between multiple heterogeneity levels and multiple optimization algorithms, and the target optimization algorithm is the optimization algorithm that has the highest adaptability to the heterogeneity level of the target oil reservoir; an iterative optimization unit for iteratively optimizing the parameter values ​​of the chemical agent combination variables to be optimized in the benefit-related objective function based on the constructed benefit-related objective function, the constraint conditions, and the target optimization algorithm, until the set iterative optimization end conditions are met, and obtaining the optimized parameter values ​​of the chemical agent combination variables; As a unit, it is used to use the optimized parameter values ​​of the chemical agent combination variables as an optimized discontinuous chemical flooding cycle combination scheme.

[0016] The discontinuous chemical flooding cycle combination scheme optimization method and device provided by the present invention can monitor the permeability variation coefficient of the target oil reservoir, determine the target optimization algorithm with the highest adaptability to the target oil reservoir based on the permeability variation coefficient of the target oil reservoir and the integrated optimization algorithm, and iteratively optimize the parameter values ​​of the chemical combination variables to be optimized in the benefit-related objective function based on the constructed benefit-related objective function, constraints and target optimization algorithm until the set iterative optimization end conditions are met, and obtain the optimized parameter values ​​of the chemical combination variables. The present invention can comprehensively consider the multiple variables and multiple constraints in the discontinuous chemical flooding cycle combination optimization scheme, use the target optimization algorithm with the highest adaptability to the target oil reservoir for iterative optimization, improve the optimization efficiency, achieve full optimization of the discontinuous chemical flooding cycle combination scheme, improve the efficiency and accuracy of the optimization of the discontinuous chemical flooding cycle combination scheme, improve the oil reservoir recovery rate, and improve the economic benefits of oil reservoir development. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or related technologies, the following is a brief introduction to the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A flow chart of a method for optimizing a discontinuous chemical flooding cycle combination scheme provided by an embodiment of the present invention; Figure 2 A flow chart of another method for optimizing a discontinuous chemical flooding cycle combination scheme provided by an embodiment of the present invention; Figure 3 A flow chart of another method for optimizing a discontinuous chemical flooding cycle combination scheme provided by an embodiment of the present invention; Figure 4 A flow chart of another method for optimizing a discontinuous chemical flooding cycle combination scheme provided by an embodiment of the present invention; Figure 5A schematic diagram of a discontinuous chemical flooding cycle combination scheme optimization device provided by an embodiment of the present invention; Figure 6 A schematic structural diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0020] The following combination Figures 1-4 The present invention describes the optimization method of the discontinuous chemical flooding cycle combination scheme.

[0021] like Figure 1 As shown, this embodiment proposes a first discontinuous chemical flooding cycle combination scheme optimization method, which may include the following steps: S101. Monitor the permeability variation coefficient of the target reservoir.

[0022] Specifically, this embodiment can perform core sampling on the target oil reservoir to obtain multiple core samples. By performing relevant tests and inspections on the core samples, the permeability range of the target oil reservoir can be measured, and its permeability variation coefficient can be determined.

[0023] Specifically, this embodiment can periodically monitor the permeability variation coefficient of the target oil reservoir.

[0024] S102. Determine the heterogeneity level of the target oil reservoir based on the monitored permeability variation coefficient.

[0025] Specifically, this embodiment can determine the heterogeneity level of the target oil reservoir based on the monitored permeability variation coefficient of the target oil reservoir.

[0026] Optionally, the heterogeneity level of the target reservoir is low heterogeneity, medium heterogeneity or high heterogeneity. In this case, step S102 may include: When the permeability variation coefficient is less than or equal to a set first variation coefficient threshold, the heterogeneity level of the target reservoir is determined to be low heterogeneity; When the permeability variation coefficient is greater than the first variation coefficient threshold and less than or equal to the set second variation coefficient threshold, the heterogeneity level of the target reservoir is determined to be medium heterogeneity; When the permeability variation coefficient is greater than the second variation coefficient threshold, the heterogeneity level of the target oil reservoir is determined to be high heterogeneity.

[0027] The first coefficient of variation threshold and the second coefficient of variation threshold can be determined by technicians according to actual conditions and are not limited in this embodiment. Optionally, the first coefficient of variation threshold can be 0.5 and the second coefficient of variation threshold can be 0.8.

[0028] Specifically, in this embodiment, when the permeability coefficient of variation of the target reservoir is less than or equal to a first coefficient of variation threshold, the target reservoir is determined to be a low heterogeneity reservoir. When the permeability coefficient of variation of the target reservoir is greater than the first coefficient of variation threshold and less than or equal to a second coefficient of variation threshold, the target reservoir is determined to be a medium heterogeneity reservoir. When the permeability coefficient of variation of the target reservoir is greater than the second coefficient of variation threshold, the target reservoir is determined to be a high heterogeneity reservoir.

[0029] S103. Based on the heterogeneity level of the target oil reservoir, search for a target optimization algorithm corresponding to the heterogeneity level of the target oil reservoir in the created correspondence group; wherein the correspondence group includes a one-to-one correspondence between multiple heterogeneity levels and multiple optimization algorithms, and the target optimization algorithm is the optimization algorithm that has the highest adaptability to the heterogeneity level of the target oil reservoir.

[0030] Specifically, this embodiment can search for an optimization algorithm that is most compatible with the heterogeneity level of the target reservoir among the preliminarily selected optimization algorithms based on the heterogeneity level of the target reservoir, and use the algorithm as the target optimization algorithm.

[0031] Specifically, this embodiment can first determine the optimization algorithms with the highest adaptability to different heterogeneity levels, and create a correspondence group to save the one-to-one correspondence between multiple heterogeneity levels and multiple optimization algorithms. For any heterogeneity level, the optimization algorithm corresponding to the heterogeneity level is the optimization algorithm with the highest adaptability to the heterogeneity level.

