A low-permeability oil reservoir percolation region division method, device, equipment and medium

Abstract

The application provides a low-permeability reservoir seepage region division method, device, equipment and medium. It relates to the technical field of oil and gas field development. The method comprises the following steps: obtaining the minimum threshold pressure gradient required for the target fluid to produce continuous seepage in the core sample, and the linear threshold pressure gradient at which the seepage velocity changes from nonlinearity to linearity; embedding a threshold pressure gradient model and a stress sensitivity model in an initial injection-production numerical model established based on the injection-production well pattern of the target reservoir, simulating the seepage of the target fluid based on the obtained injection-production numerical model, and obtaining the local pressure gradient field of the target reservoir; in the simulation: the threshold pressure gradient model performs segmented constraint on the seepage velocity of the target fluid according to the minimum threshold pressure gradient and the linear threshold pressure gradient; the stress sensitivity model dynamically corrects the parameters of the target reservoir based on the pressure change caused by injection-production disturbance; and based on the comparison result of the local pressure gradient field and the two pressure gradients, the seepage region of the target reservoir is divided.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, and in particular to a method, apparatus, equipment and medium for dividing seepage zones in oil reservoirs. Background Technology

[0002] Identifying inter-well flow capacity directly determines the accuracy of inter-well pressure transmission capacity assessment and reservoir utilization prediction. In reservoirs with small pore throats and strong heterogeneity, fluid seepage deviates significantly from the classic Darcy flow law. Within the same well network, different areas may simultaneously exhibit multiple states, including no flow, nonlinear slow seepage, and linear stable seepage. Without targeted seepage area delineation, it is impossible to distinguish between effective flow channels and stagnant blind zones between wells, making it difficult to accurately determine whether pressure can be effectively transmitted and whether fluid can be truly utilized. This leads to systematic deviations in development scheme design, injection and production parameter optimization, and reserve utilization evaluation. Therefore, accurately identifying and delineating the flow states of different areas between wells is a key prerequisite and necessary technical step for achieving efficient reservoir development and rational strategy formulation.

[0003] Existing reservoir flow characterization techniques typically employ numerical simulation methods based on empirically assigned parameters. The core principle involves modifying the classical Darcy flow equation by introducing a threshold pressure gradient parameter, which depends on theoretical assumptions or empirical values, when establishing an inter-well grid model. The modified nonlinear seepage equation is then iteratively solved under a set operating regime to calculate the inter-well scale pressure distribution field and fluid seepage state. However, the core mechanism of this method, where the threshold parameter relies on empirical values, leads to a disconnect between the boundary constraints of the input model and the physical characteristics at the actual core scale. Consequently, the simulated inter-well pressure gradient field cannot accurately reflect the characteristics of the fluid at different seepage velocity stages, ultimately making it difficult to directly translate the results obtained from laboratory core experiments into quantitative criteria for actual reservoir inter-well flow zoning. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, equipment, and medium for delineating seepage zones in oil reservoirs to address the aforementioned technical problems.

[0005] The following technical solution is adopted in this specification: This specification provides a method for delineating seepage zones in oil reservoirs, including: Obtain core samples and parameters of the target reservoir; Under the target reservoir temperature and pressure conditions, obtain the minimum starting pressure gradient required for the target fluid to generate continuous seepage in the core sample, and the linear starting pressure gradient corresponding to the change of the seepage velocity of the target fluid in the core sample from nonlinear to linear. An initial injection-production numerical model is established based on the injection-production well network conditions of the target reservoir. A threshold pressure gradient model and a stress-sensitive model are embedded in the initial injection-production numerical model to obtain an injection-production numerical model. Based on the injection-production numerical model, the seepage of the target fluid is simulated to obtain the local pressure gradient field of the target reservoir. The threshold pressure gradient model constrains the seepage velocity of the target fluid in segments according to the minimum starting pressure gradient and the linear starting pressure gradient. The stress-sensitive model dynamically corrects the target reservoir parameters based on the pressure changes in the target reservoir caused by injection-production disturbances. Based on the comparison results of the local pressure gradient field with the minimum starting pressure gradient and the linear starting pressure gradient, the seepage region of the target reservoir is divided.

[0006] Furthermore, the establishment of the initial injection-production numerical model based on the injection-production well network conditions of the target reservoir specifically includes: The target reservoir is discretized using a Cartesian coordinate system to generate a structured grid containing multiple grid cells; Based on the injection-production well network conditions of the target reservoir, the well location coordinates of water injection wells and oil production wells are configured in the structured grid to establish an initial injection-production numerical model containing at least one water injection well-oil production well pair.

