Personalized injection parameter optimization design method based on emulsion polymer flooding

Abstract

The invention relates to a personalized injection parameter optimization design method based on emulsion polymer flooding. The method comprises the following steps: respectively carrying out core displacement experiments under different polymer dosages to obtain a first curve of each injection well; according to the deviation between the first curve and the second curve, the fitting deviation value of the injection well is determined; determining a heterogeneous value of the injection well according to the distance between each injection well and each adjacent injection well of the same type and the deviation between the recovery rates under each polymer dosage; performing weighted fusion on the first curve and the second curve of the injection well to obtain a target curve of the injection well, and determining the target polymer dosage of the injection well according to the target curve; according to the geological parameters and the pressure constraint parameters of each injection well, the injection speed of the injection well is determined, and the target polymer dosage is injected into the injection well based on the injection speed. According to the method, the polymer concentration and the polymer dosage can be matched for different injection wells, and the recovery ratio prediction precision is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oilfield development, and particularly relates to a method for optimizing design of individualized injection parameters based on emulsion polymer flooding. BACKGROUND

[0002] In the field of oilfield development, emulsion polymer flooding as an effective enhanced oil recovery technology, its core is to increase the viscosity of the water phase by injecting polymer solution, improve the oil-water mobility ratio, and thus expand the swept volume. However, the actual effect of this technology is highly dependent on the accurate design of injection parameters. When determining these key parameters, the existing methods often have significant limitations: or only rely on numerical simulation, the theoretical model of which is difficult to accurately depict the complex heterogeneous reservoir geological conditions underground, resulting in prediction deviation; or only through limited core experiments, the results of which are difficult to fully reflect the individual differences of different well areas and the optimal solution of economic benefits, resulting in low polymer utilization rate, unstable oil increase effect, and inability to achieve the optimal balance between cost and benefit, which restricts the large-scale and efficient application of this technology. SUMMARY

[0003] In order to solve the above technical problems, the purpose of the present application is to provide a method for optimizing design of individualized injection parameters based on emulsion polymer flooding, and the technical scheme adopted is as follows: In a first aspect, a method for optimizing design of individualized injection parameters based on emulsion polymer flooding is provided, and the method comprises: For each injection well in each type of injection well, a core displacement experiment is performed under different polymer dosages based on the polymer concentration corresponding to the type of injection well, and a first curve of each injection well is obtained; the polymer concentration corresponding to each type of injection well is obtained according to the classification results of each injection well in the target oilfield block; A fitting deviation value of each injection well is determined according to the deviation between the first curve and the second curve of each injection well; the second curve of each injection well is obtained by numerical simulation according to the geological parameters of the injection well; the first curve and the second curve are used to represent the corresponding relationship between the polymer dosage and the recovery rate; the fitting deviation value indicates the deviation degree between the core displacement experiment result and the numerical simulation result; A heterogeneity value of each injection well is determined according to the distance between each injection well and each adjacent injection well of the same type, and the deviation between the recovery rate of the injection well under each polymer dosage and the recovery rate of each adjacent injection well of the same type under the polymer dosage; the recovery rate of each injection well under each polymer dosage is obtained according to the first curve of the injection well; the heterogeneity value indicates the dispersion degree of the recovery rate of each injection well and the adjacent injection well of the same type under the same polymer dosage; The first and second curves of each injection well are weighted and fused according to the correction weight of each injection well to obtain the target curve of the injection well, and the target polymer dosage of the injection well is determined according to the target curve; the correction weight of each injection well is determined according to the fitting deviation value and heterogeneity value of the injection well. Based on the geological parameters and pressure constraint parameters of each injection well, the injection rate of the injection well is determined, and the target amount of polymer is injected into the injection well based on the injection rate.

[0004] Optionally, for multiple injection wells within each type of injection well, before obtaining the first curve for each injection well by conducting core displacement experiments at different polymer concentrations based on the corresponding polymer concentration for that type of injection well, the process further includes: The effective thickness, permeability, and K80 permeability of each injection well were measured. Based on the effective thickness, permeability, and K80 permeability of each injection well, multiple injection wells in the target oilfield block are classified to obtain the classification results; Based on the classification results, the corresponding polymer concentration for each type of injection well is determined.

[0005] Optionally, for each injection well within each type of injection well, based on the polymer concentration corresponding to that type of injection well, core displacement experiments are conducted at different polymer dosages to obtain the first curve for each injection well, including: Obtain a core sample from each injection well in each type of injection well, and place the core sample in a vacuum dryer for vacuuming. Simulated formation water was injected into the vacuum dryer until it submerged the core, and then atmospheric pressure was restored to allow the simulated formation water to seep into the core pores, resulting in a water-saturated core. The pore volume and porosity of the core were calculated based on the difference between the weight of the water-saturated core and the weight of the dry core. A water-saturated core was placed in a flow test apparatus, and formation water was injected into the core at a constant flow rate. After the flow stabilized, the absolute permeability of the core was calculated based on Darcy's law according to the inlet pressure, outlet pressure, and stable flow rate at the outlet of the flow test apparatus. The absolute permeability is used to characterize the seepage capacity of the core. The formation water in the core was displaced by simulated oil until the water content of the produced liquid at the outlet of the flow test device was less than a preset first threshold, thus obtaining an oil-saturated core. The original oil saturation of the core was determined based on the difference between the weight of the oil-saturated core and the weight of the core before the injection of simulated oil. Simulated formation water was used to waterflood oil-saturated core samples until the oil production at the outlet of the flow experiment device was less than a preset second threshold. The waterflood recovery rate of the core sample was calculated based on the ratio of the amount of crude oil produced during the waterflooding process to the oil content of the oil-saturated core sample. The waterflood recovery rate is used to characterize the amount of oil remaining after waterflooding in a single oil recovery process. Based on the injection well category to which each injection well belongs, the corresponding polymer concentration for that injection well is determined. After the water drive is completed, the simulated formation water is switched to a polymer solution with the corresponding polymer concentration. The amount of polymer in the solution is gradually increased and injected into the core. The total amount of crude oil produced under each polymer concentration is recorded. The first recovery rate of the injection well at each polymer dosage is calculated based on the total crude oil produced and the original oil content of the core. The original oil content of the core indicates the oil content in an oil-saturated core. With polymer dosage as the x-axis and the first recovery rate of each injection well at the corresponding polymer dosage as the y-axis, the first curve of the injection well is determined; when the polymer dosage is zero, the first recovery rate is equal to the water drive recovery rate.

[0006] Optionally, the second curve is obtained according to the following steps: Numerical simulation models are constructed based on the pore volume, porosity, absolute permeability, and initial oil saturation of each injection well. In the numerical simulation model, the oil displacement process under different polymer dosages is simulated sequentially according to the polymer concentration corresponding to the injection well, and the amount of crude oil produced under each polymer dosage is recorded. The corresponding secondary recovery rate is calculated based on the preset original oil content in the numerical simulation model. The polymer concentration corresponding to each injection well is determined according to the polymer concentration corresponding to the injection well category to which the injection well belongs. With polymer dosage as the x-axis and the secondary recovery rate of each injection well at the corresponding polymer dosage as the y-axis, the second curve of the injection well is determined.

