Low-permeability reservoir channeling control oil displacement strategy acquisition method based on pore network simulation

Abstract

The invention relates to a low-permeability oil reservoir channeling control oil displacement strategy obtaining method based on pore network simulation. The method comprises the steps that a three-dimensional pore structure of a core sample is obtained, a pore network model is established, multiphase fluid numerical simulation is conducted, and relative permeability parameters of an oil reservoir are determined; original data of the block oil reservoir are obtained, a three-dimensional geologic model is constructed, and relative permeability parameters are imported; geologic features, fluid properties and production history of an oil reservoir are considered, a well pattern injection-production mode and the optimal positions of an injection well and a production well are determined through the three-dimensional geologic model, and an injection-production scheme is formulated; through numerical simulation, based on sensitivity analysis, sensitive factors influencing the recovery degree are screened from engineering factors and geological factors; through numerical simulation, the recovery degree of each stage is used as an evaluation index, and sensitive factor parameters are optimized; and generating an oil displacement strategy based on the optimized sensitive factor parameters. According to the method, the relative permeability parameters are obtained in a simulation mode, then the oil displacement parameters are optimized through simulation screening, and the recovery efficiency of the low-permeability reservoir is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas development, and in particular to a method for obtaining a channeling control and oil displacement strategy for a low permeability reservoir based on pore network simulation. Background Art

[0002] Enhanced oil recovery (EOR) research has always been a research topic in the field of oil and gas field development. In recent years, gas injection EOR technology has developed rapidly, especially CO2 injection EOR technology. CO2 foam flooding technology has broad application prospects in oil field development. In the process of flooding in heterogeneous oil layers, foam can effectively block low permeability layers due to its characteristics of "blocking large but not small, blocking water but not oil", thereby adjusting the mobility difference of subsequent injected water in the low permeability layer and reducing the cross-flow of injected fluid in the low permeability layer.

[0003] For low permeability fault block reservoirs, the currently widely used commercial numerical simulation software can simulate the basic behavior of multiphase fluids in the reservoir, but has limited processing capabilities for considering the impact of pore structure on relative permeability characteristics and time-varying properties. Especially in low permeability tight reservoirs, due to the complexity of the pore network and the nonlinearity of relative permeability characteristics, traditional simulation methods are difficult to accurately predict the dynamic response of the reservoir and optimize development strategies.

[0004] Traditional water drive and gas drive technologies have limited effects in low permeability reservoirs. Existing technologies still have many deficiencies in optimizing injection strategies and evaluating technical effects: poor foam stability, complex optimization of injection and production parameters, high costs, and complex flow behavior of foam in porous media. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a method for obtaining a controlled oil recovery strategy for a low permeability oil reservoir based on pore network simulation. It first obtains the phase permeability parameters through simulation, and then optimizes the oil recovery parameters through simulation screening, thereby effectively improving the recovery rate of the low permeability oil reservoir.

[0006] In order to solve the above technical problems, the technical solution of the present invention is: a method for obtaining a low permeability reservoir channeling control and oil recovery strategy based on pore network simulation, the method comprising:

[0007] Step S1, obtaining the three-dimensional pore structure of the core sample, establishing a pore network model based on it, and performing multiphase fluid numerical simulation to determine the phase permeability parameters of the reservoir;

[0008] Step S2, obtaining the original data of the block reservoir, thereby constructing a three-dimensional geological model of the block reservoir, and inputting the relative permeability parameters into the three-dimensional geological model;

[0009] Step S3, considering the geological characteristics, fluid properties and production history of the oil reservoir, using the three-dimensional geological model to determine the well network injection and production method, the optimal location of the injection wells and production wells, and formulate an injection and production plan;

[0010] Step S4, through numerical simulation, based on sensitivity analysis, sensitive factors affecting the degree of recovery are selected from engineering factors and geological factors, wherein the engineering factors include injection method, CO2 foam injection rate, slug gas-liquid ratio, CO2 foam intervention timing, foam-assisted intervention timing and slug density, and the geological factors include reservoir density, vertical heterogeneity and fracture characteristics;

[0011] Step S5, optimizing the parameters of sensitive factors by numerical simulation with the recovery degree of each stage as the evaluation index;

[0012] Step S6, generating a channeling control and oil recovery strategy for the oil reservoir in the block based on the optimized sensitive factor parameters.