[0032] Optionally, in another discontinuous chemical flooding cycle combination optimization method proposed in this embodiment, the method may further include, before step S103: Create multiple simulated reservoirs with varying levels of heterogeneity; For any optimization algorithm, based on the optimization algorithm, the discontinuous chemical flooding cycle combination scheme is optimized for each simulated oil reservoir to obtain the optimized combination scheme for each simulated oil reservoir, and the recovery factor improvement value of each simulated oil reservoir is determined according to the optimized combination scheme of each simulated oil reservoir. The maximum recovery factor improvement value is determined among the recovery factor improvement values ​​of each simulated oil reservoir, and the heterogeneity level of the simulated oil reservoir corresponding to the maximum recovery factor improvement value is determined as the heterogeneity level with the highest adaptability to the optimization algorithm.

[0033] It is understood that this embodiment can create multiple simulated reservoirs and set different heterogeneity levels for each simulated reservoir. Based on numerical simulation and different optimization algorithms, the embodiment can determine the optimal combination schemes obtained by iteratively optimizing the different simulated reservoirs under different optimization algorithms. For any simulated reservoir, this embodiment can use the different optimization combination schemes obtained by iteratively optimizing the different optimization algorithms. Then, by comparing the recovery rate increases obtained by the different optimization combination schemes, the maximum recovery rate increase is determined. The optimization algorithm corresponding to the maximum recovery rate is determined as the optimal algorithm corresponding to the heterogeneity level of the simulated reservoir.

[0034] Optionally, multiple optimization algorithms include particle swarm optimization, differential evolution, and covariance matrix adaptive evolutionary algorithms; When the heterogeneity level of the target reservoir is low heterogeneity, the target optimization algorithm is the covariance matrix adaptive evolutionary algorithm; When the heterogeneity level of the target reservoir is medium, the target optimization algorithm is the particle swarm optimization algorithm; When the heterogeneity level of the target reservoir is high, the target optimization algorithm is the differential evolution algorithm.

[0035] S104. Based on the constructed benefit-related objective function, constraint conditions and target optimization algorithm of the discontinuous chemical flooding cycle combination scheme, the parameter values ​​of the chemical agent combination variables to be optimized in the benefit-related objective function are iteratively optimized until the set iterative optimization end conditions are met, and the optimized parameter values ​​of the chemical agent combination variables are obtained.

[0036] Specifically, in this embodiment, the benefit-related objective function and constraint conditions of the discontinuous chemical flooding and conversion cycle combination scheme may be created first.

[0037] Specifically, the selection of benefit-related objective function can maximize the cumulative incremental net present value as the goal, comprehensively considering the mixed use of multiple chemical agents, aiming to improve the economic benefits of reservoir development.

[0038] Optionally, the benefit-related objective function is: ; ; ; in, NPV is the cumulative incremental net present value, T For the project life cycle, For the Total annual income; For the The total annual cost includes the purchase cost of various chemicals, injection and production operating costs, equipment depreciation and maintenance costs;r is the discount rate; For the Crude oil prices in the year; For the Annual crude oil production is related to the mixing effect of various chemicals; n is the number of types of chemical agents; For the i Unit price of the chemical; For the t Year i The amount of chemical used; For the t Annual operating costs; For the t annual equipment depreciation and maintenance costs; The annual environmental protection and governance costs.

[0039] Optionally, the chemical combination variables include concentrations, injection volumes, and injection rates of multiple chemicals, and the constraints include a total concentration upper limit constraint, a total injection volume upper limit constraint, a total injection rate upper limit constraint, and a chemical compatibility constraint.

[0040] Specifically, in this embodiment, chemical concentration, chemical injection amount, and chemical injection speed may be selected as key optimization variables.

[0041] The chemical concentration , and Respectively represent i The minimum and maximum allowable concentrations of the chemical agent can be determined based on test data. For example, the allowable concentration range for polymers is 500 to 2000 mg / L, and the allowable concentration range for surfactants is 100 to 500 mg / L.

[0042] Among them, the amount of chemical injection , and Respectively represent i The minimum and maximum injection rates of each chemical agent can be determined based on the chemical supply, cost budget and reservoir requirements.

[0043] Among them, the chemical injection rate , and Respectively represent i The minimum and maximum injection rates of the chemical agent can be determined by the technical specifications of the injection equipment and the absorption capacity of the reservoir, and can be specifically 50 to 150 cubic meters per day.

[0044] Specifically, the chemical combination variable can be represented by a vector as , which contains the chemical concentration, injection volume and injection speed of all chemicals to be optimized, a total of 3 n variables.

[0045] Among them, the upper limit constraint of the total concentration can be determined based on indoor compatibility experiments, which can be specifically expressed as: .

[0046] The upper limit constraint of the total injection volume can be calculated based on the geological reserves of the block, which can be expressed as: .

[0047] The upper limit constraint of the total injection speed can be based on the rated power of the injection equipment, which can be expressed as: .

[0048] Chemical compatibility constraints can be specifically expressed as: (When i is incompatible with j).

[0049] in, and Respectively represent i Kind and j A chemical agent. i Species and j When two chemical agents are incompatible, such as polymers and high-valent metal ions, the two cannot be used at the same time.

[0050] It should be noted that the goal of the optimization problem in this embodiment is to solve the optimization variable vector so that NPV Maximize and satisfy all constraints. In this embodiment, a penalty function method can be used to process the constraints.

[0051] Optionally, step S104 may include: Extracting function-related production data of the benefit-related objective function from the operating production data of the target oil reservoir; The function-related production data, benefit-related objective function and constraints are input into the target optimization algorithm so that the target optimization algorithm: takes maximizing the function value of the benefit-related objective function as the goal, and under the condition of satisfying the constraints, iteratively optimizes the concentration, injection amount and injection rate of each chemical in the benefit-related objective function based on the function-related production data until the set iterative optimization end conditions are met, and obtains the optimized concentration, optimized injection amount and optimized injection rate of each chemical.