[0007] Furthermore, the threshold pressure gradient model applies piecewise constraints to the target fluid seepage velocity based on the minimum initiation pressure gradient and the linear initiation pressure gradient, specifically including: When the local pressure gradient of a grid cell is less than the minimum starting pressure gradient, the target fluid seepage velocity in the corresponding grid cell is set to zero. When the local pressure gradient of a grid cell is greater than or equal to the minimum starting pressure gradient and less than the linear starting pressure gradient, the target fluid seepage velocity in the corresponding grid cell is calculated using a nonlinear seepage equation. When the local pressure gradient of a grid cell is greater than or equal to the linear starting pressure gradient, Darcy's law is used to calculate the target fluid seepage velocity in the corresponding grid cell.

[0008] Furthermore, the step of performing target fluid seepage simulation based on the injection-production numerical model to obtain the local pressure gradient field of the target reservoir specifically includes: The injection-machining numerical model is solved iteratively in a numerical simulator; In each iteration step, the local pressure gradient of each grid cell is obtained based on the current pressure field distribution; The local pressure gradient is compared with the minimum starting pressure gradient and the linear starting pressure gradient, and the flow state of the target fluid in each grid cell and the target reservoir parameters of each grid cell are adjusted according to the comparison results. The adjusted target reservoir parameters are fed back into the injection-production numerical model for the next iteration until the pressure field distribution converges, thus obtaining the local pressure gradient field of the target reservoir.

[0009] Furthermore, the stress-sensitive model dynamically corrects the target reservoir parameters based on the target reservoir pressure changes caused by injection-production disturbances, and uses elastoplastic evolution law to characterize the porosity change path with pore pressure, specifically including: During the pressure drop phase when the pore pressure decreases, when the pore pressure is higher than the preset plastic critical pressure, the porosity is controlled to decrease reversibly and linearly along the elastic compaction path as the pore pressure decreases; when the pore pressure decreases to or below the plastic critical pressure, the porosity is controlled to decrease irreversibly and nonlinearly along the plastic compaction path as the pore pressure decreases. During the pressure rise phase of pore pressure recovery, the porosity is controlled to only undergo partial elastic rebound, so that the porosity after the rebound is kept lower than the initial porosity state before the pressure drop, forming a hysteretic rebound path to obtain the dynamic porosity under the current iteration step. The dynamic porosity of the current iteration step is assigned to the corresponding mesh element for use in the next iteration step.

[0010] Furthermore, the process of dividing the target reservoir into seepage zones based on the comparison results of the local pressure gradient field with the minimum initiation pressure gradient and the linear initiation pressure gradient specifically includes: When the local pressure gradient of a grid cell is less than the minimum starting pressure gradient, the corresponding grid cell is divided into an unused area. When the local pressure gradient of a grid cell is greater than or equal to the minimum starting pressure gradient and less than the linear starting pressure gradient, the corresponding grid cell is divided into a critical movable region. When the local pressure gradient of a grid cell is greater than or equal to the linear initiation pressure gradient, the corresponding grid cell is divided into a linear displacement region.

[0011] Furthermore, the minimum start-up pressure gradient and the linear start-up pressure gradient are obtained through a displacement experiment combining unsteady and steady states; the displacement experiment includes: The airtightness of the displacement experimental system was tested, and the core samples were sequentially subjected to vacuuming and formation water saturation treatment to establish the bound water state. A constant flow displacement was performed on the core sample under the bound water state, and the displacement inlet pressure, displacement outlet pressure, and volume data of the produced target fluid were continuously collected during the displacement process. Based on the collected inlet pressure, outlet pressure, and volume data of the target fluid produced, a curve showing the relationship between pressure gradient and seepage velocity is established. From the corresponding relationship curve, the inflection point pressure gradient corresponding to the establishment of continuous flow is identified as the minimum starting pressure gradient, and the inflection point pressure gradient corresponding to the transition from the nonlinear curved segment to the near-linear straight segment of the corresponding relationship curve is identified as the linear starting pressure gradient.