[0013] Further, step S1 specifically includes:

[0014] Step S11, based on the core sample of the actual oil reservoir, obtaining a pore structure image by CT scanning or microscope observation;

[0015] Step S12, processing the pore structure image to extract geometric features of pores and throats;

[0016] Step S13, constructing a pore network model including pore nodes and connecting throats according to the extracted geometric features, and setting parameters;

[0017] Step S14, performing CO2 foam migration simulation calculation based on the established pore network model to determine the relative permeability parameters of the reservoir.

[0018] Further, the setting parameters in step S13 specifically include:

[0019] Set the pore size based on the pore size distribution of the core sample;

[0020] Set the throat radius distribution according to the geometric characteristics of the throat;

[0021] The wettability parameters of pores and throats are set according to experimental data.

[0022] Further, step S2 specifically includes:

[0023] Step S21, using the coring analysis results, determine the lithology, porosity, permeability and mineral composition information of the formation, and collect the production history data of the oil field, including pressure, production and injection-production ratio;

[0024] Step S22, using the logging data, determine the main stratigraphic interfaces, divide the stratigraphic layers, identify the main faults, establish a fault model, and describe the location, scale, and nature of the faults;

[0025] Step S23, dividing the target block into a number of grid cells, performing model gridding, adjusting the grid type, size, and step size used in the model, and each cell contains attribute parameters of the formation;

[0026] Step S24, establishing a three-dimensional geological homogeneous and heterogeneous model of foam alternate injection in the target block, and designing a five-point well pattern mining mode;

[0027] Step S25, inputting the relative permeability parameters into the three-dimensional geological homogeneous and heterogeneous model.

[0028] Further, step S3 specifically includes:

[0029] Use geological models to analyze reservoir characteristics and determine the best well location layout;

[0030] Assess distances between wells to ensure adequate coverage and effective fluid exchange;

[0031] Select perforation sections based on reservoir physical properties and fluid characteristics to improve fluid flow efficiency and recovery rate;

[0032] Formulate reasonable mining plans and mining dates based on geological conditions, fluid properties and mining objectives.

[0033] Further, in step S4, the strategy for screening sensitive factors is:

[0034] The sensitivity of each factor was quantified by the coefficient of variation, and the factors with a coefficient of variation greater than the preset value were regarded as sensitive factors.

[0035] Furthermore, the degree of recovery in step S5 includes the oil production rate, recovery factor, production gas-oil ratio and foam system strength of the oil reservoir.

[0036] After adopting the above technical scheme, the present invention establishes a numerical model that considers the time-varying nature of phase permeability based on the simulation results of the pore network model, optimizes the foam segment injection strategy and evaluates the technical effect, and can consider the influence of saturation path and pressure on phase permeability, so that the development effect prediction and parameter design of the subsequent numerical model are more accurate; in view of the problems existing in the development of low-permeability oil reservoirs, an injection and production plan is formulated based on the three-dimensional geological model of the oil reservoir, and the optimization parameters are screened by simulation. The present invention realizes the seamless connection between the simulation of the time-varying nature of CO2 foam drive phase permeability and the numerical simulation of the oil reservoir, effectively improves the recovery rate of low-permeability oil reservoirs, solves the problems of poor foam stability, complex optimization of injection and production parameters, and thus improves the economic benefits and technical level of oil field development. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow chart of a method for obtaining a channeling control and oil displacement strategy for a low permeability reservoir based on pore network simulation of the present invention;

[0038] Figure 2 Pore, throat and coordination number diagram for core analysis of specific embodiments;

[0039] Figure 3 Flowchart for importing key parameters such as relative permeability into CMG STARS;

[0040] Figure 4 The recovery factor curves of different injection methods are shown in Figure 2.

