A numerical simulation method for characterizing the mechanism of reduced CO2-crude oil miscibility pressure

Abstract

This invention discloses a numerical simulation method for characterizing the mechanism of reducing CO2-crude oil miscibility pressure, belonging to the field of numerical simulation for CO2-driven reservoir development. The method includes: constructing a three-dimensional reservoir numerical model and a capillary numerical model based on geological and fluid data of the target reservoir in the field; determining the minimum miscibility pressure and recovery rate after adding a miscibility reducer through capillary experiments; generating miscibility characteristic baseline data based on the minimum miscibility pressure and recovery rate; adding light hydrocarbons to the capillary numerical model to simulate the miscibility reducer injection process; adjusting the binary interaction coefficients between CO2 and crude oil components using response surface methodology based on the miscibility characteristic baseline data; and inputting the fitted binary interaction coefficients into the three-dimensional reservoir numerical model to quantitatively analyze the changes in interfacial tension and crude oil viscosity after adding the miscibility reducer. This application provides a quantitative basis for optimizing miscibility reducer injection, offering precise guidance for field schemes to suppress gas channeling and improve miscibility efficiency.

Description

Technical Field

[0001] This invention belongs to the field of numerical simulation for CO2-driven oil reservoir development, and particularly relates to a numerical simulation method for characterizing the mechanism of reducing CO2-crude oil miscibility pressure. Background Technology

[0002] The minimum miscibility pressure (MMP) of CO2 and crude oil is a core parameter determining oil displacement efficiency. Lowering the MMP can significantly reduce injection energy consumption and expand the miscibility sweep range, which is crucial for the efficient development of low-permeability reservoirs in oil fields. Currently, research on the reduction of crude oil MMP by miscibility modifiers mainly relies on experimental methods: MMP at different proportions of miscibility modifiers is determined through capillary experiments, and its impact on interfacial tension and crude oil swelling is evaluated in combination with core displacement experiments, thereby determining the injection method and dosage of miscibility modifiers.

[0003] However, existing technologies have significant limitations. First, the mechanism characterization relies on experiments and is difficult to extend to the field scale. The microscopic mechanism by which miscible agents reduce MMP is mainly revealed through small-scale laboratory experiments. The experimental results can only reflect local core mechanisms and cannot be directly mapped to the three-dimensional spatial distribution characteristics of field-level reservoirs. Second, numerical simulation characterization is lacking, and parameter optimization lacks support. In existing numerical models, key parameters such as the binary interaction coefficient between CO2 and crude oil components and the mass transfer coefficient between the miscible agent and crude oil are mostly based on empirical settings and are not dynamically calibrated in conjunction with experimental data of the miscible agent, making it impossible to accurately predict the distribution of the miscible zone under field conditions. Finally, the optimization of field injection schemes is limited. Due to the lack of reliable numerical simulation methods, the optimization of injection parameters for the miscible agent still relies on experimental experience and is difficult to coordinate with the actual characteristics of the reservoir, resulting in problems such as premature gas channeling and lower-than-expected miscibility efficiency in field applications. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a numerical simulation method to characterize the mechanism of reducing CO2-crude oil miscibility pressure. By calibrating key parameters under the action of demixing agents, a numerical model that can characterize the demixing mechanism is constructed, providing support for the optimization of demixing agent injection parameters at the field scale, thereby solving the problems existing in the prior art.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a numerical simulation method for characterizing the mechanism of reducing CO2-crude oil miscibility pressure, comprising:

[0006] Based on the geological and fluid data of the target oil reservoir in the mining area, a three-dimensional reservoir numerical model and a capillary numerical model are constructed.

[0007] The minimum miscibility pressure and recovery rate after adding the miscibility demixing agent were determined by the thin tube test, and miscibility characteristic benchmark data were generated based on the minimum miscibility pressure and recovery rate.

[0008] A light hydrocarbon simulation demixing agent injection process was added to the capillary numerical model. Based on the miscibility characteristic baseline data, the binary interaction coefficient between CO2 and crude oil components was adjusted using the response surface methodology.