[0007] Optionally, the fitting deviation value of each injection well is determined based on the deviation between the first and second curves, including: Within a preset range of polymer dosage, the absolute difference between the recovery rates of the first and second curves of each injection well under the same polymer dosage is calculated, and the absolute difference is accumulated within the preset range to obtain the fitting deviation value of the injection well.

[0008] Optionally, the heterogeneity value of the injection well is determined based on the distance between each injection well and each adjacent similar injection well, and the deviation between the recovery rate of the injection well at each polymer dosage and the recovery rate of each adjacent similar injection well at that polymer dosage, including: The distance weight between each injection well and each adjacent injection well of the same type is determined based on the spatial distance between each injection well and each adjacent injection well of the same type, and the depth difference between the injection well and the injection well of the same type. Calculate the difference in first recovery rate between each injection well and each adjacent injection well of the same type at each polymer dosage, and obtain the difference in first recovery rate between the injection well and the injection well of the same type at that polymer dosage; The sum of the first recovery rate differences between each injection well and each adjacent injection well of the same type at multiple polymer dosages is calculated to obtain the sum of the first recovery rates between the injection well and the injection well of the same type. Based on the normalized spatial weights between each injection well and each adjacent injection well of the same type, and the first recovery rate and value between the injection well and the injection well of the same type, the heterogeneity index between the injection well and the injection well of the same type is determined; the heterogeneity index indicates the degree of dispersion of the recovery rate between each injection well and the injection well of the same type at the same polymer dosage. The heterogeneity value of an injection well is obtained by summing the heterogeneity indices between each injection well and its neighboring similar injection wells.

[0009] Optionally, before weighted fusion of the first and second curves of the injection well according to the correction weight of each injection well to obtain the target curve of the injection well, the method further includes: The heterogeneity value and fitting deviation value of each injection well are normalized, and the correction weight of the injection well is determined based on the ratio of the normalized heterogeneity value to the fitting deviation value.

[0010] Optionally, the first and second curves of the injection well are weighted and fused according to the correction weight of each injection well to obtain the target curve of the injection well, including: Under the same polymer dosage, the target recovery rate of the target curve under the polymer dosage is obtained by multiplying the first recovery rate of the first curve by the correction weight and the second recovery rate of the second curve by the supplementary weight; the supplementary weight indicates the difference between the preset value and the correction weight. Calculate the target recovery rate corresponding to multiple polymer dosages, and use the polymer dosage as the x-axis and the target recovery rate of each injection well at the corresponding polymer dosage as the y-axis to determine the target curve of the injection well.

[0011] Optionally, the target polymer dosage for the injection well is determined based on the target curve, including: The polymer dosage corresponding to the maximum target recovery rate in the target curve of each injection well is determined as the target polymer dosage for that injection well.

[0012] Optionally, the injection rate of each injection well is determined based on its geological parameters and pressure constraint parameters, including: The injection rate of each injection well is determined based on the product of the lowest apparent water absorption index of the oil layer and the highest injection pressure at the wellhead, porosity, and injection-production well spacing. Among these parameters, the geological parameters include porosity, injection-production well spacing, and the lowest apparent water absorption index of the oil layer, while the pressure constraint parameter includes the highest injection pressure at the wellhead.

[0013] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of this application.

[0014] This application offers the following advantages: By classifying injection wells and matching differentiated polymer concentrations to each type, the swept volume of the displacement system is expanded, avoiding inrush into high-permeability layers and under-injection into low-permeability layers. Regarding dosage determination, it overcomes the limitations of traditional reliance solely on numerical simulations. By integrating the actual oil displacement response obtained from core displacement experiments with theoretical predictions from numerical simulations, and introducing a quantitative geological heterogeneity assessment mechanism, both are dynamically weighted and corrected to generate a polymer dosage-recovery curve that more closely reflects actual subsurface conditions. Based on this, the economically optimal target polymer dosage is determined, significantly improving polymer utilization efficiency and reducing development costs. Regarding injection rate, the injection rate is optimized using the geological parameters and pressure constraint parameters of the injection wells, reducing polymer shear degradation. This forms a complete technical closed loop from concentration matching and dosage optimization to rate control, achieving a balance between oil displacement effect, economic benefits, and engineering safety. It can accurately identify and quantify the impact of reservoir heterogeneity on polymer flooding at the single-well scale, thereby matching the optimal polymer concentration and dosage for different injection wells, avoiding blind injection, improving the accuracy of recovery rate prediction and field implementation effect, and effectively solving the problem of scheme mismatch and resource waste caused by ignoring the actual geological complexity. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a personalized injection parameter optimization design method based on emulsion polymer drive in one embodiment; Figure 2 This is a schematic diagram of a personalized injection parameter optimization design system based on emulsion polymer drive in one embodiment. Figure 3 This is a schematic diagram of the structure of an electronic device in one embodiment. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a personalized injection parameter optimization design method based on emulsion polymer drive proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] The following description, in conjunction with the accompanying drawings, details a specific scheme for a personalized injection parameter optimization design method based on emulsion polymer drive provided in this application. For example... Figure 1 As shown, the method includes: S11. For each injection well in each type of injection well, based on the polymer concentration corresponding to that type of injection well, core displacement experiments are conducted at different polymer dosages to obtain the first curve for each injection well.

[0020] The polymer concentration for each type of injection well is determined based on the classification results of each injection well within the target oilfield block.

[0021] The first curve is used to characterize the relationship between polymer dosage and recovery rate.

[0022] The polymer can be an emulsion polymer, which is an oil-in-water type, wherein the polymer is dissolved in oil droplets and the external phase is water. The polymer is preferably partially hydrolyzed polyacrylamide or a hydrophobic associative polymer. After the emulsion is injected into the formation, it is diluted or demulsified by shearing to release the polymer, thereby increasing the viscosity of the displacement fluid and improving the mobility ratio.

[0023] In one embodiment, before obtaining the first curve for each injection well (multiple injection wells in each type of injection well) by conducting core displacement experiments at different polymer concentrations based on the polymer concentration corresponding to that type of injection well, the method further includes: The effective thickness, permeability, and K80 permeability of each injection well were measured. Based on the effective thickness, permeability, and K80 permeability of each injection well, multiple injection wells in the target oilfield block are classified to obtain the classification results; Based on the classification results, the corresponding polymer concentration for each type of injection well is determined.

[0024] This application's embodiments classify multiple injection wells within a target oilfield block based on four key indicators: effective thickness of the injection well, permeability, permeability corresponding to K80, and degree of polymer flooding control.

[0025] The effective thickness of an injection well refers to the total net thickness of the oil-bearing layers within the perforated section of the well that possess industrial oil-producing capacity and participate in the injection-production flow. Its determination primarily relies on well logging interpretation and comprehensive geological research: First, conventional well logging curves (such as deep lateral resistivity, sonic transit time, spontaneous potential, and density logging) are used to identify oil-bearing layers, and effective reservoirs are delineated by setting lower limits for parameters such as porosity, oil saturation, and permeability. Second, the well logging interpretation model is calibrated and corrected using core analysis data to ensure interpretation accuracy. Finally, under geological stratigraphic comparison and control, the thickness of all effective oil-bearing layers in a single well is cumulatively calculated as the effective thickness of the injection well.