[0041] Figure 5 The gas-oil ratio curve for different injection methods. DETAILED DESCRIPTION

[0042] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

[0043] like Figure 1 As shown, a method for obtaining a channeling control and oil displacement strategy for a low permeability reservoir based on pore network simulation includes:

[0044] Step S1, obtaining the three-dimensional pore structure of the core sample, establishing a pore network model based on it, and performing multiphase fluid numerical simulation to determine the phase permeability parameters of the reservoir; specifically:

[0045] Step S11, based on the core sample of the actual oil reservoir, the pore structure image is obtained by CT scanning or microscope observation, the scanning size is 2900×2900×2100, the analysis size is: 1007×1007×1007, and the resolution is 3μm;

[0046] Step S12, using PerGeos image processing software to process the pore structure image and extract the geometric features of the pores and throats;

[0047] Step S13, based on the extracted geometric features, construct a pore network model with pore nodes and connecting throats, and set parameters; wherein the setting parameters specifically include: setting the pore size according to the pore size distribution of the core sample; setting the throat radius distribution according to the geometric features of the throat; and setting the wettability parameters of the pores and throats according to experimental data. Table 1 shows the pore, throat and coordination number parameters of the core analysis of a specific embodiment; Figure 2 The pore, throat and coordination number diagram of the core analysis for a specific embodiment.

[0048] Table 1 Specific Example Core Analysis Pore, Throat and Coordination Number Parameters

[0049] Porosity parameters Analyze the results Throat parameters Analyze the results Total number of pores 45206 Number of throats 67461 Maximum pore radius 57.60μm Maximum throat radius 33.95μm Average pore radius 8.71μm Average throat radius 5.02μm Maximum pore volume <![CDATA[2.68×106μm 3 ]]> Maximum throat volume <![CDATA[4.06×105μm 3 ]]> Average pore volume <![CDATA[2.10×104μm 3 ]]> Average throat volume <![CDATA[8.63×103μm 3 ]]> Maximum coordination number 22 Maximum throat length 395μm Average coordination number 3.97 Average throat length 22.37μm

[0050] Step S14, performing CO2 foam migration simulation calculation based on the established pore network model to determine the relative permeability parameters of the reservoir.

[0051] Among them, the CO2 foam migration simulation calculation is carried out based on the established pore network model, including:

[0052] Using MATLAB programming, a platform that can interact with CMG was established to realize the reading of STARS operation results, correction of phase permeability curves, rewriting of STARS data files and re-operation process.

[0053] This step is based on the dynamic memory invasion percolation algorithm to form a CO2 foam control simulation method in the reservoir micro-nano pore network system that comprehensively considers the CO2 phase change, foam dynamic evolution and other mechanisms, and predicts key seepage characteristic parameters such as CO2 foam starting pressure and phase permeability curve:

[0054] (1) The reservoir has a large curvature of oil-water relative permeability, a narrow two-phase zone, a large residual oil saturation, a Sor of 0.3-0.35, a water phase relative permeability of 0.05-0.15, and a low movable oil saturation; the relative permeability calculation results in different directions are slightly different, but the overall trend is consistent. The direction with good connectivity has a lower residual oil saturation;

[0055] (2) The difficulty and low accuracy of CO2 foam relative permeability testing in low-permeability reservoirs were overcome, and representative oil / water and gas / water relative permeability curves of the target area were obtained.

[0056] Step S2, obtaining the original data of the block reservoir, thereby constructing a three-dimensional geological model of the block reservoir, and inputting the relative permeability parameters into the three-dimensional geological model; wherein the original data of the block reservoir includes well data, geological data and basic data of injection and production well development. The low permeability reservoir in the block meets the requirements of permeability less than 50mD and comprehensive water content not exceeding 40%. Figure 3 A specific embodiment is shown in which key parameters such as relative permeability are introduced into the CMG STARS flow chart.