[0052] Specifically, this embodiment can extract relevant production data required for calculating benefit-related objective functions from the operating production data of the target oil reservoir, such as the crude oil price, crude oil production, equipment depreciation cost, maintenance cost, environmental protection cost and governance cost in the tth year, and the procurement cost and injection and production operation cost of various chemical agents.

[0053] Specifically, this embodiment can input function-related production data, benefit-related objective functions and constraints into the target optimization algorithm, so that the target optimization algorithm can iteratively optimize the parameter values ​​of the chemical combination variables until the iterative optimization end conditions are met, and obtain the optimized parameter values ​​of the chemical combination variables, that is, the optimized concentration, optimized injection volume and optimized injection speed of each chemical.

[0054] S105. Using the optimized parameter values ​​of the chemical agent combination variables as an optimized discontinuous chemical flooding cycle combination scheme.

[0055] Specifically, this embodiment can use the optimized parameter values ​​of the chemical combination variables as an optimized discontinuous chemical flooding cycle combination scheme. Based on this optimized discontinuous chemical flooding cycle combination scheme, this embodiment can control relevant equipment used in reservoir injection and production, thereby improving reservoir injection and production efficiency and economic benefits.

[0056] The discontinuous chemical flooding cycle combination scheme optimization method proposed in this embodiment can monitor the permeability variation coefficient of the target oil reservoir, determine the target optimization algorithm with the highest adaptability to the target oil reservoir based on the permeability variation coefficient of the target oil reservoir and the integrated optimization algorithm, and iteratively optimize the parameter values ​​of the chemical combination variables to be optimized in the benefit-related objective function based on the constructed benefit-related objective function, constraints, and target optimization algorithm until the set iterative optimization end conditions are met, and obtain the optimized parameter values ​​of the chemical combination variables. This embodiment can comprehensively consider the multiple variables and multiple constraints in the discontinuous chemical flooding cycle combination optimization scheme, use the target optimization algorithm with the highest adaptability to the target oil reservoir for iterative optimization, improve the optimization efficiency, achieve full optimization of the discontinuous chemical flooding cycle combination scheme, improve the efficiency and accuracy of the discontinuous chemical flooding cycle combination scheme optimization, increase the oil reservoir recovery rate, and improve the economic benefits of oil reservoir development.

[0057] based on Figure 1 This embodiment proposes another method for optimizing a discontinuous chemical flooding cycle combination scheme. After step S105, the method may further include the following steps: S201. Optimize injection and production of the target reservoir according to the optimized discontinuous chemical flooding cycle combination scheme, and monitor key production data of the target reservoir; S202. Based on the key production data of the target oil reservoir, determine whether to continue executing the step of optimizing the injection and production of the target oil reservoir according to the optimized discontinuous chemical flooding cycle combination scheme, or return to the step of monitoring the permeability variation coefficient of the target oil reservoir to re-optimize the discontinuous chemical flooding cycle combination scheme of the target oil reservoir.

[0058] Specifically, this embodiment can monitor key production data of a target reservoir while optimizing injection and production of the target reservoir using an optimized discontinuous chemical flooding cycle combination scheme. Based on the key production data of the target reservoir, a determination is made as to whether to continue using the optimized discontinuous chemical flooding cycle combination scheme to optimize injection and production of the target reservoir or to re-optimize the discontinuous chemical flooding cycle combination scheme for the target reservoir.

[0059] Optionally, when the key production data of the target reservoir include water cut increase rate and pressure fluctuation, the above step S202 may include: When it is determined that the water cut increase rate of the target reservoir is greater than the first set threshold, or the pressure fluctuation is greater than the second set threshold, returning to the step of monitoring the permeability variation coefficient of the target reservoir to re-optimize the discontinuous chemical flooding cycle combination scheme of the target reservoir; When it is determined that the water cut increase rate of the target reservoir is not greater than the first set threshold and the pressure fluctuation is not greater than the second set threshold, the step of optimizing injection and production of the target reservoir according to the optimized discontinuous chemical flooding cycle combination scheme is continued.

[0060] Specifically, when it is determined that the water content increase rate of the target reservoir is greater than the first set threshold, or the pressure fluctuation is greater than the second set threshold, this embodiment can determine that the reservoir injection and production effect is poor, and can re-optimize the discontinuous chemical flooding cycle combination scheme, and then use the optimized new discontinuous chemical flooding cycle combination scheme for reservoir injection and production, thereby enhancing the flexibility of reservoir injection and production optimization and ensuring the reservoir injection and production effect.

[0061] Specifically, this embodiment can determine that the reservoir injection and production effect is better when it is determined that the water cut increase rate of the target reservoir is not greater than the first set threshold and the pressure fluctuation is not greater than the second set threshold, and the current optimized discontinuous chemical flooding cycle combination scheme can continue to be used for reservoir injection and production.

[0062] The discontinuous chemical flooding cycle combination scheme optimization method proposed in this embodiment can monitor the key production data of the target oil reservoir after using the optimized discontinuous chemical flooding cycle combination scheme to optimize the injection and production of the target oil reservoir. Based on the key production data, it is decided whether to continue using the current scheme for oil reservoir injection and production or to adjust the current scheme before oil reservoir injection and production. This can enhance the flexibility of oil reservoir injection and production optimization and ensure the oil reservoir injection and production effect.

[0063] Based on Figure 1 In the third non - continuous chemical flooding variable - cycle combination scheme optimization method proposed in this embodiment, numerical simulation of the reservoir to be studied can be carried out, and the optimization algorithm with the highest adaptability can be determined according to different permeability variation coefficients.

[0064] Specifically, an optimization model driven by reservoir data can be constructed in this embodiment.