[0026] Permeability is a key parameter characterizing the ability of rocks to allow fluids to pass through, and its acquisition requires a combination of direct measurement and indirect interpretation. The most direct method is core testing: core samples obtained from the corresponding formation of the injection well are used in the laboratory to measure their absolute permeability using gas or liquid (such as simulated formation water) through steady-state or unsteady-state methods. This data has the highest accuracy and is used for calibration. Continuous acquisition at actual well points relies on well logging interpretation: by establishing the correlation between core analysis permeability and well logging responses (such as acoustic waves, density, neutrons, nuclear magnetic resonance, etc.), a regional interpretation model is formed, allowing for continuous calculation and evaluation of permeability throughout the well section. The permeability ultimately used for classification usually refers to the average permeability of the target formation or the permeability of the main flow unit.

[0027] K80 permeability is a key indicator characterizing the heterogeneity of reservoir permeability. It refers to the permeability value corresponding to a cumulative probability of 80% on the cumulative permeability frequency distribution curve. Its acquisition is based on a series of single-well permeability data: First, through core analysis or well logging interpretation methods, a series of continuous or discrete permeability data within the target formation are obtained; then, these permeability values ​​are sorted from high to low, and their cumulative frequency distribution is calculated; finally, the point with a cumulative probability of 80% is found on the cumulative frequency distribution curve, and the permeability value corresponding to this point is K80. This parameter reflects the capacity of the relatively low-permeability section that constitutes the majority of the formation; the lower the value, the stronger the heterogeneity, and the more prominent the phenomenon of high-permeability channels controlling fluid flow.

[0028] Polymer flooding control level refers to the percentage of crude oil reserves effectively swept and displaced by the polymer solution injected into injection wells during polymer flooding development. Its acquisition is a dynamic process based on reservoir engineering analysis, primarily achieved through comprehensive evaluation of connectivity analysis and dynamic monitoring data of the injection-production well network. Specific methods include: determining the connectivity of injection-production well groups using inter-well stratigraphic correlation and structural studies; analyzing inter-well conductivity using well test interpretation (such as injection pressure drop tests); applying numerical simulation techniques for historical fitting to invert connectivity and sweep range; and comprehensively analyzing the injection profiles of injection wells, the production profiles of production wells, and chemical tracer monitoring results to quantitatively assess the actual reservoir sweep ratio of polymer flooding. This parameter directly reflects the control efficiency of the injection wells over the surrounding reservoirs.

[0029] This established a classification standard for well groups and concentration quantification, dividing injection wells into three well groups, resulting in the classification results of the injection wells. See Table 1 for details. Table 1. Well Group Classification Criteria for Injection Wells

[0030] This application focuses on 28 injection wells in a test area in Daqing. Based on well group classification standards and design principles, and considering the historical parameters of the 28 injection wells and the aforementioned classification standards, the classification results of the injection wells in the actual test area were obtained, and the polymer concentration corresponding to each type of injection well was determined. There are 4 Class I wells with a designed polymer concentration of 1200 mg / L; 13 Class II wells with a polymer concentration of 800-1000 mg / L, with an average polymer concentration of 907 mg / L; and 11 Class III wells with a polymer concentration of 500-700 mg / L, with an average polymer concentration of 572 mg / L. The classification results of the 28 injection wells in the test area are shown in Table 2.

[0031] Table 2 Design table of polymer concentration for single wells in the test area

[0032] To determine the amount of emulsion polymer used, the embodiments of this application mainly use core flooding experiments and software numerical simulations, combined with the distribution of injection wells and heterogeneous conditions, to determine the amount of emulsion polymer used.

[0033] Therefore, in one embodiment, for each injection well in each type of injection well, based on the polymer concentration corresponding to that type of injection well, core displacement experiments are conducted at different polymer dosages to obtain a first curve for each injection well, including: Obtain a core sample from each injection well in each type of injection well, and place the core sample in a vacuum dryer for vacuuming. Simulated formation water was injected into the vacuum dryer until it submerged the core, and then atmospheric pressure was restored to allow the simulated formation water to seep into the core pores, resulting in a water-saturated core. The pore volume and porosity of the core were calculated based on the difference between the weight of the water-saturated core and the weight of the dry core. A water-saturated core was placed in a flow test apparatus, and formation water was injected into the core at a constant flow rate. After the flow stabilized, the absolute permeability of the core was calculated based on Darcy's law according to the inlet pressure, outlet pressure, and stable flow rate at the outlet of the flow test apparatus. The absolute permeability is used to characterize the seepage capacity of the core. The formation water in the core was displaced by simulated oil until the water content of the produced liquid at the outlet of the flow test device was less than a preset first threshold, thus obtaining an oil-saturated core. The original oil saturation of the core was determined based on the difference between the weight of the oil-saturated core and the weight of the core before the injection of simulated oil. Simulated formation water was used to waterflood oil-saturated core samples until the oil production at the outlet of the flow experiment device was less than a preset second threshold. The waterflood recovery rate of the core sample was calculated based on the ratio of the amount of crude oil produced during the waterflooding process to the oil content of the oil-saturated core sample. The waterflood recovery rate is used to characterize the amount of oil remaining after waterflooding in a single oil recovery process. Based on the injection well category to which each injection well belongs, the corresponding polymer concentration for that injection well is determined. After the water drive is completed, the simulated formation water is switched to a polymer solution with the corresponding polymer concentration. The amount of polymer in the solution is gradually increased and injected into the core. The total amount of crude oil produced under each polymer concentration is recorded. The first recovery rate of the injection well at each polymer dosage is calculated based on the total crude oil produced and the original oil content of the core. The original oil content of the core indicates the oil content in an oil-saturated core. With polymer dosage as the x-axis and the first recovery rate of each injection well at the corresponding polymer dosage as the y-axis, the first curve of the injection well is determined; when the polymer dosage is zero, the first recovery rate is equal to the water drive recovery rate.

[0034] The first threshold can be 20% to 30%, preferably 25%. The second threshold can be 1% to 5%, preferably 2%.

[0035] For multiple injection wells in the test area, core samples were obtained from the corresponding oil layers of each injection well. Core displacement experiments were conducted to evaluate the effects of different polymer dosages. The specific procedure for core displacement experiments using emulsion polymers is as follows: core vacuuming – water saturation – water permeability testing – oil saturation – water-driven oil displacement – ​​emulsion polymer oil displacement. Specifically: Core vacuuming: For a single core sample injected downhole, the cleaned and dried core is placed in a holder and the whole thing is placed in a vacuum dryer. The vacuum pump is started and vacuuming is carried out for 4 to 8 hours to remove the gas from the pores of the core sample, creating conditions for complete filling of the subsequent saturated liquid.

[0036] Saturated Water: Under vacuum conditions, pre-prepared simulated formation water is introduced into the vacuum dryer through pipelines to submerge the core. Then, atmospheric pressure is restored, and the simulated formation water is forced into the core pores under hydrostatic pressure, resulting in a water-saturated core. The water-saturated core is weighed, and the pore volume and porosity of the core sample are obtained by comparing its weight to that of a dry core. Specifically, the pore volume is obtained by dividing the difference between the mass of the core in the saturated state and the dry core mass by the density of the simulated formation water. The porosity is determined by the ratio of the pore volume to the total core volume, where the total core volume is calculated based on its geometric dimensions.

[0037] Water permeability test: A core sample saturated with water is loaded into the holder of a core flow test apparatus. Formation water is injected into the core sample at a constant flow rate using a horizontal flow pump. Once the flow stabilizes, the inlet pressure, outlet pressure, and stable flow rate at the outlet are accurately recorded. Based on this, the absolute permeability (water phase permeability) of the core sample is calculated using Darcy's formula.