[0009] The fitted binary interaction coefficients are input into the three-dimensional reservoir numerical model to quantitatively analyze the changes in interfacial tension and crude oil viscosity after the addition of the demixing agent.

[0010] Preferably, the process of constructing the three-dimensional reservoir numerical model and the capillary numerical model includes:

[0011] Key fluid property data of the target reservoir were obtained based on indoor experiments;

[0012] The Peng-Robinson equation is used to fit the crude oil phase parameters and minimum miscibility pressure in the key fluid property data. When the fitting accuracy is higher than 90%, the numerical model of fluid composition is output.

[0013] Based on the reservoir geological parameters, a three-dimensional reservoir numerical model and a capillary numerical model are constructed using the fluid component numerical model.

[0014] Preferably, the capillary experiment includes:

[0015] A thin tube model of fixed specifications was selected to determine the pore volume after kerosene saturation;

[0016] Inject formation crude oil into the thin tube model at a fixed rate until the composition of the produced fluid is consistent with the original formation crude oil. Continue injecting until the cumulative injection volume reaches twice the pore volume, and record the original crude oil volume.

[0017] A mixture containing a demixing agent was injected into a capillary model of saturated crude oil using a constant pressure displacement method, with the displacement pressure being 0.1 MPa higher than the back pressure valve set pressure.

[0018] For every 0.1 times the pore volume of the mixed system injected, the injection pressure, produced oil volume, produced gas volume, and gas-oil ratio are recorded until the cumulative injection volume reaches 1.2 times the pore volume.

[0019] The experimental pressure was gradually increased by 2 MPa and the displacement process was repeated until the recovery rate reached over 90%.

[0020] Preferably, the process of adjusting the binary interaction coefficient between CO2 and crude oil components includes:

[0021] Obtain the binary interaction coefficient between CO2 and crude oil components to be adjusted and the adjustment range;

[0022] The Box-Behnken design was used to generate a combination of coefficients, and the deviation between the simulated and experimental values ​​of the minimum miscibility pressure was calculated by substituting them into the capillary numerical model.

[0023] Using the bias-weighted average as the objective function, a response surface model is constructed using a quadratic polynomial to fit the mapping relationship between the binary interaction coefficients and the objective function;

[0024] Based on the aforementioned response surface model, and with the deviation threshold as a constraint, the optimal binary interaction coefficients are obtained through a genetic algorithm.

[0025] Preferably, the formula for calculating the deviation between the simulated and experimental values ​​of the minimum miscibility pressure is as follows:

[0026] ΔP=|P i -P ex | / P ex ×100%

[0027] Among them, P i To simulate the minimum miscibility pressure, P ex This is the minimum miscibility pressure value in the experiment.

[0028] Preferably, the response surface model is:

[0029] Δ=β0+β1k1+β2k2+β3k3+β 11 k1 2 +β 22 k2 2 +β 33 k3 2 +β 12 k1k2+β 13 k1k3+β 23 k2k3

[0030] Among them, β0, β i β ii β ij These are the constant term coefficients, linear term coefficients, quadratic term coefficients, and interaction term coefficients of the model. The constant term coefficients reflect the basic bias level of the model when there is no influence from independent variables. The linear term coefficients represent the degree and direction of the linear influence of their respective binary interaction coefficients on the objective function. The quadratic term coefficients reflect the influence of the rate of change of the interaction coefficients on the bias. The interaction coefficients are used to quantify the combined effect of different interaction coefficients on the bias when they change together. k1 is the interaction coefficient between CO2 and C1, k2 is the interaction coefficient between CO2 and C2-C6, and k3 is the interaction coefficient between CO2 and C... 7+ The interaction coefficients k1, k2, and k3 describe the strength of molecular interactions between CO2 and different carbon groups in crude oil, respectively, and affect the phase characteristics, miscibility pressure, and displacement efficiency of the CO2-crude oil system.