[0012] This specification provides a reservoir seepage zone delineation device, comprising: The data acquisition module is used to acquire core samples and parameters of the target reservoir. The pressure gradient threshold acquisition module is used to acquire, under the temperature and pressure conditions of the target reservoir, the minimum starting pressure gradient required for the target fluid to generate continuous seepage in the core sample, and the linear starting pressure gradient corresponding to the change of the seepage velocity of the target fluid in the core sample from nonlinear to linear. The seepage simulation module is used to establish an initial injection-production numerical model based on the injection-production well network conditions of the target reservoir, and to embed a threshold pressure gradient model and a stress-sensitive model into the initial injection-production numerical model to obtain an injection-production numerical model; based on the injection-production numerical model, target fluid seepage simulation is performed to obtain the local pressure gradient field of the target reservoir; wherein, the threshold pressure gradient model constrains the target fluid seepage velocity in segments according to the minimum starting pressure gradient and the linear starting pressure gradient, and the stress-sensitive model dynamically corrects the target reservoir parameters based on the target reservoir pressure changes caused by injection-production disturbances; The region division module is used to divide the target reservoir into seepage regions based on the comparison results of the local pressure gradient field with the minimum starting pressure gradient and the linear starting pressure gradient.

[0013] This specification provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for dividing reservoir seepage zones.

[0014] This specification provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method for dividing the seepage zone of an oil reservoir.

[0015] The above-mentioned technical solutions adopted in this specification can achieve the following beneficial effects: This invention replaces traditional empirical values ​​with the minimum and linear initiation pressure gradients of the target reservoir core, anchoring the seepage initiation criteria that conform to the real physical laws of the reservoir from the source. This eliminates the systematic deviation caused by artificially setting the initiation pressure. Based on this, the measured pressure gradient is integrated into the threshold pressure gradient model to achieve segmented constraints on fluid seepage behavior, restoring the three-stage real evolution law of no flow, nonlinear seepage, and linear seepage in low-permeability reservoirs. At the same time, combined with the stress-sensitive effect of dynamic correction of reservoir parameters driven by injection and production pressure disturbances, the threshold pressure gradient constraint and the dynamic evolution of reservoir properties complement each other. This allows the simulated local pressure gradient field to reflect the real initiation seepage characteristics and dynamic changes in porosity and permeability parameters of the reservoir, thereby achieving accurate conversion of indoor core experimental results into quantitative criteria for inter-well flow zoning in actual reservoirs. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 This is one of the flowcharts illustrating a method for dividing seepage zones in an oil reservoir, as provided in this specification. Figure 2 This is a schematic diagram of a conglomerate core sample provided in this specification; Figure 3 This is a schematic diagram of a target fluid provided in this specification; Figure 4 This is a schematic diagram of a stress-sensitive model provided in this specification; Figure 5 This is the second flowchart illustrating a method for dividing seepage zones in an oil reservoir, as provided in this specification. Figure 6 This specification provides a schematic diagram illustrating the pressure gradient-seepage velocity relationship and the identification of MTPG and PTPG. Figure 7 This specification provides a schematic diagram of the relative permeability curves for an oil-water system and an oil-gas system. Figure 8 This specification provides a three-dimensional schematic diagram and cross-sectional view of an inter-well numerical model. Figure 9 This specification provides a numerical model of an injection-production well and a schematic diagram of the pressure gradient field and flow zoning between wells. Figure 10 This is a schematic diagram of a reservoir seepage zone delineation device provided in this specification; Figure 11 This is a schematic diagram of a computer device provided for this specification. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0019] The technical solution provided by this invention can be applied to scenarios involving the identification of inter-well flow capacity, the delineation of threshold control zones, and the evaluation of development effects in low-permeability and ultra-low-permeability conglomerate reservoirs. Low-permeability conglomerate reservoirs typically exhibit characteristics such as small pore throats, poor connectivity, strong heterogeneity, and high flow resistance. The fluid flow process in these reservoirs often deviates from the classic Darcy flow law, exhibiting a significant threshold pressure gradient effect. When the local pressure gradient falls below a certain threshold, the fluid struggles to form continuous and effective flow, leading to weakened inter-well pressure transmission capacity, reduced reservoir utilization, and decreased displacement efficiency.