[0057] Wherein, step S2 specifically includes:

[0058] Step S21, using the coring analysis results, determine the lithology, porosity, permeability and mineral composition information of the formation, and collect the production history data of the oil field, including pressure, production and injection-production ratio;

[0059] Step S22, using the logging data, determine the main stratigraphic interfaces, divide the stratigraphic layers, identify the main faults, establish a fault model, and describe the location, scale, and nature of the faults;

[0060] Step S23, dividing the target block into a number of grid cells, performing model gridding, adjusting the grid type, size, and step size used in the model, and each cell contains attribute parameters of the formation;

[0061] Step S24, establish a three-dimensional geological homogeneous and heterogeneous model of foam alternating injection in the target block, design a five-point well network (one injection and four production) mining mode, with a buried depth of 3006m and a corner injection and production well spacing of 425m.

[0062] Step S25, inputting the relative permeability parameters into the three-dimensional geological homogeneous and heterogeneous model.

[0063] In this step, the target block reservoir meets the following conditions:

[0064] The reservoir pressure is 30MPa, the reservoir temperature is 101℃, the porosity is 10%, the horizontal permeability is 15-50mD, the vertical permeability is 3-10mD, and the initial oil saturation is 0.7%.

[0065] Step S3, considering the geological characteristics, fluid properties and production history of the oil reservoir, using the three-dimensional geological model to determine the well network injection and production method, the optimal location of the injection wells and production wells, and formulate an injection and production plan; specifically including:

[0066] Use the geological model to analyze reservoir characteristics (such as permeability, porosity, oil and gas distribution, etc.) to determine the best well layout; evaluate the distance between wells to ensure adequate coverage and effective fluid exchange; select perforation sections based on reservoir physical properties and fluid properties to improve fluid flow efficiency and recovery rate; formulate a reasonable mining plan and mining date based on geological conditions, fluid properties and mining targets. According to the same system of injection and production wells in the target block and the injection method of different foam slugs, the physical and chemical parameters are determined in combination with the three-dimensional geological model of the target block established in step S2.

[0067] Among them, the physicochemical parameters include permeability, porosity, fluid saturation, oil layer thickness, viscosity, volume coefficient, oil, water, rock physical parameters, relative permeability curves, and parameters related to the rheological properties of the foam system on changes in permeability, resistance and residual resistance coefficient.

[0068] Step S4, through numerical simulation, based on sensitivity analysis, sensitive factors affecting the degree of recovery are selected from engineering factors and geological factors, wherein the engineering factors include injection method, CO2 foam injection rate, slug gas-liquid ratio, CO2 foam intervention timing, foam-assisted intervention timing and slug density, and the geological factors include reservoir density, vertical heterogeneity and fracture characteristics;

[0069] Among them, the strategy for screening sensitive factors is to quantify the sensitivity of each factor through the coefficient of variation, and take the factors with a coefficient of variation greater than the preset value as sensitive factors.

[0070] Among them, the injection methods include pure gas injection, water-gas injection, and CO2 foam injection. Figure 4 The recovery curves of different injection methods are shown. Figure 4 It can be seen that after depletion production and continuous gas injection production, water-gas cross-injection / CO2 foam control channeling are carried out respectively. Compared with water-gas cross-injection, CO2 foam has a higher peak oil production rate, a longer effective period, and a higher cumulative production increase. Figure 5 The gas-oil ratio curves of different injection methods are shown. Figure 5 It can be seen that the use of CO2 foam to control gas channeling can inhibit gas channeling and achieve the lowest gas-oil ratio.