[0031] Preferably, the genetic algorithm includes:

[0032] N sets of parameter combinations are randomly generated within a preset parameter range, serving as individuals;

[0033] Each set of parameters is input into the capillary numerical model, and the fitness value is calculated based on the output miscibility parameters.

[0034] Individuals are selected using the roulette wheel selection method, and the optimal individual is retained based on the fitness value. The optimal individual is then subjected to single-point crossover and random perturbation mutation.

[0035] Iterate until a preset number of times, then output the optimal binary interaction coefficient.

[0036] Preferably, the quantitative analysis of changes in interfacial tension and crude oil viscosity after adding a demixing agent includes:

[0037] Output the interfacial tension field diagrams before and after adding the demixing agent;

[0038] Output crude oil viscosity field diagrams before and after adding the demixing agent;

[0039] Output the miscibility range field diagram before and after adding the demisting agent.

[0040] In a second aspect, the present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0041] Thirdly, the present invention also discloses a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in the first aspect.

[0042] Compared with the prior art, the present invention has the following advantages and technical effects:

[0043] This invention provides a numerical simulation method for characterizing the mechanism of reducing CO2-crude oil miscibility pressure, comprising: constructing a three-dimensional reservoir numerical model and a capillary numerical model based on geological and fluid data of the target reservoir in the field; determining the minimum miscibility pressure and recovery rate after adding a miscibility reducer through capillary experiments, and generating miscibility characteristic baseline data based on the minimum miscibility pressure and recovery rate; adding light hydrocarbons to the capillary numerical model to simulate the miscibility reducer injection process, and adjusting the binary interaction coefficients between CO2 and crude oil components using response surface methodology based on the miscibility characteristic baseline data; inputting the fitted binary interaction coefficients into the three-dimensional reservoir numerical model to quantitatively analyze the changes in interfacial tension and crude oil viscosity after adding the miscibility reducer.

[0044] This invention, based on the output interface tension field map, crude oil viscosity field map, and miscibility range field map of the calibrated binary interaction coefficients, breaks through the limitations of experimental scale and realizes three-dimensional spatial mechanism characterization at the mine level.

[0045] This invention uses response surface methodology to force dynamic calibration of the CO2-crude oil interaction coefficient based on thin-tube experimental data, eliminating empirical errors in key parameters and ensuring the reliability of miscibility pressure prediction.

[0046] This application provides quantitative basis for optimizing the injection of demixing agents, and provides precise guidance for mine solutions to suppress gas channeling and improve miscibility. Attached Figure Description

[0047] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0048] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of a three-dimensional reservoir numerical model according to an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of the numerical model of the thin tube according to an embodiment of the present invention;

[0051] Figure 4 This is a graph showing the experimental results of the minimum miscibility pressure before the addition of the demisting agent in an embodiment of the present invention;

[0052] Figure 5 This is a graph showing the experimental results of minimum miscibility pressure after adding a demisting agent according to an embodiment of the present invention;

[0053] Figure 6 This is an interfacial tension field diagram before and after the addition of the demixing agent in an embodiment of the present invention;

[0054] Figure 7 This is a crude oil viscosity field diagram before and after the addition of the demixing agent in an embodiment of the present invention;

[0055] Figure 8 This is a field diagram showing the miscibility range before and after the addition of the demisting agent in an embodiment of the present invention. Detailed Implementation

[0056] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0057] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0058] Example 1

[0059] like Figure 1 As shown, this embodiment provides a numerical simulation method for characterizing the mechanism of reducing CO2-crude oil miscibility pressure, including:

[0060] Step S1: Construct a three-dimensional reservoir numerical model and a capillary numerical model based on the geological and fluid data of the target reservoir;

[0061] Specifically, constructing the aforementioned three-dimensional reservoir numerical model and capillary numerical model includes:

[0062] (1) Based on indoor experiments, key fluid property data such as relative permeability of oil and gas, crude oil composition, minimum miscibility pressure of CO2 and crude oil, crude oil density, crude oil viscosity, and gas-oil ratio were obtained.