[0038] Saturated Oil: Simulated oil is used to displace water from the core sample at a slow flow rate (0.01~0.05 ml / min). This displacement continues until the liquid produced at the outlet is almost entirely oil, meaning the water content of the produced liquid at the outlet is less than a preset first threshold, and the water production remains stable and at an extremely low level, resulting in an oil-saturated core. Based on the weight of the core sample after oil saturation, combined with the weight of the core sample after water saturation (the core weight before simulated oil injection), the difference between the core weight in the oil-saturated state and the core weight in the water-saturated state is calculated to obtain the weight change caused by the injection of simulated oil and the displacement of some formation water. Taking into account the density difference between simulated crude oil and simulated formation water, this weight change is converted into the volume of simulated oil contained in the core. The densities of simulated oil and simulated formation water are obtained. Based on the increase in core weight during oil-water displacement, and combined with the density difference between simulated oil and simulated formation water, the volume of simulated oil injected and retained in the core to achieve this weight change is calculated. By combining the core pore volume known from the saturated water experiment, the proportion of simulated crude oil volume to pore volume is calculated, which is the original oil saturation of the core. The original oil saturation can be used to simulate the oil content of the reservoir in its initial state.

[0039] Waterflooding for oil recovery: Based on the core sample after saturated oil measurement, simulated formation water is continuously used for displacement at a set flow rate (usually consistent with the actual water injection rate in the oilfield) until a stable oil production is measured at the outlet, i.e., until the oil production at the outlet of the flow experiment device is less than a preset second threshold. The waterflooding recovery rate of the core sample is calculated based on the ratio of the crude oil produced during the waterflooding process to the oil content of the oil-saturated core. Specifically, during the waterflooding experiment, all fluid produced at the outlet of the flow experiment device is continuously collected and separated by oil-water separation. The volume or mass of crude oil in each segment is measured and summed to obtain the amount of crude oil produced during waterflooding. Simultaneously, based on the oil saturation state of the core before waterflooding begins, the oil content of the oil-saturated core is determined. The oil content of the oil-saturated core is the total amount of simulated oil contained in the core, which is obtained by combining saturated oil experiments with fluid density and pore volume calculations. The ratio of the amount of crude oil produced during waterflooding to the oil content of the oil-saturated core is calculated, and the result is the waterflooding recovery rate of the core sample. The waterflooding recovery rate is used to simulate the primary oil recovery process and measure the amount of remaining oil after waterflooding.

[0040] Emulsion polymer flooding: After waterflooding ends, continuing waterflooding will not recover the remaining oil. The waterflooding fluid is switched to an emulsion polymer solution. Based on the well category of each injection well, the corresponding polymer concentration for that well is determined. The polymer dosage of the emulsion polymer solution is gradually increased, and the total amount of crude oil recovered at each polymer dosage is recorded. The first recovery rate (FER) of the injection well at that polymer dosage is calculated based on the ratio of the total crude oil production to the original oil content in the core. Specifically, the FER is calculated as the ratio of the total crude oil production to the original oil content in the core. Based on scatter plots of different polymer dosages and corresponding FERs, the data points in the scatter plots are fitted using mathematical fitting methods (such as polynomial regression or spline interpolation) to generate a continuous and integrable function curve within the dosage range, thus obtaining the first curve for the corresponding polymer dosage in the physical flooding experiment.

[0041] S12. Determine the fitting deviation value of each injection well based on the deviation between the first curve and the second curve.

[0042] The first and second curves are used to characterize the relationship between polymer dosage and recovery rate.

[0043] The fitting deviation value indicates the degree of deviation between the experimental results of core displacement and the numerical simulation results.

[0044] In one embodiment, the second curve is obtained according to the following steps: Numerical simulation models are constructed based on the pore volume, porosity, absolute permeability, and initial oil saturation of each injection well. In the numerical simulation model, the oil displacement process under different polymer dosages is simulated sequentially according to the polymer concentration corresponding to the injection well, and the amount of crude oil produced under each polymer dosage is recorded. The corresponding secondary recovery rate is calculated based on the preset original oil content in the numerical simulation model. The polymer concentration corresponding to each injection well is determined according to the polymer concentration corresponding to the injection well category to which the injection well belongs. With polymer dosage as the x-axis and the secondary recovery rate of each injection well at the corresponding polymer dosage as the y-axis, the second curve of the injection well is determined.

[0045] First, a numerical simulation model was constructed: based on the measurement data of shale core samples corresponding to each injection well, a numerical simulation model was established in ChemEOR software, including a reservoir geological sub-model and a fluid sub-model. The inputs to the geological sub-model included the porosity, absolute permeability, effective thickness, and original oil saturation of the injection well, and the pore volume was calculated by combining geometric parameters. The fluid sub-model was configured based on the prior physicochemical properties of the emulsion polymer, including the concentration-viscosity relationship, adsorption isotherm, shear thinning coefficient, etc., wherein the physicochemical properties of the emulsion polymer are known parameters after the polymer type is determined.

[0046] The Meter equation or the Flory-Huggins equation is used to describe the variation of polymer solution viscosity with concentration, salinity and shear rate. At the same time, Langmuir-type adsorption isotherms are introduced to simulate the adsorption loss of polymer on the pore surface of shale, so as to characterize its retention effect in low-permeability media.

[0047] Next, the multiphase flow-mass transfer coupling equations are solved: based on Darcy's law and the principle of component mass conservation, the ChemEOR software is used to solve the transport control equations of the aqueous phase, oil phase and polymer components, and to dynamically simulate the evolution of the pressure field, saturation field and polymer concentration field during the displacement process, and obtain the fitting parameters.

[0048] Finally, a polymer dosage-recovery curve (second curve) is generated: Based on the polymer concentration corresponding to the injection well category, different polymer dosages (e.g., 0.2PV, 0.4PV…1.2PV) are sequentially set in the model, and displacement simulation is run; for each polymer dosage, the cumulative amount of crude oil produced is recorded, and the corresponding secondary recovery rate is calculated in conjunction with the preset original oil content in the model (determined by pore volume and original oil saturation). The polymer dosage-recovery curve for this injection well is plotted with polymer dosage as the x-axis and secondary recovery rate as the y-axis, serving as its second curve.

[0049] The oil recovery curve obtained from the core displacement experiment described above reflects the oil recovery curve of the corresponding oil layer in the injection well under ideal conditions. However, since the core displacement experiment is conducted at ambient temperature and pressure, it cannot accurately reflect the impact of high temperature and pressure downhole on the oil recovery. Furthermore, the ChemEOR numerical simulation mainly relies on a priori polymer physical flooding model and cannot reflect the impact of the actual downhole environment and heterogeneity differences on the oil recovery. Therefore, both methods result in some distortion in the simulated relationship between polymer dosage and oil recovery. It is necessary to combine the oil recovery curve relationship of both methods with the geological conditions of the oil reservoir to achieve an accurate evaluation of the actual oil recovery curve between polymer dosage and oil recovery.