[0065] Among them, reservoir physical parameter acquisition can be carried out in this embodiment: through three - dimensional seismic, well - logging interpretation and core experiments, basic data such as porosity distribution, permeability anisotropy coefficient, and crude oil viscosity are obtained; Objective function construction: Taking the maximization of net present value (NPV) as the core objective, coupling the recovery factor index (such as recovery factor ≥ 0.45) and the water - cut rising rate constraint (≤ 3% / year); Optimization variable definition: Select the chemical agent concentration gradient (0.1 - 2.0 wt%) of the injection well group, slug alternation period (15 - 90 days), and injection rate (50 - 200 m³ / d) as decision variables, and the upper and lower limits of the variables are determined based on core flow experiments; Technical constraint setting: Reservoir protection constraint: Injection pressure ≤ 80% of the rock fracture pressure (measured by triaxial compressive experiment); Fluid compatibility constraint: The mixing ratio of polymer and surfactant needs to satisfy the interfacial tension ≤ 10⁻³ mN / m (based on pendant - drop method experimental data); Optimization problem formalization: Combining the reservoir numerical simulation results, establish a mathematical expression of the optimization problem considering dynamic response.

[0066] After that, algorithm screening suitable for reservoir characteristics can be carried out in this embodiment.

[0067] As Figure 2 shown, this embodiment can also perform reservoir heterogeneity classification, algorithm adaptive selection, optimization algorithm module, optimization result output and dynamic feedback / adaptive switching.

[0068] Specifically, this embodiment can perform heterogeneity level division: Based on the permeability variation coefficient Vk, the reservoir is divided into three levels: low (Vk ≤ 0.5), medium (0.5 < Vk ≤ 0.8), and high (Vk > 0.8); Algorithm - reservoir matching rule: High - heterogeneity reservoir (Vk > 0.8): Enable the differential evolution algorithm (i.e., DE in Figure 3 ), set the mutation factor F = 0.8, crossover probability CR = 0.7, to enhance the global search ability; Medium - heterogeneity reservoir (0.5 < Vk ≤ 0.8): Enable the particle swarm optimization algorithm (i.e., PSO in Figure 3 ), set the particle swarm size to 40, learning factors c1 = c2 = 2.0, inertia weight w = 0.729, to balance global exploration and local exploitation; Low heterogeneity reservoir (Vk≤0.5): Enable the covariance matrix adaptive evolution strategy (i.e. Figure 3 In CMA-ES, we set the initial step size σ = 0.1 and the population size λ = 4 + 3 × log(N) (N is the dimension of the optimization variable) to improve the local optimization accuracy.

[0069] Adaptability verification: Through comparative tests on three groups of typical reservoir models (Vk=0.4, 0.6, and 0.9), an algorithm-reservoir characteristic matching matrix was established to provide a basis for the adaptive selection mechanism.

[0070] The differential evolution algorithm converges 23% faster than PSO in highly heterogeneous models, and the probability of the global optimal solution is increased by 18%; The particle swarm optimization algorithm has the best comprehensive performance in the moderately heterogeneous model, with an average optimization accuracy improvement of 12% compared with DE and CMA-ES; The covariance matrix adaptive evolutionary algorithm has the highest local search accuracy in low heterogeneity models, and the standard deviation of the solution is reduced by 40%.

[0071] Implementation of dynamic feedback optimization system Real-time data interface development: Connect to the oilfield operation monitoring system through relevant communication transmission protocols to collect real-time data such as wellhead pressure and water content (sampling frequency 1 time / hour); Adaptive trigger mechanism: When the water cut rate increases by more than 0.5% / day or the pressure fluctuation is more than 5%, the optimization algorithm is automatically activated to optimize the next round of injection and production plan; On-site control execution: The optimization results are converted into control signals such as injection pump frequency and valve opening through logic control to achieve closed-loop control.

[0072] The optimization method for the discontinuous chemical flooding and variable cycle combination scheme proposed in this embodiment has the advantages of high efficiency and strong optimization ability. By integrating algorithms such as differential evolution, particle swarm optimization, and covariance matrix adaptive evolution strategy, it takes into account both global search and local optimization, effectively avoids local optimality, and improves optimization accuracy and speed. An adaptive dynamic adjustment mechanism is adopted to enable the optimization algorithm to be flexibly adjusted according to the characteristics of the solution space, adapt to complex reservoir conditions, and achieve multi-variable, multi-constrained optimization goals. This invention can significantly improve recovery rate and economic benefits, is superior to traditional optimization methods, and has good application prospects.

[0073] like Figure 3 As shown, in the fourth discontinuous chemical flooding cycle combination scheme optimization method proposed in this embodiment, the optimization model construction, algorithm screening and adaptation, dynamic feedback optimization and scheme output can be performed in sequence to perform scheme optimization and reservoir injection and production optimization. Among them, this embodiment can use an integrated optimization algorithm to perform adaptive scheme optimization. Among them, Figure 3In the figure, DE stands for differential evolution algorithm, PSO stands for particle swarm optimization algorithm, and CMA-ES stands for covariance matrix adaptive evolutionary algorithm.

[0074] In the other discontinuous chemical flooding cycle combination scheme optimization method proposed in this embodiment, the following steps may be included: S300, Determination of reservoir heterogeneity parameters.

[0075] Specifically, in this example, 52 core samples were obtained from the target reservoir through coring analysis. The measured permeability range was 12.3-1867 mD, and the permeability coefficient of variation (Vk) was calculated to be 0.72. Based on the classification criteria, the target reservoir was determined to have moderate heterogeneity. Triaxial compression testing determined the reservoir rock fracture pressure to be 40 MPa, and the upper limit of the injection pressure was set at 32 MPa (80% of the fracture pressure).

[0076] S301. Establish objective function, constraints and select optimization variables.

[0077] Specifically, this embodiment can establish a benefit-related objective function to NPV With maximization as the goal, the mixed use of multiple chemicals is comprehensively considered to improve the economic benefits of reservoir development.