[0063] (2) Based on the key fluid property data, the Peng-Robinson equation is used to fit the crude oil phase parameters and minimum miscibility pressure. When the fitting accuracy is higher than 90%, the numerical model of fluid composition is output.

[0064] (3) Based on the fluid component numerical model, a three-dimensional reservoir numerical model and a capillary model are constructed in combination with reservoir geological parameters. The reservoir geological parameters include: reservoir porosity and permeability, reservoir thickness and depth, formation temperature and pressure, and oil saturation.

[0065] Step S2: Determine the minimum miscibility pressure (MMP) and recovery rate after adding the miscibility degrading agent through a thin tube experiment to determine the miscibility characteristic baseline data;

[0066] The specific steps for determining the minimum miscibility pressure and recovery rate after adding a miscibility reducer include:

[0067] (1) Select a thin tube model with a length of not less than 10m and an inner diameter of 3.5-8mm. After cleaning, saturate it with kerosene and measure the pore volume (denoted as PV). Place the thin tube model in a constant temperature chamber and set the temperature to be consistent with the target reservoir temperature.

[0068] (2) Prepare formation crude oil samples, CO2 gas with a purity of ≥99%, and demixing agent;

[0069] (3) Inject formation crude oil into the capillary model at a rate of 0.2 ml / min to displace kerosene until the crude oil composition in the produced fluid is consistent with that of the original formation crude oil (verified by gas chromatography analysis). Continue injecting crude oil until the cumulative injection volume reaches twice the pore volume to ensure that the model is completely saturated with crude oil. Record the amount of original crude oil in the capillary model at this time (denoted as N). o );

[0070] (4) The mixed system was injected into the thin tube model by constant pressure displacement. The displacement pressure was 0.1 MPa higher than the back pressure valve set pressure. The back pressure valve pressure simulated the bottom flow pressure of the production well.

[0071] (5) During the displacement process, for every 0.1 PV of the mixed system injected, the injection pressure, the volume of produced oil, the volume of produced gas and the gas-oil ratio are recorded until the cumulative injection amount reaches 1.2 PV;

[0072] (6) After the first displacement is completed, increase the experimental pressure (gradient of 2 MPa / time) and repeat the above displacement process until the recovery rate of crude oil reaches more than 90%.

[0073] Step S3: Add light hydrocarbon simulation demixing agent injection process to the capillary tube numerical model, and adjust the binary interaction coefficient between CO2 and crude oil components using response surface methodology based on capillary tube experimental data, so that the accuracy between simulated MMP and experimental values ​​is higher than 90%.

[0074] Specifically, establishing the response surface methodology for fitting the CO2-crude oil component binary interaction coefficients includes:

[0075] (1) Determine the binary interaction coefficient between CO2 and crude oil components to be adjusted and the adjustment range;

[0076] (2) The Box-Behnken design was used to generate a coefficient combination scheme, which was then substituted into the thin tube numerical model to obtain the deviation between the simulated and experimental values ​​of each set of coefficients.

[0077] (3) Using the bias-weighted average as the objective function, a response surface model is constructed using a quadratic polynomial. The fitting coefficients are mapped to the objective function, and the fitting accuracy is ≥95%.

[0078] (4) Based on the response surface model, with the deviation ≤5% as a constraint, the optimal binary interaction coefficient is obtained by using a genetic algorithm.

[0079] In this embodiment, the binary interaction coefficients include:

[0080] The interaction coefficients (k1) between CO2 and C1, (k2) between CO2 and C2-C6, and between CO2 and C 7+ The interaction coefficient (k3).

[0081] In this embodiment, the MMP deviation is:

[0082] ΔP=|P i -P ex | / P ex ×100%

[0083] Among them, P i To simulate the minimum miscibility pressure, P ex This is the minimum miscibility pressure value in the experiment.