[0050] Under normal circumstances, reservoir development is often correlated. Therefore, it is possible to correct for polymer recovery rates based on the correlation between injection wells in well groups. Thus, it is necessary to first obtain geological condition data for the well groups. For each injection well in the current test area, the geological conditions of each injection well are obtained. Specifically, this includes obtaining the corresponding spatial location (latitude and longitude coordinates) based on the injection well number, and the depth value of the corresponding oil layer under each injection well. The obtained spatial location and oil layer depth value are used as the geological condition data for this scheme.

[0051] Furthermore, for core samples corresponding to the oil layer of each injection well, based on the above steps, a first curve from the core-driven oil displacement experiment and two second curves from numerical simulations of polymer dosage and polymer recovery rate were obtained. It should be noted that the oil displacement experiment, since the first recovery rate was obtained through experiments with different polymer dosages, yielded a scatter plot. The data points in the scatter plot were fitted using mathematical fitting methods (such as polynomial regression or spline interpolation) to generate a continuous and integrable function curve within the dosage range, i.e., the first curve. The second curve obtained from the numerical simulations is based on the polymer flooding model, thus yielding a continuous oil recovery curve.

[0052] For oil displacement experiments using core samples from a single injection well, the relationship between polymer dosage and recovery rate can be plotted under normal temperature and pressure. In this case, the influence of the high-temperature and high-pressure environment downhole on the physical properties of the polymer is not considered, and it can be approximated as the oil displacement and recovery rate under normal temperature and pressure. Numerical simulations, based on fixed prior polymer flooding models in the software, depict the relationship between polymer dosage and recovery rate. While this is closer to reality, the simulation is based solely on the static parameters of a single well, ignoring the impact of downhole heterogeneity on the simulation results. Therefore, the simulation results may deviate from reality.

[0053] For the two sets of oil enhancement relationships obtained from single-well downhole displacement experiments and numerical simulations, the first curve is obtained by curve fitting of the scatter plot obtained from the core displacement experiment, and the second curve is obtained by numerical simulation. If the absolute value integral of the longitudinal deviation of the two curves within the preset range of polymer dosage is larger, it indicates that the downhole heterogeneity has a greater impact on the simulation results; conversely, the smaller the difference between the two, the smaller the interference of the downhole environment on the simulation of recovery rate.

[0054] In one embodiment, determining the fitting deviation value of an injection well based on the deviation between the first curve and the second curve for each injection well includes: Within a preset range of polymer dosage, the absolute difference between the recovery rates of the first and second curves of each injection well under the same polymer dosage is calculated, and the absolute difference is accumulated within the preset range to obtain the fitting deviation value of the injection well.

[0055] Fit deviation value The calculation formula is: In the formula, A represents the fitting deviation value for each injection well, in units of... m is the amount of polymer used. This is the starting point for polymer dosage, in units of , This is the end point for polymer dosage, in units of , is usually a set value, , That is, the preset range is (400, 2000), because below the lower limit, it will not be possible to drive oil or use physical materials, and above the upper limit, the economic value is not high. For the first curve, The purpose of taking the absolute value of the two curves here is to avoid the interference of the numerical calculation caused by their intersection and the reduction of the integral area.

[0056] To reduce the influence of unit values ​​on subsequent analysis, the fitting deviation value obtained from a single injection well is normalized by the maximum-minimum normalization method within the current category of that injection well. For example, if the current injection well belongs to a category I well, the normalization is achieved by using the maximum-minimum fitting deviation value among all injection wells in that category. Similarly, the normalization is performed on all other injection wells to obtain the corresponding normalized fitting deviation value, denoted as . .

[0057] Calculation principle: In the analysis of fitting deviation, the focus is on the relationship between the oil enhancement curves obtained by two methods, namely core flooding experiments and numerical simulations. The fitting deviation between the polymer dosage and recovery rate obtained by the two methods is evaluated by integrating the area. If the fitting deviation value of a single injection well is larger, it indicates that there are more downhole geological influences and the degree of interference in the current injection well.

[0058] S13. Determine the heterogeneity value of the injection well based on the distance between each injection well and each adjacent injection well of the same type, and the deviation between the recovery rate of the injection well at each polymer dosage and the recovery rate of each adjacent injection well of the same type at the same polymer dosage.

[0059] The recovery rate of each injection well at each polymer dosage is obtained from the first curve of that injection well.

[0060] The heterogeneity value indicates the degree of dispersion in the recovery rate of each injection well compared to adjacent similar injection wells with the same polymer dosage.

[0061] The development of the geology downhole is crucial for reservoir flooding. Numerical simulations only use static geological data from the corresponding downhole injection well, such as porosity, permeability, and saturation, to adjust polymer dosage and recovery rate based on existing empirical models. However, these values ​​only indicate the statistical data of the current downhole shale structure and do not reflect the development and connection of pore throats at the microscopic level. Although some shale formations may have similar values ​​for porosity, permeability, and saturation, their internal pore throat structures may differ due to heterogeneity. The microstructure of shale is key to polymer flooding.

[0062] Oil displacement experiments on core samples are used to characterize the pore-throat structure within shale. The pore-throat structure of shale influences fluid storage, flow, adsorption, and degradation. Therefore, shale samples from different well groups may have similar or identical pore-throat structures, resulting in similar oil displacement curves in core oil displacement experiments. However, shale samples with similar porosity, permeability, and saturation values ​​but different internal pore-throat structures will show larger deviations in their oil displacement curves.

[0063] First, the formation changes have a certain continuity during the development of the oil reservoir. Therefore, for a single injection well, if the geological parameters of the current injection well are closer to those of similar injection wells in the neighborhood, the downhole shale development of the two wells may be more similar. This gives us the distance weight of the neighboring injection wells to the current injection well. It should be noted that the neighborhood refers to a circular neighborhood with a radius of 100m for the current injection well, or a circular neighborhood with a radius of 10 times the average well spacing in the current test area.

[0064] In one embodiment, the heterogeneity value of an injection well is determined based on the distance between each injection well and each adjacent similar injection well, and the deviation between the recovery rate of the injection well at each polymer dosage and the recovery rate of each adjacent similar injection well at that polymer dosage, including: The distance weight between each injection well and each adjacent injection well of the same type is determined based on the spatial distance between each injection well and each adjacent injection well of the same type, and the depth difference between the injection well and the injection well of the same type. Calculate the difference in first recovery rate between each injection well and each adjacent injection well of the same type at each polymer dosage, and obtain the difference in first recovery rate between the injection well and the injection well of the same type at that polymer dosage; The sum of the first recovery rate differences between each injection well and each adjacent injection well of the same type at multiple polymer dosages is calculated to obtain the sum of the first recovery rates between the injection well and the injection well of the same type. Based on the normalized spatial weights between each injection well and each adjacent injection well of the same type, and the first recovery rate and value between the injection well and the injection well of the same type, the heterogeneity index between the injection well and the injection well of the same type is determined; the heterogeneity index indicates the degree of dispersion of the recovery rate between each injection well and the injection well of the same type at the same polymer dosage. The heterogeneity value of an injection well is obtained by summing the heterogeneity indices between each injection well and its neighboring similar injection wells.

[0065] The formula for calculating distance weight is: ,in This represents the distance weight of the current injection well relative to the k-th injection well of the same type within its neighborhood. This represents the normalized spatial distance between the current injection well and the k-th injection well. This represents the normalized depth difference between the current injection well and the corresponding oil layer of the k-th injection well. The purpose of normalization here is to avoid excessive spatial distance and reduce the influence of the depth difference. The normalization method used is maximum-minimum normalization. The distance weight, normalized spatial distance and normalized depth difference in this formula are all dimensionless quantities.