[0071] CO2 foam injection rates include: 2.5m3 / day, 5m3 / day, 10m3 / day, 15m3 / day, 20m3 / day, 25m3 / day, 30m3 / day;

[0072] Slug gas-liquid ratio refers to the CO2 foam slug gas-liquid ratio, including: 3:1, 2:1, 1:1;

[0073] The timing of CO2 foam intervention includes: after injecting CO2 0.04PV, after injecting CO2 0.08PV, after injecting CO2 0.12PV, and after injecting CO2 0.16PV;

[0074] The timing of foam-assisted intervention includes: early, mid, and late stage addition of foam;

[0075] Slug density includes: foam slug density period 10 days, 20 days, 30 days;

[0076] The density of oil reservoirs includes reservoir geological permeability of 15mD and 50mD;

[0077] Longitudinal heterogeneity includes rhythmic characteristics and extremely poor permeability;

[0078] Crack characteristics include: crack direction and half crack length.

[0079] It should be noted that there is no significant statistical correlation between these parameters, as verified by the independence test, and their mutual relationship will not have a significant impact on the channeling control effect or recovery factor. Therefore, the impact of each parameter on the results can be studied separately in the subsequent sensitivity analysis.

[0080] Among them, Table 2 shows the extreme difference in vertical heterogeneity permeability; Table 3 shows reservoir characteristics and working system; Table 4 shows fracture parameters.

[0081] Table 2 Longitudinal heterogeneity permeability extremes

[0082]

[0083] Table 3 Reservoir characteristics and working system

[0084]

[0085]

[0086] Table 4 Crack parameters

[0087]

[0088] The design scheme of the target block was simulated and the comparison results showed that:

[0089] Injection method: The best injection method is CO2 foam injection;

[0090] Timing of CO2 foam intervention: Early foam injection can control mobility and reduce production gas-oil ratio, while late foam injection can optimize the injectivity of gas injection wells;

[0091] CO2 foam injection rate: CO2 foam injection rate affects foam rheology and generation capacity. As the injection rate increases, the degree of foam control and enhanced oil recovery increases first and then decreases. When the injection rate is low, the foam generation capacity is poor, gravity differentiation and viscosity fingering are obvious, and the foam enhanced oil recovery is small.

[0092] Foam slug gas-liquid ratio: Foam slug increases the gas-liquid ratio and improves the peak oil production rate; it reduces the gas-liquid ratio and prolongs the effective channeling control time;

[0093] Foam-assisted intervention timing: Foam-assisted intervention timing is to design foam plugs at both ends of the water-gas injection process, which can effectively balance the plugging depth and lateral sweep effect;

[0094] Slug design: The higher the slug density, the better the effect;

[0095] Reservoir density: Foam is effective in low-permeability and ultra-low-permeability reservoirs. However, as the permeability decreases, the injectivity will be inhibited, and it is necessary to optimize it by reducing the gas-oil ratio, adjusting the slug design, and delaying the injection timing;

[0096] Vertical heterogeneity: For positive rhythm reservoirs, foam is used to replace water-gas cross injection; for negative rhythm reservoirs, foam is used to connect water-gas cross injection to reduce the remaining oil in the middle of the oil layer;

[0097] Fracture characteristics: For favorable fracture distribution characteristics, CO2 foam can improve the utilization efficiency of fracture control areas; for unfavorable fracture conditions, CO2 foam can effectively alleviate the negative effects after the formation of advantageous channels.

[0098] Step S5, optimizing sensitive parameters by numerical simulation with the recovery degree of each stage as evaluation index;

[0099] The degree of recovery includes the oil production rate, recovery factor, gas-oil ratio and foam system strength of the reservoir. In the optimization process, iterative optimization is used to adjust the injection strategy according to the simulation results, real-time monitoring and dynamic adjustment of parameters, and the optimization model combined with historical data, which helps to improve the efficiency and recovery factor of foam injection.

[0100] Step S6, generating a channeling control and oil recovery strategy for the oil reservoir in the block based on the optimized sensitive parameters.

