CN111625925A - Ternary combination flooding injection-production optimization method based on chromatographic separation - Google Patents
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Abstract
The invention discloses a ternary combination flooding injection-production optimization method based on chromatographic separation, and particularly relates to the field of oil and gas field development. The method comprises the steps of establishing an ASP flooding reservoir geological model by utilizing reservoir data to conduct reservoir numerical simulation, selecting ASP flooding parameters from simulation results, establishing an ASP flooding chromatographic separation degree evaluation model, optimizing injection concentration of each substance in ASP flooding by utilizing a gradient-free optimization algorithm, updating chromatographic separation parameter values, judging whether increment of the updated chromatographic separation parameters meets convergence conditions or not, substituting the optimized injection concentration into the ASP flooding reservoir geological model to continue optimization if the increment of the updated chromatographic separation parameters does not meet the convergence conditions, and outputting optimal injection concentration if the increment of the updated chromatographic separation parameters does not meet the convergence conditions. The method combines a non-gradient optimization algorithm with oil reservoir numerical simulation, optimizes the optimal injection parameters based on a chromatographic separation degree evaluation model, realizes the rapid prediction of the chemical flooding effect, and has important significance for guiding the formulation of an oil reservoir chemical flooding development scheme.
Description
Technical Field
The invention relates to the field of oil and gas field development, in particular to a ternary combination flooding injection-production optimization method based on chromatographic separation.
Background
With the development of oil fields, the annual output of a plurality of oil fields in the world is reduced at present, the oil fields enter a high or ultrahigh water cut period, and the recovery efficiency is difficult to improve by only relying on water drive development. The tertiary oil recovery improves the water flooding sweep efficiency and the oil washing efficiency by injecting a chemical agent, thereby greatly improving the recovery ratio. The ternary combination flooding consists of alkali, a surfactant and a polymer, and when the ternary combination flooding flows in an oil layer, the alkali, the surfactant and the polymer are subjected to differential migration to generate a phenomenon called chromatographic separation; the ternary combination flooding utilizes the synergistic effect of alkali, surfactant and polymer to improve the respective oil displacement efficiency by times, and is widely applied to various oil fields at present.
Currently, research on ASP flooding is focused on indoor and mine tests. However, the indoor test focuses on the research on the self performance and parameters of the chemical agent through a micro-layer surface, and the optimization of the injection time and the injection concentration of the chemical agent under the complex oil reservoir condition is difficult to realize; the chemical flooding pilot and expansive mine field test is also a small-scale test, cannot reflect the overall condition of a block, and is limited by factors such as high cost, high implementation difficulty, unobvious effect and the like.
Chemical flooding numerical simulation techniques are commonly used for studies on chemical agent dosage, injection timing, injection concentration, slug design, and the like. The numerical simulation model starts from the description of a micro mechanism, can effectively simulate the real state of the chemical oil displacement system in an oil reservoir through the control of a mathematical physical equation, and has the advantages of low cost, high speed and easiness in realization.
Disclosure of Invention
The invention aims to solve the defects that the determination of the optimal injection concentration of the ternary combination flooding of a complex oil reservoir is difficult to realize in an indoor test, the test cost of a mine field is high, the implementation difficulty is high, and the effect is not obvious, and provides a ternary combination flooding injection-production optimization method based on chromatographic separation.
The invention specifically adopts the following technical scheme:
a ternary combination flooding injection-production optimization method based on chromatographic separation specifically comprises the following steps:
and 4, calculating the increment of the updated ASP flooding chromatographic separation parameter, judging whether the convergence condition is met, returning to the step 3 if the convergence condition is not met, substituting the injection concentrations of the optimized alkali, the optimized surfactant and the optimized polymer into the ASP flooding reservoir geological model, continuing iterative optimization, and outputting the injection concentrations of the alkali, the optimized surfactant and the optimized polymer in the ASP flooding as the optimal injection concentration if the convergence condition is met.
Preferably, the step 1 specifically includes the following sub-steps:
step 1.1: selecting an oil field block still having residual oil development potential in a medium-high-permeability oil field developed by water injection, and collecting oil reservoir static data and oil reservoir dynamic data;
step 1.2: and (2) integrating static data and dynamic data of the oil reservoir, establishing a ternary complex oil displacement reservoir geological model by using an oil reservoir numerical simulation software Eclipse, adjusting injection concentration parameters of alkali, a surfactant and a polymer in the ternary complex oil displacement reservoir geological model, setting the injection speed of each substance to be constant in the simulation process, and performing oil reservoir numerical simulation to obtain a ternary complex oil displacement reservoir numerical simulation result.