[0084] In this embodiment, the response surface model is:

[0085] Δ=β0+β1k1+β2k2+β3k3+β 11 k1 2 +β 22 k2 2 +β 33 k3 2 +β 12 k1k2+β 13 k1k3+β 23 k2k3

[0086] Among them, β0, β i β ii β ij These are the constant term coefficients, linear term coefficients, quadratic term coefficients, and interaction term coefficients of the model. The constant term coefficients reflect the basic bias level of the model when there is no influence from independent variables. The linear term coefficients represent the degree and direction of the linear influence of their respective binary interaction coefficients on the objective function. The quadratic term coefficients reflect the influence of the rate of change of the interaction coefficients on the bias. The interaction coefficients are used to quantify the combined effect of different interaction coefficients on the bias when they change together. k1 is the interaction coefficient between CO2 and C1, k2 is the interaction coefficient between CO2 and C2-C6, and k3 is the interaction coefficient between CO2 and C... 7+ The interaction coefficients k1, k2, and k3 describe the strength of molecular interactions between CO2 and different carbon groups in crude oil, respectively, and affect the phase characteristics, miscibility pressure, and displacement efficiency of the CO2-crude oil system.

[0087] In this embodiment, the genetic algorithm is:

[0088] (1) Randomly generate N sets of parameter combinations (N≥50) within the preset parameter range, with each set of parameters as an individual;

[0089] (2) Input each set of parameters into the numerical model, output the miscibility characteristic parameters (minimum miscibility pressure, recovery rate, etc.), substitute them into the fitness function to calculate the individual fitness value. The higher the fitness value, the better the parameter combination.

[0090] (3) A roulette wheel selection method is used to select dominant individuals for the next generation based on their fitness values, retaining the best individuals (elite retention strategy). Selected individuals undergo single-point crossover with a crossover probability of 0.6-0.8 to generate offspring parameter combinations. The parameter values ​​of offspring individuals are randomly perturbed with a mutation probability of 0.01-0.05 to avoid local optima.

[0091] (4) Repeat the above steps until the number of iterations reaches the preset value (e.g., 100 times) or the difference between the optimal fitness values ​​of two adjacent generations is ≤1%, and output the final optimal parameter combination.

[0092] Step S4: Based on the three-dimensional numerical model, input the fitted binary interaction coefficients to quantify the synergistic mechanism of "light hydrocarbons promoting CO2 dissolution - reducing interfacial tension - expanding the miscibility zone".

[0093] Specifically, quantifying the synergistic mechanism of "light hydrocarbons promoting CO2 dissolution - reducing interfacial tension - expanding the miscibility zone" involves quantitatively analyzing the changes in interfacial tension and crude oil viscosity after adding the miscibility reducer, including:

[0094] Output the interfacial tension field diagrams before and after adding the demixing agent;

[0095] Output crude oil viscosity field diagrams before and after adding the demixing agent;

[0096] Output the miscibility range field diagram before and after adding the demisting agent.

[0097] This embodiment uses a certain oil reservoir as an example. The reservoir has an average porosity of 5.7%, an average permeability of 5-10 mD, a formation pressure and saturation pressure of 36.1 MPa, a formation temperature of 154.2℃, crude oil viscosity and volume coefficient of 0.163 mPa·s and 1.899, respectively, and a dissolved gas-oil ratio of 273 mPa·s. 3 / m 3 The specific steps are as follows:

[0098] (1) Construct a three-dimensional reservoir numerical model and a capillary numerical model based on the geological and fluid data of the target reservoir.

[0099] 1.1 Collect reservoir geological data, as shown in Table 1;

[0100] Table 1

[0101]

[0102] 1.2 Collect fluid experiment data, including the composition of well fluid components, as shown in Table 2. Crude oil characteristics, as shown in Table 3;

[0103] Table 2

[0104]

[0105] Table 3

[0106]

[0107] 1.3 Combining fluid properties and reservoir geological parameters, a three-dimensional reservoir numerical model and a capillary numerical model were established using the CMG-GEM simulator, such as... Figure 2 , Figure 3 As shown.

[0108] (2) Minimum miscibility pressure (MMP) and recovery rate before and after the addition of the miscibility degrading agent were determined by thin-tube experiments to establish baseline data for miscibility characteristics.