[0078] S302. Formal representation of optimization problems and method for handling constraints.

[0079] The goal of the optimization problem is to find the optimization variable vector so that NPV Maximize and satisfy all constraints. The penalty function method is used to deal with the constraints.

[0080] S303. Screening and optimization mechanism adaptability analysis of the optimization algorithm for discontinuous chemical flooding and cycle combination schemes.

[0081] To address the complex optimization requirements of moderately heterogeneous reservoirs (Vk = 0.72), this study systematically compared four typical intelligent optimization algorithms: genetic algorithm, particle swarm optimization (PSO), differential evolution (DE), and covariance matrix adaptive evolutionary algorithm (CMAE). Preliminary screening using the Rosenbrock test function (dimension 30) revealed that PSO demonstrated the best overall performance, with an average convergence iteration count of 87 generations, 43% and 22% fewer than the 152 generations for the GA and 112 generations for the DE. The probability of a global optimal solution reached 82%, significantly higher than the 63% for the CMAE. The PSO's core advantages lie in its fast convergence, simple parameter setup, and ability to balance global exploration with local development capabilities, making it particularly suitable for multi-peak optimization problems in moderately heterogeneous reservoirs.

[0082] Analysis and evaluation of optimization algorithm performance. To further optimize the performance of the particle swarm optimization algorithm, key parameters were optimized through orthogonal experimental design, and the optimal parameter combination was determined to be: particle swarm size 40 (convergence is 18% faster than 30 scale, and computation time is reduced by 25% compared to 50 scale), learning factor c1=c2=2.0, and inertia weight adopts a linear decreasing strategy (from 0.9 to 0.4). 30 independent experiments were conducted on a test set consisting of Rosenbrock function (multi-peak), Rastrigin function (high dimension), and Griewank function (non-linear). The results showed that the average number of convergence iterations of the particle swarm optimization algorithm was 87 generations, and the quality of the optimal solution ( NPV =287 million yuan) outperformed the differential evolution algorithm (273 million yuan) and the covariance matrix adaptive evolutionary algorithm (265 million yuan), taking 105 minutes to compute and significantly outperforming the other algorithms in terms of stability (standard deviation 0.12). In-depth analysis showed that the particle swarm optimization algorithm achieved the optimal balance in a moderately heterogeneous reservoir model: it improved local search accuracy by 15.7% compared to the differential evolution algorithm and enhanced global search capability by 23.1% compared to the covariance matrix adaptive evolutionary algorithm.

[0083] Research on the compatibility of optimization mechanisms and nonlinear characteristics. The adaptability of the particle swarm optimization algorithm to moderately heterogeneous reservoirs stems primarily from its unique optimization mechanism. The particle swarm's information sharing mechanism enables simultaneous tracking of multiple potential optimal solutions, effectively matching the multi-peak characteristics caused by reservoir heterogeneity. The dynamic adjustment strategy of the inertia weight achieves an adaptive balance between exploration and development, maintaining strong global search capabilities to escape local optima in the early stages of optimization (high w values) and enhancing local development capabilities to fine-tune parameters in the later stages of optimization (low w values). The swarm's collaborative nature effectively prevents the algorithm from being trapped in local optima caused by high permeability zones. To verify this adaptability, this example was tested in simulated reservoirs with different Vk values. In a low-heterogeneity reservoir (Vk = 0.4), the particle swarm optimization algorithm achieved a 5.8% recovery increase, lower than the 6.5% achieved by the covariance matrix adaptive evolutionary algorithm. In a moderate-heterogeneity reservoir (Vk = 0.72), the particle swarm optimization algorithm achieved an 8.3% recovery increase, significantly higher than the 6.8% achieved by the differential optimization algorithm and the 5.2% achieved by the covariance matrix adaptive evolutionary algorithm. In a high-heterogeneity reservoir (Vk = 0.9), the 6.1% increase was lower than the 7.5% achieved by the differential optimization algorithm. These results fully demonstrate that the particle swarm optimization algorithm is the best fit for moderate-heterogeneity reservoirs.

[0084] S304. Construction, application and verification of optimization solution algorithm for discontinuous chemical flooding and variable cycle combination scheme.

[0085] Specifically, in this embodiment, based on the dynamic change characteristics of reservoir heterogeneity, the present invention constructs a multi-algorithm intelligent scheduling system that can automatically switch optimization algorithms according to the permeability variation coefficient Vk monitored in real time. The core scheduling logic of the system is as follows: when Vk > 0.8 (high heterogeneity), the DE algorithm (mutation factor F = 0.8, crossover probability CR = 0.7) is enabled to strengthen global search; when 0.5 < Vk ≤ 0.8 (medium heterogeneity), it is switched to the PSO algorithm (parameter settings: scale 40, c1 = c2 = 2.0, w = 0.729); when Vk ≤ 0.5 (low heterogeneity), the CMA-ES algorithm (initial step size σ = 0.1) is selected to improve local optimization accuracy.

[0086] To achieve optimization solution. The optimization solution system is developed in Python language and mainly includes four major functional modules. The algorithm core module implements the core logic such as particle initialization, velocity update, and position update of the particle swarm optimization algorithm; the real-time data interface module connects to the on-site operation monitoring system through relevant communication transmission protocols to collect key production data such as wellhead pressure (sampling frequency 1 minute / time, accuracy ±0.1 MPa), water cut (sampling frequency 1 day / time, accuracy ±0.1%), and injection volume (sampling frequency 1 day / time, accuracy ±0.5 m³); according to the change of monitoring data, re-optimize the plan opportunely, and the minimum time interval between two plan optimizations is not less than 30 days; establish an automatic call interface for numerical simulation software to predict the effects of different chemical flooding plans; the constraint handling module uses the penalty function method to dynamically handle various constraint conditions to ensure the engineering feasibility of the optimization plan.