[0066] Calculation principle: In the process of assessing the distance between the current injection well and the neighboring area, the main measure is the contribution of the spatial distribution of similar injection wells in the neighboring area to the current injection well. For the current injection well, the closer the spatial distance and depth distribution, the closer the geological conditions of the reference injection well in the neighboring area are to the current injection well, and thus the greater the weight value of the reference injection well.

[0067] Secondly, by combining the scatter plot data of polymer usage and recovery rate of shale samples from the current injection well in the oil displacement experiment, we can analyze the differences between the pore throat structure of the shale in the current injection well and the neighboring reference injection well.

[0068] This allows for the assessment of the heterogeneity of the current injection well. The formula for calculating the heterogeneity value B is as follows: In the formula, B represents the heterogeneity value of the current injection well, and N represents the number of injection wells of the same type in the neighborhood of the current injection well. This represents the normalized weight of the k-th reference injection well within the neighborhood of the current injection well. Summation normalization is used here, where `sum` represents the summation function. This represents the difference in first-recovery rate between the current injection well and the k-th reference injection well at the i-th polymer dosage. By iterating through all injection wells and normalizing based on the maximum-minimum values ​​of heterogeneity assessments of the same type, the normalized heterogeneity assessment value of the current injection well is obtained, denoted as . .

[0069] Calculation principle: Since the relationship between polymer dosage and recovery rate obtained from core flooding experiments can more realistically reflect the pore throat structure inside the shale, if the shale growth difference between the current injection well and the reference injection well in the adjacent range is greater, it indicates that the pore throat structure difference is greater. At this time, the recovery rate difference corresponding to the discrete polymer dosage in the core flooding experiment is greater, and the value of the heterogeneity assessment obtained is greater.

[0070] S14. According to the correction weight of each injection well, the first curve and the second curve of the injection well are weighted and fused to obtain the target curve of the injection well, and the target polymer dosage of the injection well is determined according to the target curve.

[0071] The correction weight for each injection well is determined based on the fitting deviation and heterogeneity value of that injection well.

[0072] In one embodiment, before weighted fusion of the first and second curves of the injection well according to the correction weight of each injection well to obtain the target curve of the injection well, the method further includes: The heterogeneity value and fitting deviation value of each injection well are normalized, and the correction weight of the injection well is determined based on the ratio of the normalized heterogeneity value to the fitting deviation value.

[0073] The heterogeneity value of each injection well Deviation from fit Normalization yields the normalized heterogeneous values. The normalized fit deviation value Based on the normalized heterogeneous values Deviation from fit The ratio is used to determine the correction weight of the injection well. Correction weights The calculation formula is: .

[0074] In one embodiment, the first and second curves of the injection well are weighted and fused according to the correction weight of each injection well to obtain the target curve of the injection well, including: Under the same polymer dosage, the target recovery rate of the target curve under the polymer dosage is obtained by multiplying the first recovery rate of the first curve by the correction weight and the second recovery rate of the second curve by the supplementary weight; the supplementary weight indicates the difference between the preset value and the correction weight. Calculate the target recovery rate corresponding to multiple polymer dosages, and use the polymer dosage as the x-axis and the target recovery rate of each injection well at the corresponding polymer dosage as the y-axis to determine the target curve of the injection well.

[0075] The above method yields the relationship between polymer usage and recovery rate deviation in a single injection well. Core flooding experiments can reflect the internal reservoir development of shale samples and measure heterogeneity, while numerical simulation results tend to be more influenced by theoretical calculations and are more susceptible to heterogeneity. Based on the above analysis, numerical corrections are made to the current single injection well. The specific method is as follows: ; Where L(m) is the objective function, representing the recovery rate value corresponding to the m-th polymer dosage, in percentage (%). This represents the correction weight, calculated as follows: Dimensionless units The first curve represents the first recovery rate of the core flooding experiment at the m-th polymer dosage, expressed as a percentage. The second curve represents the numerical value of the second recovery rate under the numerical simulation at the m-th polymer dosage, in units of %.

[0076] Numerical simulation assesses the impact of polymer dosage on oil recovery by establishing polymer models, and in principle, it is closer to reality. Without considering heterogeneity, the smaller the fitting bias, the greater the contribution of the numerical simulation to the correction process. However, by combining oil displacement experiments with experimental difference analysis of reference injection wells within a nearby area, the impact of geological heterogeneity can be assessed. Therefore, the greater the heterogeneity difference, the more easily the numerical simulation results are interfered with, and the contribution of the recovery rate obtained from core-displacement experiments is greater. Thus, combining the results of core-displacement experiments and numerical simulations to achieve numerical correction of polymer dosage and recovery rate yields a more realistic oil enhancement curve relationship.

[0077] In one embodiment, determining the target polymer dosage for the injection well based on the target curve includes: The polymer dosage corresponding to the maximum target recovery rate in the target curve of each injection well is determined as the target polymer dosage for that injection well.

[0078] The above method is used to perform correction analysis on polymer usage and polymer recovery rate, thereby obtaining the objective function of a single injection well. For example, taking a certain injection well of type II among the 28 injection wells in the test area as an example, its corresponding oil increase curve is obtained.

[0079] The current injection concentration in the injection well is 900 mg / L. Using the objective function, it can be observed that the recovery rate gradually increases with the increase in polymer dosage. When the emulsion polymer concentration is 900 mg / L, increasing the polymer dosage from 600 mg / L·PV to 800 mg / L·PV increases the recovery rate by 1.30 percentage points; from 800 mg / L·PV to 1000 mg / L·PV, the increase is 0.96 percentage points; from 1000 mg / L·PV to 1200 mg / L·PV, the increase is 0.74 percentage points; and from 1200 mg / L·PV to 1400 mg / L·PV, the increase is 0.56 percentage points.

[0080] Ultimately, by combining the cost of polymer usage and the benefits of increased oil production, and with the goal of maximizing net profit, the polymer usage corresponding to the highest target recovery rate in the target curve of each injection well is determined as the target polymer usage for that injection well.

[0081] S15. Based on the geological parameters and pressure constraint parameters of each injection well, determine the injection rate of the injection well, and inject the target amount of polymer into the injection well based on the injection rate.

[0082] The injection rate of emulsion polymers is a key factor affecting reservoir recovery. A suitable injection rate can reduce the shear degradation of the polymer solution as it passes through the perforation, resulting in a higher working viscosity of the polymer solution in the oil reservoir. In addition, during the injection process of a well group, the injection rate of a single well should be matched with the overall injection situation to ensure the injection-production balance among the injection well groups. Therefore, it is necessary to determine the injection rate of emulsion polymers.

[0083] In actual production, based on production experience, the increase in polymer injection rate is limited by the on-site injection pressure. The boundary condition is that the injection pressure in the injection well should not exceed the overlying rock pressure of the oil layer, and a margin should be left between the two. Therefore, the relationship between the emulsion polymer injection rate and the maximum wellhead injection pressure is as follows: ; Where V represents the current injection rate of the injection well, expressed in PV / a. This indicates the maximum injection pressure at the wellhead, expressed in MPa. This indicates the lowest apparent water absorption index of the oil layer, in units of... L represents the distance between injection and production wells, in meters. The porosity of the oil layer is expressed as a percentage (%). In this embodiment, if the porosity is 23%, the value is 0.23 in the formula. It should be noted that the minimum apparent water absorption index of the oil layer refers to the ratio of the minimum daily water injection rate that can be maintained under wellhead pressure to the wellhead pressure. It reflects the basic water absorption capacity that can be maintained even under the most unfavorable conditions. Through stratified testing, the water absorption rate and wellhead pressure of each sub-layer are obtained, and the apparent water absorption index of each layer is calculated. The minimum value of each layer is taken as the minimum apparent water absorption index of the entire well.