Preferably, the step 2 specifically includes the following sub-steps:
step 2.1: drawing a chromatographic separation curve;
selecting ternary combination flooding parameters including injection concentrations of alkali, surfactant and polymer, daily liquid yield, daily alkali yield quality, daily surfactant yield quality and daily polymer yield in ternary combination flooding of each well in a block, and respectively calculating the ratio of the extraction end concentration and the initial injection concentration in each injection amount by using the selected ternary combination flooding parameters aiming at the alkali, the surfactant and the polymer in the ternary combination flooding, wherein the initial injection concentration calculation formula is as follows:
wherein l represents an injection substance, l ═ 1 represents an alkali, l ═ 2 represents a surfactant, and l ═ 3 represents a polymer; c. ClDenotes the initial implantation concentration of the implanted species in kg/m3(ii) a h represents the number of the injection well; n represents the number of wells into which the substance l is injected; c. ClhRepresents the concentration of the injected substance l of the h-th well in kg/m3；
Selecting a reference line, taking the injection amount as the abscissa of a chromatographic separation curve, taking the ratio of the concentration of each substance extraction end in the ternary combination flooding in the injection amount to the initial injection concentration as the ordinate of the chromatographic separation curve, and drawing a chromatographic separation curve of the ternary combination flooding injection-production process aiming at alkali, a surfactant and a polymer in the ternary combination flooding;
step 2.2: calculating chromatographic separation parameters;
dividing the total production time of an oil reservoir into a plurality of time steps with the same time length, converting the production time of the oil reservoir into injection quantity by using the injection speed, defining the area enclosed by a curve connected with the lowest relative concentration points of alkali, surfactant and polymer in the ternary combination flooding corresponding to any oil reservoir production time and a reference line as the minimum area in a chromatographic separation curve, defining the area enclosed by a curve connected with the highest relative concentration points of alkali, surfactant and polymer in the ternary combination flooding corresponding to any oil reservoir production time and the reference line as the maximum area, respectively determining the minimum area and the maximum area of alkali, surfactant and polymer in the ternary combination flooding in the chromatographic separation curve by using a time grid discretization method, and defining chromatographic separation parameters as the ratio of the minimum area to the maximum area, calculating chromatographic separation parameters, the formula is as follows:
in the formula (I), the compound is shown in the specification,
representing chromatographic separation parameters, △ c (k) representing area infinitesimal formed by the connecting line of the lowest points of relative concentrations of alkali, surfactant and polymer in the three-component flooding and a reference line in a time step, △ cl(k) The area infinitesimal surrounded by a relative concentration curve of an injected substance l and a reference line in the three-component combined flooding in the time step is represented, wherein l & lt 1 & gt represents alkali, l & lt 2 & gt represents surfactant, l & lt 3 & gt represents polymer, △ ci′j(k) Representing the area infinitesimal surrounded by the difference of the relative concentrations of the substance i and the substance j and the reference line in the time step; k represents a time step; n represents the total number of time steps.
Preferably, the step 3 specifically includes the following sub-steps:
step 3.1: determining the maximum value and the minimum value of the injection concentrations of the three substances of the alkali, the surfactant and the polymer in the ASP flooding according to the injection concentrations of the alkali, the surfactant and the polymer in each well in the block, and setting constraint conditions aiming at the injection concentrations of the alkali, the surfactant and the polymer in the ASP flooding;
step 3.2: optimizing chromatographic separation parameters by using a gradient-free optimization algorithm based on social learning particle swarm, and setting gradient-free optimization algorithm parameters comprising a target function, optimization variables, population numbers, convergence conditions and particle initial speeds, wherein the target function is the chromatographic separation parameters, and the optimization variables are the injection concentrations of alkali, surfactant and polymer in the ASP flooding;
step 3.3: when a gradient-free optimization algorithm based on social learning particle swarm is used for first optimization, injection concentrations of alkali, surfactant and polymer in the ternary combination flooding are randomly generated in set constraint conditions, the injection concentrations of the alkali, the surfactant and the polymer which are randomly generated are substituted into a ternary combination flooding reservoir geological model, oil reservoir numerical simulation is carried out, and chromatographic separation parameter values are updated; when non-first-time optimization is carried out, the injection concentrations of the alkali, the surfactant and the polymer obtained by the last iterative calculation are substituted into the ternary complex reservoir displacement geological model, numerical reservoir simulation is carried out, and chromatographic separation parameter values are updated;
step 3.4: calculating a fitness parameter by using the updated ASP flooding chromatographic separation parameter value, sequencing the fitness parameters, and optimizing the injection concentrations of three substances, namely alkali, a surfactant and a polymer in the ASP flooding through a learning mechanism of social learning particle swarm.