[0020] This application provides a method for inter-well flow zoning based on threshold pressure gradients to address the problem in existing technologies where experimentally measured threshold pressure gradient parameters are difficult to directly apply to inter-well flow zoning. The technical solution provided by this invention is summarized as follows: Core samples from a target low-permeability conglomerate reservoir are selected and basic reservoir parameters are obtained; under reservoir temperature and pressure conditions, displacement experiments are conducted to obtain the minimum initiation pressure gradient and the linear initiation pressure gradient; an inter-well numerical model of the target reservoir (injection and production) is established to calculate the inter-well pressure gradient field; the pressure gradient field is compared with the experimentally measured minimum initiation pressure gradient and linear initiation pressure gradient. When the local pressure gradient satisfies minimum initiation pressure gradient ≤ ∇p < linear initiation pressure gradient, it is classified as a critical movable zone; when the local pressure gradient satisfies ∇p ≥ linear initiation pressure gradient, it is classified as a linear displacement zone. This method achieves the direct transfer of laboratory threshold pressure gradient parameters to inter-well scale flow characterization, providing reliable technical support for identifying inter-well flow capacity and evaluating development schemes in low-permeability conglomerate reservoirs.

[0021] The method for dividing the reservoir seepage zone of the present invention is described below with reference to the accompanying drawings.

[0022] Figure 1 This is a flowchart illustrating a method for dividing seepage zones in an oil reservoir, as provided in this specification. Figure 1 As shown, the method includes the following: S101. Obtain core samples and target reservoir parameters from the target reservoir.

[0023] For example, in this embodiment, multiple representative core samples were first obtained from different depths and lithological sections of the target low-permeability conglomerate reservoir through on-site drilling and coring operations. At the same time, the basic parameters of the target reservoir were obtained by combining well logging, well logging and geological data. The basic parameters of the reservoir include at least porosity, permeability, initial formation pressure, formation temperature and formation fluid physical properties (such as crude oil viscosity, density, formation water salinity, etc.). Figure 2 This document presents a schematic diagram of a conglomerate core sample, showcasing three typical cores taken from the target reservoir: Cores #1 and #2 are typical conglomerate cores, with numerous gravel particles of varying sizes and uneven distribution visible on their cross-sections. This visually reflects the strong heterogeneity and complex pore structure of the conglomerate reservoir, representing a typical example of fluid flow patterns deviating from the classical Darcy's law in low-permeability conglomerate reservoirs. Core #3 exhibits less pronounced gravel characteristics and relatively homogeneous overall lithology. In contrast to the previous two conglomerate cores, this illustrates the differences in core characteristics and degrees of heterogeneity within the target reservoir. These representative core samples from the target reservoir will be used in subsequent high-temperature, high-pressure displacement experiments to obtain key flow parameters such as the minimum initiation pressure gradient and linear initiation pressure gradient of the target fluid under actual reservoir temperature and pressure conditions. This will provide reliable experimental data support for the subsequent construction of injection-production numerical models and inter-well flow zoning.

[0024] S102. Under the temperature and pressure conditions of the target reservoir, obtain the minimum starting pressure gradient required for the target fluid to generate continuous seepage in the core sample, and the linear starting pressure gradient corresponding to the change of the seepage velocity of the target fluid in the core sample from nonlinear to linear.

[0025] For example, representative core samples from the target reservoir obtained in S101 are placed in a high-temperature, high-pressure core displacement device. First, the device's temperature, confining pressure, and back pressure are set to match the formation temperature, overlying strata pressure, and formation pressure of the target reservoir, respectively, to simulate the actual temperature and pressure environment of the reservoir. Then, a target fluid is selected to conduct core displacement experiments. Figure 3 This specification provides a schematic diagram of a target fluid, such as... Figure 3As shown, the target fluid includes, but is not limited to, formation water, crude oil, carbon dioxide, and polymer solutions, among one or more. During the experiment, a combination of unsteady and steady-state methods was employed. The displacement pressure was gradually increased while the pressure difference and fluid seepage velocity at both ends of the core were recorded in real time, establishing a complete correlation curve between pressure gradient and seepage velocity. Based on this curve, two key parameters were determined: the minimum initiation pressure gradient, which is the minimum initiation pressure gradient corresponding to the fluid overcoming pore throat capillary forces and adsorption resistance within the core and beginning to establish a continuous flow channel; and the linear initiation pressure gradient, which is the pressure gradient corresponding to the transition from the nonlinear segment to the near-linear segment of the pressure gradient-seepage velocity relationship. At this point, the nonlinear effect of fluid flow deviating from Darcy's law is significantly reduced, and seepage behavior tends to stabilize. This experimental method can accurately capture the entire nonlinear seepage process characteristics of fluids in low-permeability conglomerate cores, rather than relying on empirical formulas for estimation. This provides a real and reliable experimental data foundation for the segmented constraints and stress-sensitive coupling of subsequent injection-production numerical models, avoiding the systematic bias introduced by traditional empirical parameters.