[0109] 2.1 The minimum miscibility pressure of CO2-crude oil before the addition of the miscibility modifier was determined based on a capillary tube experiment. The results are as follows: Figure 4 As shown;

[0110] 2.2 The minimum miscibility pressure of CO2-crude oil after the addition of a demisting agent was determined based on a capillary tube experiment. The results are as follows: Figure 5 As shown;

[0111] (3) The injection process of light hydrocarbons to simulate the demixing agent was added to the capillary tube numerical model. Based on the capillary tube experimental data, the binary interaction coefficient between CO2 and crude oil components was adjusted using the response surface methodology to make the accuracy between the simulated MMP and the experimental values ​​higher than 90%.

[0112] 3.1 Determine the binary interaction coefficients between CO2 and crude oil components to be adjusted and their adjustment range: interaction coefficient between CO2 and C1 (k1), interaction coefficient between CO2 and C2-C6 (k2), and interaction coefficient between CO2 and C... 7+ The interaction coefficient (k3) is adjusted within the ranges of 0.1-0.2, 0.05-0.15, and 0.15-0.3, respectively.

[0113] 3.2 The binary interaction coefficients between CO2 and crude oil components were adjusted using the response surface methodology, and the fitting results are shown in Table 4.

[0114] Table 4

[0115]

[0116] (4) Based on the three-dimensional numerical model, input the fitted binary interaction coefficients to quantify the synergistic mechanism of "light hydrocarbons promoting CO2 dissolution - reducing interfacial tension - expanding the miscible zone".

[0117] 4.1 Based on the SR3 file generated by the modeler before and after the addition of the demixing agent, select the oil-gas interfacial tension results and output the interfacial tension field diagrams before and after the addition of the demixing agent, as shown below. Figure 6 As shown;

[0118] 4.2 Based on the modeler-generated result file SR3 before and after adding the demixing agent, select the crude oil viscosity result and output the crude oil viscosity field diagram before and after adding the demixing agent, as shown below. Figure 7 As shown;

[0119] 4.3 Based on the modeler-generated result file SR3 before and after the addition of the demixing agent, the CO2 concentration results are combined with the oil-gas interfacial tension results to output the miscibility range field diagrams before and after the addition of the demixing agent, such as... Figure 8 As shown.

[0120] Example 2

[0121] This embodiment also discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0122] Example 3

[0123] This embodiment also discloses a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0124] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A numerical simulation method for characterizing the mechanism of reducing the CO2-crude oil miscibility pressure, characterized in that, The method comprises the following steps: Based on the geological data and fluid data of the target reservoir in the mine field, a three-dimensional reservoir numerical model and a capillary tube numerical model are constructed; The minimum miscibility pressure and recovery rate after adding the demulsifying agent are determined through capillary tube experiments, and based on the minimum miscibility pressure and recovery rate, miscible characteristic reference data are generated; A light hydrocarbon simulation demulsifying agent injection process is added to the capillary tube numerical model, and based on the miscible characteristic reference data, a response surface method is used to adjust the binary interaction coefficient of CO2 and crude oil components; The process of adjusting the binary interaction coefficient of CO2 and crude oil components comprises: Obtaining the binary interaction coefficient of CO2 and crude oil components to be adjusted and the adjustment range; Using Box-Behnken design to generate a combination scheme of coefficients, and substituting it into the capillary tube numerical model to calculate the deviation of the minimum miscibility pressure simulation value and the experimental value; Taking the weighted average value of the deviation as the objective function, a quadratic polynomial is used to construct a response surface model to fit the mapping relationship between the binary interaction coefficient and the objective function; Based on the response surface model, the optimal binary interaction coefficient is obtained by genetic algorithm optimization with the deviation threshold as the constraint; The binary interaction coefficients include: CO2 and C1 interaction coefficient, CO2 and C2-C6 interaction coefficient, CO2 and C 7+ interaction coefficient; The fitted binary interaction coefficient is input into the three-dimensional reservoir numerical model to quantitatively analyze the changes of the interfacial tension and the crude oil viscosity after adding the demulsifying agent; The quantitative analysis of the changes of the interfacial tension and the crude oil viscosity after adding the demulsifying agent comprises: Outputting the interfacial tension field map before and after adding the demulsifying agent; Outputting the crude oil viscosity field map before and after adding the demulsifying agent; Outputting the miscible range field map before and after adding the demulsifying agent.