The invention has the following beneficial effects:
the method combines a non-gradient optimization algorithm with numerical reservoir simulation, and effectively simulates the real state of the ternary composite flooding system in the reservoir; the method is based on the chromatographic separation principle, by establishing the ASP flooding chromatographic separation degree evaluation model, and utilizing the chromatographic separation curve and the chromatographic separation parameters to represent the synergistic effect of alkali, surfactant and polymer in the ASP flooding, fully reflects the relation between chemical flooding and injection and production parameters in oil field production, is beneficial to the research of chemical flooding micro mechanism, has the characteristics of low cost, high speed, good effect and easy realization, realizes the effect of quickly predicting the injection chemical flooding in a block by utilizing oil field site data, and provides reference for the decision of subsequent development schemes.
Drawings
FIG. 1 is a flow chart of a ternary combination flooding injection-production optimization method based on chromatographic separation.
FIG. 2 is a diagram illustrating the distribution of well positions in a block according to an embodiment.
FIG. 3 is a chromatogram separation parameter chart before optimization of injection concentration of each substance in ASP flooding.
Fig. 4 is a flowchart of the social learning particle swarm SLPSO algorithm.
FIG. 5 is a graph of chromatographic separation parameters optimized with a gradient-free optimization algorithm using ternary complex actuation.
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings:
a three-component combination flooding injection-production optimization method based on chromatographic separation is shown in figure 1 and specifically comprises the following steps:
step 1.1: selecting an oil field block still having residual oil development potential in a medium-high-permeability oil field developed by water injection, and collecting oil reservoir static data and oil reservoir dynamic data; the oil reservoir static data comprises formation lithology, porosity, permeability, oil saturation, wellhead coordinates, oil reservoir top depth, layered data and small-layer data; the dynamic data of the oil reservoir comprises oil reservoir fluid component parameters, rock physical property parameters, oil reservoir initial conditions and production dynamic data, wherein the oil reservoir initial conditions comprise an oil-water interface, an oil-gas interface and a pressure gradient, and the production dynamic data comprise perforation completion data, daily liquid production of each well and daily water injection of each well.
Step 1.2, integrating static information and dynamic information of an oil reservoir, establishing a ternary complex oil displacement reservoir geological model by using an oil reservoir numerical simulation software Eclipse, setting a stratum with the average thickness of 10m in the ternary complex oil displacement reservoir geological model, dividing the stratum into grids with the length and the width of 25m, totaling 25 × 25 × 1 grids, setting an inclination angle of 15 degrees and the irreducible water saturation of 14.5 percent, setting 5 injection wells and 8 extraction wells in the ternary complex oil displacement reservoir geological model, wherein the well positions are distributed as shown in figure 2, each well in the oil reservoir is produced at a constant pressure by adopting ternary complex flooding, the injection speed of each injected substance in each well in the production process is kept constant, and the production speed of each extraction well is 80m3Day, injection rate of injection well 150m3The total production time of the oil reservoir is 5000d, the time duration of each time step is set to be 100d, and the injection concentration parameters of alkali, surfactant and polymer in the ternary complex oil displacement reservoir geological model can be adjusted; and (3) operating a ternary complex reservoir geological model in reservoir numerical simulation software Eclipse, and simulating a reservoir injection and production process to obtain a reservoir numerical simulation result.