[0087] S305. Application and verification.

[0088] To verify the actual application effect of this embodiment, a well group in a certain oilfield is selected for on-site tests. A three-dimensional geological model is established based on well logging data and core analysis, with a grid size of 20m × 20m × 0.5m and 32,768 effective grids. The variation coefficient Vk of the model permeability distribution is 0.72, and the crude oil viscosity is 56 mPa·s (50 °C). Three algorithms are applied for optimization comparison, and the results show that the particle swarm optimization plan performs the best in all indicators: the recovery rate is increased by 8.3% (22.1% higher than the differential evolution algorithm and 59.6% higher than the covariance matrix adaptation evolution algorithm), NPV reaching nearly 300 million yuan, the chemical agent consumption is reduced to more than 90 tons (saving 23.3% compared with the traditional plan), the water cut is reduced by nearly 11 percentage points, and the calculation time is 105 minutes.

[0089] Results from a 180-day field trial showed that implementing the particle swarm optimization algorithm increased daily oil production from 12.5 tons / day to 18.3 tons / day, a 46.4% increase. The water cut steadily decreased from 82.3% to 71.5%. The injection pressure remained stable at 28-31 MPa, consistently below the safety limit of 32 MPa. Chemical consumption per unit area decreased from 0.87 tons per 100 tons of crude oil to 0.62 tons per 100 tons of crude oil, a 28.7% decrease. During the trial, the water cut increase rate was monitored to rise to 0.5% due to dynamic development. The system automatically initiated a new round of optimization. After 15 days, the water cut increase rate decreased from 0.5% to 0.23%. The stability of the displacement front improved by 27% (based on the standard deviation of the front's advancing velocity calculated from numerical simulations), validating the dynamic adaptability of the multi-algorithm scheduling system.

[0090] like Figure 4 As shown, this embodiment can include a data acquisition module, a numerical simulation module, an optimization and decision-making module, and an execution control module. The data acquisition module can include pressure and water content sensors, while the numerical simulation module can create geological models and perform fluid simulations. The optimization and decision-making module can optimize parameters based on an integrated algorithm. The execution control module can control injection equipment, pumps, and valves based on the optimization plan to optimize injection and production in the reservoir.

[0091] like Figure 5 As shown, this embodiment proposes a discontinuous chemical flooding cycle combination scheme optimization device, which may include: Monitoring unit 501, used to monitor the permeability variation coefficient of the target reservoir; A determination unit 502 is configured to determine the heterogeneity level of the target reservoir based on the monitored permeability variation coefficient; A search unit 503 is configured to search, based on the heterogeneity level of the target reservoir, for a target optimization algorithm corresponding to the heterogeneity level of the target reservoir in the created correspondence group; wherein the correspondence group includes a one-to-one correspondence between multiple heterogeneity levels and multiple optimization algorithms, and the target optimization algorithm is the optimization algorithm that has the highest adaptability to the heterogeneity level of the target reservoir; The iterative optimization unit 504 is used to iteratively optimize the parameter values ​​of the chemical agent combination variables to be optimized in the benefit-related objective function based on the constructed benefit-related objective function, constraint conditions, and target optimization algorithm of the discontinuous chemical flooding cycle combination scheme until the set iterative optimization end conditions are met, and obtain the optimized parameter values ​​of the chemical agent combination variables; As unit 505, it is used to use the optimized parameter values ​​of the chemical agent combination variables as the optimized discontinuous chemical flooding cycle combination scheme.

[0092] It should be noted that the processing of the monitoring unit 501, the determining unit 502, the searching unit 503, the iterative optimization unit 504 and the processing unit 505 and the beneficial effects thereof can be referred to in detail. Figure 1 Steps S101 to S105 in the above are not described in detail.

[0093] Optionally, the heterogeneity level of the target reservoir is low heterogeneity, medium heterogeneity, or high heterogeneity; Based on the monitored permeability variation coefficient, the heterogeneity level of the target reservoir is determined, including: When the permeability variation coefficient is less than or equal to a set first variation coefficient threshold, the heterogeneity level of the target reservoir is determined to be low heterogeneity; When the permeability variation coefficient is greater than the first variation coefficient threshold and less than or equal to the set second variation coefficient threshold, the heterogeneity level of the target reservoir is determined to be medium heterogeneity; When the permeability variation coefficient is greater than the second variation coefficient threshold, the heterogeneity level of the target oil reservoir is determined to be high heterogeneity.

[0094] Optionally, multiple optimization algorithms include particle swarm optimization, differential evolution, and covariance matrix adaptive evolutionary algorithms; When the heterogeneity level of the target reservoir is low heterogeneity, the target optimization algorithm is the covariance matrix adaptive evolutionary algorithm; When the heterogeneity level of the target reservoir is medium, the target optimization algorithm is the particle swarm optimization algorithm; When the heterogeneity level of the target reservoir is high, the target optimization algorithm is the differential evolution algorithm.

[0095] Optionally, the benefit-related objective function is: ; ; ; in, NPV is the cumulative incremental net present value, T For the project life cycle, For the Total annual income; For the The total annual cost includes the purchase cost of various chemicals, injection and production operating costs, equipment depreciation and maintenance costs; r is the discount rate; For the Crude oil prices in the year; For the Annual crude oil production is related to the mixing effect of various chemicals; nis the number of types of chemical agents; For the i Unit price of the chemical; For the t Year i The amount of chemical used; For the t Annual operating costs; For the t annual equipment depreciation and maintenance costs; The annual environmental protection and governance costs.

[0096] Optionally, the chemical combination variables include concentrations, injection volumes, and injection rates of multiple chemicals, and the constraints include a total concentration upper limit constraint, a total injection volume upper limit constraint, a total injection rate upper limit constraint, and a chemical compatibility constraint.