[0084] Calculation principle: In single-well injection, the method of leveling the thickness of the well group is used as the basis, while taking into account the injection capacity of the injection well and the connectivity of the surrounding injection wells. The injection situation of the single well is adjusted by using the oil saturation in the well group and the water content of the surrounding injection wells.

[0085] Therefore, analysis was conducted on various injection wells within the experimental area. The apparent water absorption index of the injection wells decreased from 0.98 m³ / d·m·MPa in the blank water drive to 0.58 m³ / d·m·MPa during the low-value period of polymer injection, a decrease of 40.8%. When selecting the injection rate for emulsion polymer flooding, considering the viscosity-enhancing properties of emulsion polymers, and taking into account a maximum decrease of 50% in the apparent water absorption index of the oil layer after polymer injection, the corresponding injection rate is 0.16 PV / a (Table 3), which is not higher than the overburden pressure of 14.1 MPa in the target layer of the experimental area. The injection rate in the designed experimental area is controlled between 0.14 and 0.16 PV / a. The highest wellhead injection pressure under different injection rates and apparent water absorption index decrease conditions is shown in Table 3. Table 3. Maximum wellhead injection pressure under different injection rates and apparent water absorption index decreases.

[0086] This application's embodiments, through a categorized design, ensure a polymer concentration matching rate of ≥80% with reservoir permeability, thereby improving the system's swept volume. The polymer concentration is determined based on the reservoir's geological conditions, avoiding inrush into high-permeability layers and under-injection into low-permeability layers. In determining polymer dosage parameters, traditional methods rely solely on numerical simulation results, neglecting the influence of geological heterogeneity. Therefore, this scheme utilizes core flooding experiments to reflect the pore-throat growth of individual injection wells. By combining geological differences between injection wells and deviations between numerical simulations and core experiments, numerical corrections are made to the polymer dosage and recovery rate, achieving more accurate polymer dosage parameter determination. This matches shale polymer flooding, improving polymer utilization and reducing development costs. Regarding injection rate, the injection pressure is set to not exceed the overlying rock pressure boundary condition to optimize the injection rate and reduce polymer shear degradation.

[0087] It should be understood that, althoughFigure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0088] This application also provides a personalized injection parameter optimization design system based on emulsion polymer drive, such as... Figure 2 As shown, the system includes: The displacement module 21 is used to conduct core displacement experiments for each injection well in each type of injection well, based on the polymer concentration corresponding to that type of injection well, at different polymer dosages, to obtain the first curve for each injection well; the polymer concentration corresponding to each type of injection well is obtained according to the classification results of each injection well in the target oilfield block; The first determining module 22 is used to determine the fitting deviation value of each injection well based on the deviation between the first curve and the second curve of each injection well; the second curve of each injection well is obtained by numerical simulation based on the geological parameters of the injection well; the first curve and the second curve are used to characterize the correspondence between polymer dosage and recovery rate; the fitting deviation value indicates the degree of deviation between the core displacement experiment results and the numerical simulation results. The second determining module 23 is used to determine the heterogeneity value of an injection well based on the distance between each injection well and each adjacent injection well of the same type, and the deviation between the recovery rate of the injection well at each polymer dosage and the recovery rate of each adjacent injection well of the same type at the same polymer dosage; the recovery rate of each injection well at each polymer dosage is obtained from the first curve of the injection well; the heterogeneity value indicates the degree of dispersion of the recovery rate of each injection well and adjacent injection wells of the same type at the same polymer dosage; The third determining module 24 is used to perform weighted fusion of the first curve and the second curve of the injection well according to the correction weight of each injection well to obtain the target curve of the injection well, and to determine the target polymer dosage of the injection well according to the target curve; the correction weight of each injection well is determined according to the fitting deviation value and heterogeneity value of the injection well. The fourth determining module 25 is used to determine the injection rate of each injection well based on the geological parameters and pressure constraint parameters of each injection well, and to inject the target amount of polymer into the injection well based on the injection rate.

[0089] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs.

[0090] Figure 3 This is a schematic diagram of the structure of an electronic device according to an example embodiment of this application. The electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor. When the processor executes the computer program, it implements the method described in any of the above embodiments. Figure 3 The electronic device 30 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0091] like Figure 3 As shown, the electronic device 30 can be manifested as a general-purpose computing device, such as a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including memory 32 and processor 31).

[0092] Bus 33 includes a data bus, an address bus, and a control bus.

[0093] The memory 32 may include volatile memory, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.

[0094] The memory 32 may also include a program tool 325 (or utility) having a set (at least one) program module 324, such program module 324 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0095] The processor 31 executes various functional applications and data processing, such as the methods provided in any of the above embodiments, by running computer programs stored in the memory 32.

[0096] Electronic device 30 can also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 35. Furthermore, electronic device 30 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public network, such as the Internet) via network adapter 36. As shown, network adapter 36 communicates with other modules of electronic device 30 via bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with electronic device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0097] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0098] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in any of the above embodiments.

[0099] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0100] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0101] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method described in any of the above embodiments.

[0102] The program code for executing the computer program product of this application can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0103] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0104] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

[0105] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for optimizing personalized injection parameters based on emulsion polymer drive, characterized in that, The method includes: For each injection well in each type of injection well, core displacement experiments were conducted at different polymer concentrations based on the corresponding polymer concentration for that type of injection well to obtain the first curve for each injection well; the polymer concentration for each type of injection well was obtained based on the classification results of each injection well in the target oilfield block; The fitting deviation value of each injection well is determined based on the deviation between the first and second curves. The second curve of each injection well is obtained by numerical simulation based on the geological parameters of the injection well. The first and second curves are used to characterize the correspondence between polymer dosage and recovery rate. The fitting deviation value indicates the degree of deviation between the core displacement experiment results and the numerical simulation results. The heterogeneity value of an injection well is determined based on the distance between each injection well and each adjacent similar injection well, and the deviation between the recovery rate of the injection well at each polymer dosage and the recovery rate of each adjacent similar injection well at the same polymer dosage. The recovery rate of each injection well at each polymer dosage is obtained from a first curve of the injection well. The heterogeneity value indicates the degree of dispersion of the recovery rate of each injection well and adjacent similar injection wells at the same polymer dosage. The first and second curves of each injection well are weighted and fused according to the correction weight of each injection well to obtain the target curve of the injection well, and the target polymer dosage of the injection well is determined according to the target curve; the correction weight of each injection well is determined according to the fitting deviation value and heterogeneity value of the injection well. Based on the geological parameters and pressure constraint parameters of each injection well, the injection rate of the injection well is determined, and the target amount of polymer is injected into the injection well based on the injection rate.