step 2.1: drawing a chromatographic separation curve;
selecting ternary combination flooding parameters from the numerical reservoir simulation result, wherein the ternary combination flooding parameters comprise alkali concentration, surfactant concentration and polymer concentration injected into the ternary combination flooding of each well in a block, daily liquid yield, daily alkali yield, daily surfactant yield and daily polymer yield;
respectively calculating initial injection concentrations of alkali, surfactant and polymer in the ASP flooding by using the selected ASP flooding parameters and adopting a formula (1), and calculating the ratio of the concentration of the extraction end to the initial injection concentration in each injection amount by adopting a formula (2) as shown in data before optimization in the table 1;
the chromatographic separation curve shown in fig. 3 is drawn for alkali, surfactant and polymer in the three-element combination flooding by taking the ratio of the concentration of the extraction end to the initial injection concentration as a reference line, taking the injection amount as a horizontal coordinate, and taking the ratio of the concentration of each substance of the three-element combination flooding in the injection amount to the initial injection concentration as a vertical coordinate.
Step 2.2: calculating chromatographic separation parameters;
the total production time of the oil reservoir is divided into time steps with the same duration, the total production time of the oil reservoir is the product of the duration of a single time step and the total time step number, the injection speed of each substance in the well is constant in the process of simulating production, and the injection amount is the product of the injection speed and the production time of the oil reservoir, so that the production time of the oil reservoir can be converted into the injection amount by utilizing the injection speed, and the injection amount of the abscissa of the chromatographic separation curve corresponds to the production time of the oil reservoir.
In a chromatographic separation curve, a time grid discretization method is utilized in the chromatographic separation curve to respectively determine the minimum area and the maximum area of the alkali, the surfactant and the polymer in the three-component flooding, a formula (2) is utilized to calculate a chromatographic separation parameter, the chromatographic separation parameter represents the chromatographic separation degree of the three-component flooding, and the larger the chromatographic separation parameter is, the better the synergistic effect of the alkali, the surfactant and the polymer in the three-component flooding is.
the method specifically comprises the following steps:
step 3.1: determining alkali C in the ASP flooding according to the injection concentration of alkali, surfactant and polymer in the ASP flooding of each well in the block of the embodiment1Has an implantation concentration of at least 1.0kg/m3Maximum 30.0kg/m3Surfactant C2Has an implantation concentration of at least 1.0kg/m3Maximum 20.0kg/m3Polymer C3Is injected richThe minimum degree is 1.0kg/m3Maximum 20.0kg/m3(ii) a Setting constraint conditions aiming at the injection concentrations of alkali, surfactant and polymer in the three-component combination flooding, wherein the constraint condition of the injection concentration of the alkali is that C is more than or equal to 1.01≤30.0kg/m3The constraint condition of the injection concentration of the surfactant is that C is more than or equal to 1.02≤20.0kg/m3The constraint condition of polymer injection concentration is that C is more than or equal to 1.03≤20.0kg/m3。
Step 3.2: optimizing chromatographic separation parameters by using a gradient-free optimization algorithm and a gradient-free optimization algorithm based on social learning particle swarm, and setting gradient-free optimization algorithm parameters, wherein the chromatographic separation parameters are set as a target function, the optimization variables are injection concentrations of alkali, surfactant and polymer in ternary combination flooding corresponding to 5 injection wells in the block of the embodiment, 15 optimization variables are counted in total, the population number is 20, a learning probability and a social influence factor are set as default values, a convergence condition is set that the increment of the target function is less than 0.001, namely the increment of the chromatographic separation parameters calculated by the iteration optimization is less than 0.001 compared with the chromatographic separation parameters obtained by the last iteration, and the learning probability and the social influence factor are set as default values.
Step 3.3: when a gradient-free optimization algorithm based on social learning particle swarm is used for carrying out first optimization, injection concentrations of alkali, surfactant and polymer are randomly generated in a constraint condition of injection concentration of each substance of the ternary combination flooding, the injection concentrations of the randomly generated alkali, surfactant and polymer are substituted into a ternary combination flooding reservoir geological model, numerical reservoir simulation is carried out, and chromatographic separation parameter values are updated; and when the rest is not optimized for the first time, substituting the injection concentrations of the alkali, the surfactant and the polymer obtained by the last iterative calculation into the ternary complex reservoir displacement geological model to perform numerical reservoir simulation and update chromatographic separation parameter values.
Step 3.4: after the ASP flooding chromatographic separation parameter value is optimized each time, calculating the fitness parameter by using the learning mechanism of social learning particle swarm through the ASP flooding chromatographic separation parameter value, sequencing the fitness parameters and optimizing the injection concentration of three substances, namely alkali, surfactant and polymer in the ASP flooding, as shown in figure 4.