[0097] Optionally, the iterative optimization unit 504 is further configured to: Extracting function-related production data of the benefit-related objective function from the operating production data of the target oil reservoir; The function-related production data, benefit-related objective function and constraints are input into the target optimization algorithm so that the target optimization algorithm: takes maximizing the function value of the benefit-related objective function as the goal, and under the condition of satisfying the constraints, iteratively optimizes the concentration, injection amount and injection rate of each chemical in the benefit-related objective function based on the function-related production data until the set iterative optimization end conditions are met, and obtains the optimized concentration, optimized injection amount and optimized injection rate of each chemical.

[0098] Optionally, the above device further includes: Algorithm adaptation unit, used to: Before searching for a target optimization algorithm corresponding to the heterogeneity level of the target reservoir in the created correspondence group according to the heterogeneity level of the target reservoir, a plurality of numerical simulation reservoirs with different heterogeneity levels are created; For any optimization algorithm, based on the optimization algorithm, the discontinuous chemical flooding cycle combination scheme is optimized for each numerical simulation reservoir to obtain the optimized combination scheme for each numerical simulation reservoir. The recovery factor improvement value of each numerical simulation reservoir is determined according to the optimized combination scheme of each numerical simulation reservoir. The maximum recovery factor improvement value is determined among the recovery factor improvement values ​​of each numerical simulation reservoir. The heterogeneity level of the numerical simulation reservoir corresponding to the maximum recovery factor improvement value is determined as the heterogeneity level with the highest adaptability to the optimization algorithm.

[0099] Optionally, the above device further includes: Injection-production optimization unit, used for: After the optimized parameter values ​​of the chemical agent combination variables are used as the optimized discontinuous chemical flooding cycle combination scheme, injection and production optimization is performed on the target reservoir according to the optimized discontinuous chemical flooding cycle combination scheme, and key production data of the target reservoir is monitored; Based on the key production data of the target reservoir, it is determined whether to continue to execute the step of optimizing the injection and production of the target reservoir according to the optimized discontinuous chemical flooding cycle combination scheme, or to return to the step of monitoring the permeability variation coefficient of the target reservoir to re-optimize the discontinuous chemical flooding cycle combination scheme of the target reservoir.

[0100] Optionally, when the key production data of the target reservoir include water cut increase rate and pressure fluctuation, the injection-production optimization unit is also used to: When it is determined that the water cut increase rate of the target reservoir is greater than a first set threshold, or the pressure fluctuation is greater than a second set threshold, the monitoring unit 501 is triggered; When it is determined that the water cut increase rate of the target reservoir is not greater than the first set threshold and the pressure fluctuation is not greater than the second set threshold, the injection-production optimization unit is triggered.

[0101] The discontinuous chemical flooding cycle combination scheme optimization device proposed in this embodiment can comprehensively consider the multiple variables and multiple constraints in the discontinuous chemical flooding cycle combination optimization scheme, use the target optimization algorithm with the highest adaptability to the target oil reservoir for iterative optimization, improve the optimization efficiency, achieve full optimization of the discontinuous chemical flooding cycle combination scheme, improve the efficiency and accuracy of the discontinuous chemical flooding cycle combination scheme optimization, increase the oil reservoir recovery rate, and improve the economic benefits of oil reservoir development.

[0102] The discontinuous chemical drive cycle combination scheme optimization device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0103] The embodiment of the present invention also provides a computer device having the above Figure 5 The discontinuous chemical flooding cycle combination scheme optimization device shown.

[0104] See also Figure 6, a structural diagram of a computer device provided by an optional embodiment of the present invention, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses for communication, and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0105] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0106] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0107] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0108] The memory 20 may include volatile memory, such as random access memory. The memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive. The memory 20 may also include a combination of the above types of memory.

[0109] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0110] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for optimizing a discontinuous chemical flooding cycle combination scheme, characterized in that: include: monitoring a permeability variation coefficient of a target oil reservoir, and determining a heterogeneity grade of the target oil reservoir based on the monitored permeability variation coefficient; According to the heterogeneity level of the target oil reservoir, searching for a target optimization algorithm corresponding to the heterogeneity level of the target oil reservoir in the created correspondence group; wherein the correspondence group includes a one-to-one correspondence between multiple heterogeneity levels and multiple optimization algorithms, and the target optimization algorithm is the optimization algorithm that has the highest adaptability to the heterogeneity level of the target oil reservoir; Based on the constructed benefit-related objective function, constraint conditions, and the target optimization algorithm of the discontinuous chemical flooding cycle combination scheme, iteratively optimize the parameter values ​​of the chemical agent combination variables to be optimized in the benefit-related objective function until the set iterative optimization end conditions are met, and obtain the optimized parameter values ​​of the chemical agent combination variables; The optimized parameter values ​​of the chemical agent combination variables are used as an optimized discontinuous chemical flooding cycle combination scheme.

2. The method according to claim 1, characterized in that The heterogeneity level of the target oil reservoir is low heterogeneity, medium heterogeneity or high heterogeneity; Determining the heterogeneity level of the target oil reservoir based on the monitored permeability variation coefficient includes: When the permeability variation coefficient is less than or equal to a set first variation coefficient threshold, determining that the heterogeneity level of the target oil reservoir is low heterogeneity; When the permeability variation coefficient is greater than a first variation coefficient threshold and less than or equal to a set second variation coefficient threshold, determining that the heterogeneity level of the target oil reservoir is medium heterogeneity; When the permeability variation coefficient is greater than the second variation coefficient threshold, the heterogeneity level of the target oil reservoir is determined to be high heterogeneity.

3. The method according to claim 2, characterized in that The multiple optimization algorithms include particle swarm optimization algorithm, differential evolution algorithm and covariance matrix adaptive evolution algorithm; When the heterogeneity level of the target reservoir is low heterogeneity, the target optimization algorithm is a covariance matrix adaptive evolutionary algorithm; When the heterogeneity level of the target reservoir is medium heterogeneity, the target optimization algorithm is a particle swarm optimization algorithm; When the heterogeneity level of the target oil reservoir is high heterogeneity, the target optimization algorithm is a differential evolution algorithm.