2. The method of claim 1, wherein The process of constructing a three-dimensional reservoir numerical model and a capillary tube numerical model comprises: Based on indoor experiments, key fluid property data of the target reservoir are obtained; The Peng-Robinson equation is used to fit the crude oil phase state parameters and the minimum miscibility pressure in the key fluid property data, and when the fitting accuracy is higher than 90%, a fluid component numerical model is output; Based on the fluid component numerical model, a three-dimensional reservoir numerical model and a capillary tube numerical model are constructed in combination with reservoir geological parameters.

3. The method of claim 1, wherein The capillary tube experiment comprises: Selecting a capillary tube model of a fixed specification to measure the pore volume after kerosene saturation; Injecting formation crude oil into the capillary tube model at a fixed speed until the output fluid component is consistent with the original formation crude oil, and continuing to inject until the cumulative injection amount reaches 2 times the pore volume, and recording the original crude oil amount; Injecting a mixed system containing a demulsifying agent into the capillary tube model saturated with crude oil in a constant pressure displacement manner, and the displacement pressure is higher than the set pressure of the back pressure valve by 0.1 MPa; Every time 0.1 times the pore volume of the mixed system is injected, the injection pressure, the output oil volume, the output gas volume, and the gas oil ratio are recorded, until the cumulative injection amount reaches 1.2 times the pore volume; The experimental pressure is gradually increased by 2 MPa, and the displacement process is repeated until the recovery rate is higher than 90%.

4. The method of claim 1, wherein The calculation formula of the deviation of the minimum miscibility pressure simulation value and the experimental value is: ΔP = |P i - P ex | / P ex x 100% where P i is the simulated minimum miscibility pressure value, P ex is the experimental minimum miscibility pressure value.

5. The method of claim 1, wherein The response surface model is: Delta = beta0 + beta1k1 + beta2k2 + beta3k3 + beta 11 k1 2 + beta 22 k2 2 + beta 33 k3 2 + beta 12 k1k2 + beta 13 k1k3 + beta 23 k2k3 wherein β0, β i , β ii , and β ij are constant term coefficient, linear term coefficient, quadratic term coefficient, and interaction term coefficient of the model, respectively, the constant term coefficient reflects the basic bias level of the model without the influence of independent variables, the linear term coefficient represents the linear influence degree and direction of each corresponding binary interaction coefficient on the target function, the quadratic term coefficient reflects the influence degree of the change rate of the interaction coefficient on the bias, and the interaction coefficient is used to quantify the comprehensive action degree of the joint change of different interaction coefficients on the bias; k1 is the interaction coefficient of CO2 and C1, k2 is the interaction coefficient of CO2 and C2-C6, and k3 is the interaction coefficient of CO2 and C 7+ ; k1, k2, and k3 respectively describe the strength of molecular interaction between CO2 and different carbon groups in crude oil, which affects the phase state characteristics, miscibility pressure, and displacement efficiency of the CO2-crude oil system.

6. The method of claim 1, wherein The genetic algorithm comprises: Randomly generating N groups of parameter combinations within a preset parameter range as individuals; Inputting each group of parameters into a capillary numerical model, and calculating a fitness value based on an output miscibility characteristic parameter; Screening the individuals by using a roulette selection method, reserving an optimal individual based on the fitness value, and performing single-point crossover and random disturbance mutation on the optimal individual; Iterating to a preset number of times, and outputting an optimal binary interaction coefficient.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the steps of the method of any one of claims 1-6.

8. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the steps of the method of any one of claims 1-6.

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