And 4, calculating the increment of the updated ASP flooding chromatographic separation parameter, judging whether the convergence condition is met, returning to the step 3 if the convergence condition is not met, substituting the injection concentrations of the optimized alkali, the optimized surfactant and the optimized polymer into the ASP flooding reservoir geological model, continuously performing iterative optimization, and outputting the injection concentrations of the alkali, the optimized surfactant and the optimized polymer in the ASP flooding as the optimal injection concentrations if the convergence condition is met, wherein the optimal injection concentrations are obtained after the optimization is performed on the embodiment by adopting the method disclosed by the invention shown in the table 1.
TABLE 1 injection concentration of each substance before and after optimization in ASP flooding
As shown in table 1, the three-component combination flooding injection-production optimization method based on chromatographic separation is optimized, and the chromatographic separation parameters are improved from 0.5492 to 0.7923 under the condition that the three-component combination flooding dosage is slightly increased; substituting the optimal injection concentration into the ternary complex flooding reservoir geological model, calculating by utilizing a numerical reservoir simulation result obtained by simulation, drawing a chromatographic separation curve, and comparing the chromatographic separation curves before and after optimization to find that the contact ratio of the concentration of the alkali, the surfactant and the polymer at the extraction end in the ternary complex flooding after optimization to the initial injection concentration ratio curve is greatly improved, which shows that after the optimization by the method, the synergistic flooding effect of the alkali, the surfactant and the polymer in the ternary complex flooding is obviously improved, and the flooding effect of the ternary complex flooding in the actual production process is favorably improved.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.
Claims (4)
1. A ternary combination flooding injection-production optimization method based on chromatographic separation is characterized by comprising the following steps:
step 1, acquiring oil reservoir data, establishing a ternary combination flooding oil reservoir geological model by using an oil reservoir numerical simulation software Eclipse, and performing oil reservoir numerical simulation to obtain an oil reservoir numerical simulation result;
step 2, selecting an ASP flooding parameter by using an oil reservoir numerical simulation result, and establishing an ASP flooding chromatographic separation degree evaluation model consisting of a chromatographic separation curve and a chromatographic separation parameter;
step 3, setting a gradient-free optimization algorithm parameter by using a gradient-free optimization algorithm based on social learning particle swarm, updating the chromatographic separation parameter value of the ASP flooding, and optimizing the injection concentration of alkali, surfactant and polymer in the ASP flooding;
and 4, calculating the increment of the updated ASP flooding chromatographic separation parameter, judging whether the convergence condition is met, returning to the step 3 if the convergence condition is not met, substituting the injection concentrations of the optimized alkali, the optimized surfactant and the optimized polymer into the ASP flooding reservoir geological model, continuing iterative optimization, and outputting the injection concentrations of the alkali, the optimized surfactant and the optimized polymer in the ASP flooding as the optimal injection concentration if the convergence condition is met.
2. The method for optimizing ASP flooding injection and production based on chromatographic separation as claimed in claim 1, wherein the step 1 specifically comprises the following substeps:
step 1.1: selecting an oil field block still having residual oil development potential in a medium-high-permeability oil field developed by water injection, and collecting oil reservoir static data and oil reservoir dynamic data;
step 1.2: and (2) integrating static data and dynamic data of the oil reservoir, establishing a ternary complex oil displacement reservoir geological model by using an oil reservoir numerical simulation software Eclipse, adjusting injection concentration parameters of alkali, a surfactant and a polymer in the ternary complex oil displacement reservoir geological model, setting the injection speed of each substance to be constant in the simulation process, and performing oil reservoir numerical simulation to obtain a ternary complex oil displacement reservoir numerical simulation result.