4. The method according to claim 1, wherein The benefit-related objective function is: ； ； ； in, NPV is the cumulative incremental net present value, T For the project life cycle, For the Total annual income; For the The total annual cost includes the purchase cost of various chemicals, injection and production operating costs, equipment depreciation and maintenance costs; r is the discount rate; For the Crude oil prices in the year; For the Annual crude oil production is related to the mixing effect of various chemicals; n is the number of types of chemical agents; For the i Unit price of the chemical; For the t Year i The amount of chemical used; For the t Annual operating costs; For the t annual equipment depreciation and maintenance costs; The annual environmental protection and governance costs.

5. The method according to any one of claims 1 to 4, characterized in that The chemical agent combination variables include concentrations, injection volumes and injection rates of multiple chemicals, and the constraints include a total concentration upper limit constraint, a total injection volume upper limit constraint, a total injection rate upper limit constraint and a chemical agent compatibility constraint.

6. The method according to claim 5, characterized in that The benefit-related objective function, constraint conditions, and target optimization algorithm of the constructed discontinuous chemical flooding cycle combination scheme are used to iteratively optimize the parameter values ​​of the chemical combination variables to be optimized in the benefit-related objective function until the set iterative optimization end conditions are met, and the optimized parameter values ​​of the chemical combination variables are obtained, including: Extracting function-related production data of the benefit-related objective function from the operating production data of the target oil reservoir; The function-related production data, the benefit-related objective function and the constraint conditions are input into the target optimization algorithm so that the target optimization algorithm: takes maximizing the function value of the benefit-related objective function as the goal, and under the condition of satisfying the constraint conditions, iteratively optimizes the concentration, injection amount and injection rate of each chemical agent in the benefit-related objective function based on the function-related production data until the set iterative optimization end conditions are met, and obtains the optimized concentration, optimized injection amount and optimized injection rate of each chemical agent.

7. The method according to claim 1, characterized in that Before searching the created correspondence group for a target optimization algorithm corresponding to the heterogeneity level of the target oil reservoir according to the heterogeneity level of the target oil reservoir, the method further includes: Create multiple numerically simulated reservoirs with different levels of heterogeneity; For any of the optimization algorithms, based on the optimization algorithm, the discontinuous chemical flooding cycle combination scheme is optimized for each of the numerical simulation reservoirs to obtain the optimized combination scheme of each of the numerical simulation reservoirs, and the recovery rate improvement value of each of the numerical simulation reservoirs is determined according to the optimized combination scheme of each of the numerical simulation reservoirs. The maximum recovery rate improvement value is determined among the recovery rate improvement values ​​of each of the numerical simulation reservoirs, and the heterogeneity level of the numerical simulation reservoir corresponding to the maximum recovery rate improvement value is determined as the heterogeneity level with the highest adaptability to the optimization algorithm.

8. The method according to claim 1, characterized in that After using the optimized parameter values ​​of the chemical combination variables as the optimized discontinuous chemical flooding cycle combination scheme, the method further includes: Optimize injection and production of the target reservoir according to the optimized discontinuous chemical flooding cycle combination scheme, and monitor key production data of the target reservoir; Based on the key production data of the target oil reservoir, determine whether to continue executing the step of optimizing the injection and production of the target oil reservoir according to the optimized discontinuous chemical flooding cycle combination scheme, or return to execute the step of monitoring the permeability variation coefficient of the target oil reservoir to re-optimize the discontinuous chemical flooding cycle combination scheme of the target oil reservoir.

9. The method according to claim 8, characterized in that When the key production data of the target oil reservoir include a water cut rising rate and a pressure fluctuation, determining, based on the key production data of the target oil reservoir, whether to continue executing the step of optimizing injection and production for the target oil reservoir according to the optimized discontinuous chemical flooding cycle combination scheme, or returning to executing the step of monitoring the permeability variation coefficient of the target oil reservoir to re-optimize the discontinuous chemical flooding cycle combination scheme for the target oil reservoir, comprises: When it is determined that the water cut increase rate of the target oil reservoir is greater than a first set threshold, or the pressure fluctuation is greater than a second set threshold, returning to the step of monitoring the permeability variation coefficient of the target oil reservoir to re-optimize the discontinuous chemical flooding cycle combination scheme of the target oil reservoir; When it is determined that the water cut increase rate of the target oil reservoir is not greater than the first set threshold and the pressure fluctuation is not greater than the second set threshold, continue to execute the step of optimizing injection and production of the target oil reservoir according to the optimized discontinuous chemical flooding cycle combination scheme.

10. A discontinuous chemical flooding cycle combination scheme optimization device, characterized in that: include: A monitoring unit for monitoring the permeability variation coefficient of the target reservoir; a determining unit, configured to determine a heterogeneity grade of the target oil reservoir based on the monitored permeability variation coefficient; a search unit configured to search, based on the heterogeneity level of the target oil reservoir, for a target optimization algorithm corresponding to the heterogeneity level of the target oil reservoir in the created correspondence group; wherein the correspondence group includes a one-to-one correspondence between multiple heterogeneity levels and multiple optimization algorithms, and the target optimization algorithm is the optimization algorithm that has the highest adaptability to the heterogeneity level of the target oil reservoir; an iterative optimization unit for iteratively optimizing the parameter values ​​of the chemical agent combination variables to be optimized in the benefit-related objective function based on the constructed benefit-related objective function, the constraint conditions, and the target optimization algorithm, until the set iterative optimization end conditions are met, and obtaining the optimized parameter values ​​of the chemical agent combination variables; As a unit, it is used to use the optimized parameter values ​​of the chemical agent combination variables as an optimized discontinuous chemical flooding cycle combination scheme.

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