2. The method for optimizing personalized injection parameters based on emulsion polymer drive as described in claim 1, characterized in that, Before obtaining the first curve for each injection well (based on the polymer concentration corresponding to that type of injection well) by conducting core displacement experiments at different polymer dosages for multiple injection wells within each injection well category, the process further includes: The effective thickness, permeability, and K80 permeability of each injection well were measured. Based on the effective thickness, permeability, and K80 permeability of each injection well, multiple injection wells in the target oilfield block are classified to obtain the classification results; Based on the classification results, the corresponding polymer concentration for each type of injection well is determined.

3. The method for optimizing personalized injection parameters based on emulsion polymer drive as described in claim 1, characterized in that, For each injection well within each type of injection well, based on the polymer concentration corresponding to that type of injection well, core displacement experiments are conducted at different polymer dosages to obtain the first curve for each injection well, including: Obtain a core sample from each injection well in each type of injection well, and place the core sample in a vacuum dryer for vacuuming. Simulated formation water was injected into the vacuum dryer until it submerged the core, and then atmospheric pressure was restored to allow the simulated formation water to seep into the core pores, resulting in a water-saturated core. The pore volume and porosity of the core were calculated based on the difference between the weight of the water-saturated core and the weight of the dry core. A water-saturated core was placed in a flow test apparatus, and formation water was injected into the core at a constant flow rate. After the flow stabilized, the absolute permeability of the core was calculated based on Darcy's law according to the inlet pressure, outlet pressure, and stable flow rate at the outlet of the flow test apparatus. The absolute permeability is used to characterize the seepage capacity of the core. The formation water in the core was displaced by simulated oil until the water content of the produced liquid at the outlet of the flow test device was less than a preset first threshold, thus obtaining an oil-saturated core. The original oil saturation of the core was determined based on the difference between the weight of the oil-saturated core and the weight of the core before the injection of simulated oil. Simulated formation water was used to waterflood oil-saturated core samples until the oil production at the outlet of the flow experiment device was less than a preset second threshold. The waterflood recovery rate of the core sample was calculated based on the ratio of the amount of crude oil produced during the waterflooding process to the oil content of the oil-saturated core sample. The waterflood recovery rate is used to characterize the amount of oil remaining after waterflooding in a single oil recovery process. Based on the injection well category to which each injection well belongs, the corresponding polymer concentration for that injection well is determined. After the water drive is completed, the simulated formation water is switched to a polymer solution with the corresponding polymer concentration. The amount of polymer in the solution is gradually increased and injected into the core. The total amount of crude oil produced under each polymer concentration is recorded. The first recovery rate of the injection well at each polymer dosage is calculated based on the total crude oil produced and the original oil content of the core. The original oil content of the core indicates the oil content in an oil-saturated core. With polymer dosage as the x-axis and the first recovery rate of each injection well at the corresponding polymer dosage as the y-axis, the first curve of the injection well is determined; when the polymer dosage is zero, the first recovery rate is equal to the water drive recovery rate.

4. The method for optimizing personalized injection parameters based on emulsion polymer drive as described in claim 3, characterized in that, The second curve is obtained according to the following steps: Numerical simulation models are constructed based on the pore volume, porosity, absolute permeability, and initial oil saturation of each injection well. In the numerical simulation model, the oil displacement process under different polymer dosages is simulated sequentially according to the polymer concentration corresponding to the injection well, and the amount of crude oil produced under each polymer dosage is recorded. The corresponding secondary recovery rate is calculated based on the preset original oil content in the numerical simulation model. The polymer concentration corresponding to each injection well is determined according to the polymer concentration corresponding to the injection well category to which the injection well belongs. With polymer dosage as the x-axis and the secondary recovery rate of each injection well at the corresponding polymer dosage as the y-axis, the second curve of the injection well is determined.

5. The method for optimizing personalized injection parameters based on emulsion polymer drive as described in claim 1, characterized in that, The step of determining the fitting deviation value of each injection well based on the deviation between the first curve and the second curve includes: Within a preset range of polymer dosage, the absolute difference between the recovery rates of the first and second curves of each injection well under the same polymer dosage is calculated, and the absolute difference is accumulated within the preset range to obtain the fitting deviation value of the injection well.

6. The method for optimizing personalized injection parameters based on emulsion polymer drive as described in claim 1, characterized in that, The process of determining the heterogeneity value of an injection well based on the distance between each injection well and each adjacent injection well of the same type, and the deviation between the recovery rate of the injection well at each polymer dosage and the recovery rate of each adjacent injection well of the same type at that polymer dosage, includes: The distance weight between each injection well and each adjacent injection well of the same type is determined based on the spatial distance between each injection well and each adjacent injection well of the same type, and the depth difference between the injection well and the injection well of the same type. Calculate the difference in first recovery rate between each injection well and each adjacent injection well of the same type at each polymer dosage, and obtain the difference in first recovery rate between the injection well and the injection well of the same type at that polymer dosage; The sum of the first recovery rate differences between each injection well and each adjacent injection well of the same type at multiple polymer dosages is calculated to obtain the sum of the first recovery rates between the injection well and the injection well of the same type. Based on the normalized spatial weights between each injection well and each adjacent injection well of the same type, and the first recovery rate and value between the injection well and the injection well of the same type, the heterogeneity index between the injection well and the injection well of the same type is determined; the heterogeneity index indicates the degree of dispersion of the recovery rate between each injection well and the injection well of the same type at the same polymer dosage. The heterogeneity value of an injection well is obtained by summing the heterogeneity indices between each injection well and its neighboring injection wells of the same type.

7. The method for optimizing personalized injection parameters based on emulsion polymer drive as described in claim 1, characterized in that, Before the step of weightedly fusing the first and second curves of the injection well according to the correction weight of each injection well to obtain the target curve of the injection well, the method further includes: The heterogeneity value and fitting deviation value of each injection well are normalized, and the correction weight of the injection well is determined based on the ratio of the normalized heterogeneity value to the fitting deviation value.

8. The method for optimizing personalized injection parameters based on emulsion polymer drive as described in claim 1, characterized in that, The step of weightedly fusing the first and second curves of each injection well according to its correction weight to obtain the target curve of the injection well includes: Under the same polymer dosage, the target recovery rate of the target curve under the polymer dosage is obtained by multiplying the first recovery rate of the first curve by the correction weight and the second recovery rate of the second curve by the supplementary weight; the supplementary weight indicates the difference between the preset value and the correction weight. Calculate the target recovery rate corresponding to multiple polymer dosages, and use the polymer dosage as the x-axis and the target recovery rate of each injection well at the corresponding polymer dosage as the y-axis to determine the target curve of the injection well.

9. The method for optimizing personalized injection parameters based on emulsion polymer drive as described in claim 1, characterized in that, Determining the target polymer dosage for the injection well based on the target curve includes: The polymer dosage corresponding to the maximum target recovery rate in the target curve of each injection well is determined as the target polymer dosage for that injection well.

10. The method for optimizing personalized injection parameters based on emulsion polymer drive as described in claim 1, characterized in that, The step of determining the injection rate of each injection well based on its geological parameters and pressure constraint parameters includes: The injection rate of each injection well is determined based on the product of the lowest apparent water absorption index of the oil layer and the highest injection pressure at the wellhead, porosity, and injection-production well spacing. Among these parameters, the geological parameters include porosity, injection-production well spacing, and the lowest apparent water absorption index of the oil layer, while the pressure constraint parameter includes the highest injection pressure at the wellhead.

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