[0101] In this embodiment, the final generated channeling control and oil displacement strategy is:

[0102] Injection speed: A moderate injection speed helps to evenly distribute the foam and improve the recovery rate; too low a speed will lead to poor foam rupture and regeneration, and serious gas channeling; too high a speed will destroy the foam blocking ability and increase the risk of gas channeling. Therefore, in field experiments, it is best not to blindly increase the speed.

[0103] Intervention timing: Inject foam in advance when the degree of gas channeling is relatively mild. It is recommended to inject foam in areas with high local oil saturation to achieve stable control of gas channeling. Inject foam after gas channeling occurs. Foam has good injectability and can form effective plugging over a large area.

[0104] Slug design: The higher the slug density, the better the channeling control effect; increasing the gas-liquid ratio can increase the peak oil production rate, while reducing the gas-liquid ratio can extend the effective channeling control time. Therefore, it is recommended to dynamically adjust the slug according to the actual situation on site.

[0105] Permeability: Foam has a significant sealing effect in low-permeability and ultra-low-permeability reservoirs, but as the permeability decreases, the injectivity will be inhibited. Therefore, it is necessary to optimize the design by adjusting the gas-liquid ratio of the slug and delaying the timing of intervention.

[0106] Heterogeneity: For positive rhythm reservoirs, it is recommended to use foam to control channeling directly after continuous gas injection; for negative rhythm reservoirs, it is recommended to control channeling through water-gas cross injection after continuous gas injection, and then use foam connection to optimize the distribution of remaining oil and reduce the remaining oil in the middle of the oil layer. Add foam slugs (foam traction + foam tailing) at the beginning and end of the water-gas cross injection process to balance the plugging depth and lateral sweep efficiency.

[0107] Fracture conditions: Under favorable fracture conditions, CO2 foam can improve the utilization efficiency of fracture-controlled areas; under unfavorable fracture conditions, CO2 foam can effectively alleviate the negative effects after the formation of dominant channels. For areas with dominant channels such as fractures in reservoirs, a foam system with high stability and high plugging properties should be used for improvement; while for areas with heterogeneous matrices or high permeability bands, a foam system with good injectability and strong foaming / regeneration ability should be selected for optimization. In the future, it is possible to consider combining two different foam systems to solve the problem of CO2 gas channeling in low-permeability reservoirs.

[0108] It should be noted that in this embodiment, in actual research, we mainly analyzed the following two coefficients of variation:

[0109] ① Coefficient of variation of enhanced oil recovery: quantify the effect of foam flooding on enhanced oil recovery under different conditions.

[0110] ②Effective migration distance variation coefficient: evaluates the effective coverage and spread of foam in the reservoir.

[0111] In fact, the final generated control and flooding oil strategy is closely related to the screened sensitive parameters. In our research process, the most sensitive parameters for improving recovery and effective migration distance, such as gas-liquid ratio, injection rate, fracture conditions, etc., were screened out through the coefficient of variation. These sensitive parameters help us make reasonable optimization decisions when designing oil recovery strategies. For example, the injection rate and gas-liquid ratio directly affect the generation and migration of foam. Therefore, we clearly proposed the optimization range of injection rate and the adjustment strategy of gas-liquid ratio in the strategy to ensure that the foam can form an effective plug in the reservoir and improve the recovery rate. At the same time, considering the impact of permeability and heterogeneity on foam migration, we also proposed adjustment suggestions for different permeability conditions and reservoir types in the strategy.

[0112] Therefore, the final strategy seems to be more comprehensive, but in fact it is based on the analysis results of sensitive parameters and the actual situation of the reservoir to develop the best injection plan. Sensitivity analysis provides us with a theoretical guide, and the oil recovery strategy is the practical application of this theory.