[0026] S103. Based on the injection-production well network conditions of the target reservoir, establish an initial injection-production numerical model. Embed a threshold pressure gradient model and a stress-sensitive model into the initial injection-production numerical model to obtain the injection-production numerical model. Based on the injection-production numerical model, perform target fluid seepage simulation to obtain the local pressure gradient field of the target reservoir. During the simulation: the threshold pressure gradient model performs segmented constraints on the target fluid seepage velocity according to the minimum starting pressure gradient and the linear starting pressure gradient; the stress-sensitive model dynamically corrects the target reservoir parameters based on the pressure changes in the target reservoir caused by injection-production disturbances.

[0027] For example, firstly, based on the injection-production well network conditions, reservoir basic parameters, and fluid properties of the target reservoir, an initial numerical model at the injection-production well group scale is established. This model takes the inter-well region as the research object, divides it into grid cells, and sets boundary and initial conditions to provide a basic framework for subsequent seepage simulation. Based on this, the minimum starting pressure gradient and linear starting pressure gradient obtained from the S102 experiment are embedded into a threshold pressure gradient model, and this model is coupled into the initial injection-production numerical model to achieve segmented constraints on the target fluid seepage velocity: when the local pressure gradient is less than the minimum starting pressure gradient, the fluid cannot form effective continuous flow, and the seepage velocity is set to 0; when the local pressure gradient is between the minimum starting pressure gradient and the linear starting pressure gradient, the fluid is in a nonlinear seepage stage, and the seepage velocity has a nonlinear relationship with the pressure gradient; when the local pressure gradient is greater than the linear starting pressure gradient, the fluid enters a linear seepage stage, and the seepage velocity approximately satisfies a linear relationship with the pressure gradient, thus restoring the true segmented seepage characteristics of low-permeability reservoir fluids.

[0028] Meanwhile, the stress-sensitive model is embedded in the same numerical model. Based on the pressure changes of each grid unit between wells during the injection and production process, the porosity, permeability and other parameters of the target reservoir are dynamically corrected. This simulates the physical process of the reservoir's pore throat structure being compressed or expanded and its physical properties evolving under pressure disturbance. Figure 4 This specification provides a schematic diagram of a stress-sensitive model, such as... Figure 4 As shown, this model uses pore pressure (MPa) as the abscissa and porosity φ as the ordinate, intuitively reflecting the elastoplastic evolution of reservoir porosity with pore pressure: Under initial conditions, as pore pressure decreases (effective stress increases), the reservoir first undergoes an elastic compaction stage, and porosity decreases reversibly and linearly with pressure; when the pore pressure continues to decrease to the plastic critical pressure P... plastic When the pressure decreases, the reservoir enters the plastic compaction stage, and the porosity decreases irreversibly and nonlinearly. When the pore pressure rises again, the reservoir only undergoes partial elastic rebound, and the porosity cannot be restored to the initial state, forming a hysteretic rebound path. This reflects the stress sensitivity and plastic deformation characteristics of low-permeability reservoirs under injection and production disturbances, and provides a physical basis for the dynamic correction of reservoir parameters in numerical simulation.

[0029] With the combined effect of the threshold pressure gradient model and the stress-sensitive model, an injection-production numerical model that can reflect the real physical characteristics of the reservoir is constructed. Then, based on this model, the seepage simulation of the target fluid is carried out, and the local pressure gradient value ∇p at each location in the inter-well region of the target reservoir is obtained by iterative solution, generating a complete local pressure gradient field, thus providing a direct numerical calculation basis for subsequent inter-well flow zoning.

[0030] S104. Based on the comparison results of the local pressure gradient field with the minimum starting pressure gradient and the linear starting pressure gradient, the seepage zone of the target reservoir is divided.