3. The method for optimizing ASP flooding injection and production based on chromatographic separation as claimed in claim 1, wherein the step 2 specifically comprises the following substeps:
step 2.1: drawing a chromatographic separation curve;
selecting ternary combination flooding parameters including injection concentrations of alkali, surfactant and polymer, daily liquid yield, daily alkali yield quality, daily surfactant yield quality and daily polymer yield in ternary combination flooding of each well in a block, and respectively calculating the ratio of the extraction end concentration and the initial injection concentration in each injection amount by using the selected ternary combination flooding parameters aiming at the alkali, the surfactant and the polymer in the ternary combination flooding, wherein the initial injection concentration calculation formula is as follows:
wherein l represents an injection substance, l ═ 1 represents an alkali, l ═ 2 represents a surfactant, and l ═ 3 represents a polymer; c. ClDenotes the initial implantation concentration of the implanted species in kg/m3(ii) a h represents the number of the injection well; n represents the number of wells into which the substance l is injected; c. ClhRepresents the concentration of the injected substance l of the h-th well in kg/m3；
Selecting a reference line, taking the injection amount as the abscissa of a chromatographic separation curve, taking the ratio of the concentration of each substance extraction end in the ternary combination flooding in the injection amount to the initial injection concentration as the ordinate of the chromatographic separation curve, and drawing a chromatographic separation curve of the ternary combination flooding injection-production process aiming at alkali, a surfactant and a polymer in the ternary combination flooding;
step 2.2: calculating chromatographic separation parameters;
dividing the total production time of an oil reservoir into a plurality of time steps with the same time length, converting the production time of the oil reservoir into injection quantity by using the injection speed, defining the area enclosed by a curve connected with the lowest relative concentration points of alkali, surfactant and polymer in the ternary combination flooding corresponding to any oil reservoir production time and a reference line as the minimum area in a chromatographic separation curve, defining the area enclosed by a curve connected with the highest relative concentration points of alkali, surfactant and polymer in the ternary combination flooding corresponding to any oil reservoir production time and the reference line as the maximum area, respectively determining the minimum area and the maximum area of alkali, surfactant and polymer in the ternary combination flooding in the chromatographic separation curve by using a time grid discretization method, and defining chromatographic separation parameters as the ratio of the minimum area to the maximum area, calculating chromatographic separation parameters, the formula is as follows:
in the formula (I), the compound is shown in the specification,
representing chromatographic separation parameters, △ c (k) representing area infinitesimal formed by the connecting line of the lowest points of relative concentrations of alkali, surfactant and polymer in the three-component flooding and a reference line in a time step, △ cl(k) The area infinitesimal enclosed by a relative concentration curve of an injected substance l and a reference line in the three-component composite flooding in a time step is shown, wherein l-1 represents alkali, l-2 represents surfactant, l-3 represents polymer, and △ c'ij(k) Representing the area infinitesimal surrounded by the difference of the relative concentrations of the substance i and the substance j and the reference line in the time step; k represents a time step; n represents the total number of time steps.
4. The method for optimizing ASP flooding injection and production based on chromatographic separation as claimed in claim 1, wherein the step 3 specifically comprises the following substeps:
step 3.1: determining the maximum value and the minimum value of the injection concentrations of the three substances of the alkali, the surfactant and the polymer in the ASP flooding according to the injection concentrations of the alkali, the surfactant and the polymer in each well in the block, and setting constraint conditions aiming at the injection concentrations of the alkali, the surfactant and the polymer in the ASP flooding;
step 3.2: optimizing chromatographic separation parameters by using a gradient-free optimization algorithm based on social learning particle swarm, and setting gradient-free optimization algorithm parameters comprising a target function, optimization variables, population numbers, convergence conditions and particle initial speeds, wherein the target function is the chromatographic separation parameters, and the optimization variables are the injection concentrations of alkali, surfactant and polymer in the ASP flooding;
step 3.3: when a gradient-free optimization algorithm based on social learning particle swarm is used for first optimization, injection concentrations of alkali, surfactant and polymer in the ternary combination flooding are randomly generated in set constraint conditions, the injection concentrations of the alkali, the surfactant and the polymer which are randomly generated are substituted into a ternary combination flooding reservoir geological model, oil reservoir numerical simulation is carried out, and chromatographic separation parameter values are updated; when non-first-time optimization is carried out, the injection concentrations of the alkali, the surfactant and the polymer obtained by the last iterative calculation are substituted into the ternary complex reservoir displacement geological model, numerical reservoir simulation is carried out, and chromatographic separation parameter values are updated;
step 3.4: calculating a fitness parameter by using the updated ASP flooding chromatographic separation parameter value, sequencing the fitness parameters, and optimizing the injection concentrations of three substances, namely alkali, a surfactant and a polymer in the ASP flooding through a learning mechanism of social learning particle swarm.
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