[0113] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A method for obtaining a low permeability reservoir channeling control and oil recovery strategy based on pore network simulation, characterized in that: Methods include: Step S1, obtaining the three-dimensional pore structure of the core sample, establishing a pore network model based on it, and performing multiphase fluid numerical simulation to determine the phase permeability parameters of the reservoir; Step S2, obtaining the original data of the block reservoir, thereby constructing a three-dimensional geological model of the block reservoir, and importing the relative permeability parameters; Step S3, considering the geological characteristics, fluid properties and production history of the oil reservoir, using the three-dimensional geological model to determine the well network injection and production method, the optimal location of the injection wells and production wells, and formulate an injection and production plan; Step S4, using the three-dimensional geological model to perform numerical simulation, and based on sensitivity analysis, select sensitive factors affecting the degree of recovery from engineering factors and geological factors, wherein the engineering factors include injection method, CO2 foam injection rate, slug gas-liquid ratio, CO2 foam intervention timing, foam-assisted intervention timing, and slug density, and the geological factors include reservoir density, vertical heterogeneity, and fracture characteristics; Step S5, optimizing the parameters of sensitive factors by numerical simulation with the recovery degree of each stage as the evaluation index; Step S6, generating a channeling control and oil recovery strategy for the oil reservoir in the block based on the optimized sensitive factor parameters.

2. The method for obtaining a low permeability reservoir channeling control and oil recovery strategy based on pore network simulation according to claim 1, characterized in that: Step S1 specifically includes: Step S11, based on the core sample of the actual oil reservoir, obtaining a pore structure image by CT scanning or microscope observation; Step S12, processing the pore structure image to extract geometric features of pores and throats; Step S13, constructing a pore network model including pore nodes and connecting throats according to the extracted geometric features, and setting parameters; Step S14, performing CO2 foam migration simulation calculation based on the established pore network model to determine the relative permeability parameters of the reservoir.

3. The method for obtaining a low permeability reservoir channeling control and oil recovery strategy based on pore network simulation according to claim 2, characterized in that: The setting parameters in step S13 specifically include: Set the pore size based on the pore size distribution of the core sample; Set the throat radius distribution according to the geometric characteristics of the throat; The wettability parameters of pores and throats are set according to experimental data.

4. The method for obtaining a low permeability reservoir channeling control and oil recovery strategy based on pore network simulation according to claim 1, characterized in that: Step S2 specifically includes: Step S21, using the coring analysis results, determine the lithology, porosity, permeability and mineral composition information of the formation, and collect the production history data of the oil field, including pressure, production and injection-production ratio; Step S22, using the logging data, determine the main stratigraphic interfaces, divide the stratigraphic layers, identify the main faults, establish a fault model, and describe the location, scale, and nature of the faults; Step S23, dividing the target block into a number of grid cells, performing model gridding, adjusting the grid type, size, and step size used in the model, and each cell contains attribute parameters of the formation; Step S24, establishing a three-dimensional geological homogeneous and heterogeneous model of foam alternate injection in the target block, and designing a five-point well pattern mining mode; Step S25, inputting the relative permeability parameters into the three-dimensional geological homogeneous and heterogeneous model.

5. The method for obtaining a low permeability reservoir channeling control and oil recovery strategy based on pore network simulation according to claim 1, characterized in that: Step S3 specifically includes: Use geological models to analyze reservoir characteristics and determine the best well location layout; Assess distances between wells to ensure adequate coverage and effective fluid exchange; Select perforation sections based on reservoir physical properties and fluid characteristics to improve fluid flow efficiency and recovery rate; Formulate reasonable mining plans and mining dates based on geological conditions, fluid properties and mining objectives.

6. The method for obtaining a low permeability reservoir channeling control and oil recovery strategy based on pore network simulation according to claim 1, characterized in that: In step S4, the strategy for screening sensitive factors is: The sensitivity of each factor was quantified by the coefficient of variation, and the factors with a coefficient of variation greater than the preset value were regarded as sensitive factors.

7. The method for obtaining a low permeability reservoir channeling control and oil recovery strategy based on pore network simulation according to claim 1, characterized in that: The degree of recovery in step S5 includes the oil production rate, recovery factor, production gas-oil ratio and foam system strength of the oil reservoir.

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