[0031] Exemplarily, first, based on the complete local pressure gradient field in the inter-well area of the target reservoir simulated by the injection-production numerical model in S103, the local pressure gradient ∇p of each grid cell in the model is traversed and compared one by one with the minimum threshold pressure gradient (MTPG) and the linear threshold pressure gradient (PTPG) measured in the experiment in S102, so as to achieve the direct transfer from the microscopic core experiment parameters to the macroscopic inter-well scale, providing an accurate quantitative division basis for the subsequent evaluation of reservoir production degree. The specific division rules are as follows: when the local pressure gradient of the grid cell satisfies ∇p < MTPG, the fluid cannot overcome the pore-throat resistance to form continuous flow, and the corresponding grid cell is divided into the non-production area; when the local pressure gradient of the grid cell satisfies MTPG ≤ ∇p < PTPG, the fluid is in the critical state of non-linear seepage and can only form slow and unstable flow, and the corresponding grid cell is divided into the critical movable area; when the local pressure gradient of the grid cell satisfies ∇p ≥ PTPG, the fluid enters the stable linear seepage stage and the flow efficiency is significantly improved, and the corresponding grid cell is divided into the linear displacement area. Through the above quantitative comparison rules, the boundary of the fluid flow state in different inter-well areas can be clearly characterized, and the accurate division of the non-production area, the critical movable area and the linear displacement area can be realized. It can not only identify the pressure gradient distribution characteristics of "high at both ends and low in the middle" in the inter-well area of the low-permeability conglomerate reservoir, clarify the distribution range of the high-production linear displacement area near the well and the weak-production critical movable area in the middle, but also dynamically characterize the migration law of the flow boundary under different development strategies, thus providing a direct implementation basis for the subsequent optimization of injection-production parameters, adjustment and selection of development plans.

[0032] The above steps S101~S104 achieve the direct transfer of the threshold pressure gradient parameters measured in the indoor core experiment to the flow characterization at the inter-well scale. By using the comparison relationship between the local pressure gradient and the minimum threshold pressure gradient (minimum threshold pressure gradient), linear threshold pressure gradient (linear threshold pressure gradient), the inter-well area can be quantitatively rather than qualitatively divided into flow zones. At the same time, it can accurately identify the pressure gradient distribution characteristics of "high at both ends and low in the middle" in the inter-well area of the low-permeability conglomerate reservoir, and further determine the weak-production area in the middle and the high-production area near the well, ultimately providing a reliable basis for the analysis of the inter-well flow capacity, evaluation of development effect and subsequent parameter adjustment of the low-permeability conglomerate reservoir.

[0033] Based on the above Figure 1 shown embodiments, in the above S102, when obtaining the minimum threshold pressure gradient required for the target fluid to generate continuous seepage in the core sample, the embodiments of the present application provide a possible implementation method: S201. Construct a displacement experiment system.

[0034] The workflow of this core displacement experimental system is as follows: First, the target reservoir core sample is loaded into the core holder. A vacuum pump is used to evacuate the core and the entire experimental pipeline to remove residual gas. Then, the fan heating device is turned on to heat the core holder to the target reservoir formation temperature. At the same time, the confining pressure pump is started to apply confining pressure to the core holder to simulate the pressure of the overlying rock formation. Back pressure is set at the system outlet through the back pressure pump and back pressure valve to simulate the formation pressure. After the system temperature and pressure conditions stabilize, the ISCO pump drives the target fluid, such as water, oil, gas, or polymer solution, into the core holder through the corresponding intermediate container via valve switching. Core displacement experiments are carried out under different displacement flow rates or pressure gradients. During the experiment, the pressure and temperature signals at the inlet and outlet of the core holder are collected in real time by sensors and transmitted to the data acquisition terminal for recording. The fluid produced at the outlet is measured by a measuring cylinder. Finally, the correlation between pressure gradient and seepage velocity is established by collecting pressure difference and flow rate data, and key seepage parameters such as the minimum starting pressure gradient and linear starting pressure gradient of the target fluid in the core sample are obtained.

[0035] S202. Perform airtightness testing on the displacement experiment, and sequentially perform vacuuming and formation water saturation treatment on the core samples to establish the bound water state.

[0036] For example, representative core samples from the low-permeability conglomerate reservoir of the Bai 21 test area in the Baikouquan Formation of Xinjiang Oilfield were first selected and prepared into standard cylindrical core plugs. The selected cores covered three different permeability levels: 2.15 mD, 38.66 mD, and 149.72 mD, corresponding to porosities of 6.5%, 5.91%, and 8.31%, respectively, to comprehensively reflect the seepage characteristics of different physical property sections of the target reservoir. Subsequently, the airtightness of the core displacement experimental system was tested to ensure that there were no leaks in the system pipelines, valves, and core holders, avoiding the impact of air leakage on the accuracy of pressure and flow data during the experiment. After passing the airtightness test, the core samples were sequentially cleaned and dried to remove... After removing residual drilling fluid, crude oil, and impurities from the core pores, a vacuum pump is connected for prolonged vacuuming to completely remove air from the core pores and pipelines, creating conditions for subsequent formation water saturation. After vacuuming, pre-prepared target formation water is injected into the core holder, and the formation water is pressurized to fully fill the core pores, establishing an initial saturated water state. Then, the bound water state under the target reservoir conditions is established through oil-water displacement, restoring the actual oil- and water-bearing conditions of the reservoir. At the same time, the corresponding displacement medium is configured according to experimental requirements, including at least one of formation water, crude oil, carbon dioxide, or polymer solution, to prepare for subsequent constant flow displacement experiments and start-up pressure gradient tests.

[0037] S203. Perform constant flow displacement on the core sample under bound water conditions, and continuously collect displacement inlet pressure, displacement outlet pressure, and volume data of the produced target fluid during the displacement process.

[0038] For example, after establishing the bound water state under the target reservoir conditions in the core sample, the core holder is placed in a fan-heated device, and the experimental temperature is set to the target reservoir formation temperature of 62°C. The confining pressure and back pressure are set by the confining pressure pump and back pressure pump respectively, so that the effective stress and back pressure of the core are stabilized at the target reservoir formation pressure of 25.5 MPa, completely simulating the real temperature and pressure environment of the reservoir. Then, according to the experimental purpose, the corresponding displacement medium is selected, including one or more of model oil, synthetic formation water, HPAM polymer solution or CO2, and pumped by ISCO pump at a preset constant pressure. Displacement experiments were conducted stepwise using flow gradients. After each flow rate stabilized, displacement continued, and sensors were used to collect real-time data on the displacement inlet and outlet pressures of the core holder. Simultaneously, a graduated cylinder was used to measure the volume of the target fluid produced at the outlet until the pressure and flow rate reached a stable state before switching to the next flow rate. The pressure difference and the volume of the produced fluid under different flow rate conditions were recorded throughout the process. This provided complete and reliable experimental data to support subsequent calculations of seepage velocity, construction of the relationship curve between pressure gradient and seepage velocity, and determination of the minimum starting pressure gradient and the linear starting pressure gradient.

[0039] S204. Based on the collected inlet pressure, outlet pressure, and target fluid volume data, establish the correlation curve between pressure gradient and seepage velocity.

[0040] For example, the inlet pressure, outlet pressure, and produced fluid volume data continuously collected in the S203 constant flow displacement experiment are first standardized. Combined with the effective length and cross-sectional area of ​​the core sample, the pressure gradient at both ends of the core corresponding to each stable displacement stage is calculated. At the same time, the corresponding fluid seepage velocity is calculated based on the produced fluid volume and displacement time, thereby constructing a complete pressure gradient-seepage velocity correspondence curve.

[0041] S205. From the corresponding relationship curves, the inflection point pressure gradient corresponding to the start of continuous flow is identified as the minimum starting pressure gradient, and the inflection point pressure gradient corresponding to the transition from the nonlinear curved section to the near-linear straight section of the corresponding relationship curve is identified as the linear starting pressure gradient.

[0042] For example, based on the segmented characteristics of this curve, two types of key parameters are identified: when the fluid overcomes the core pore throat resistance and bound water resistance, and establishes a continuous flow channel for the first time, the pressure gradient corresponding to the starting point of the curve entering the rising section from the zero-velocity zone is the minimum starting pressure gradient; as the displacement flow rate continues to increase, the relationship curve between the pressure gradient and the seepage velocity gradually transitions from an early nonlinear tortuous segment to a near-linear straight segment, and the inflection point pressure gradient corresponding to this transition point is the linear starting pressure gradient. At this point, the nonlinear effect of the fluid flow behavior deviating from Darcy's law is significantly weakened, and the flow enters the stable seepage stage. Through the above data processing and curve feature identification methods, the entire evolution law of fluid in low-permeability conglomerate cores from no flow to nonlinear seepage, and then to linear stable seepage can be accurately depicted, providing real and reliable experimental parameter support for the segmented constraints and inter-well flow zoning of subsequent injection-production numerical models.

[0043] By executing S201 to S205, the true threshold pressure gradient boundary affected by fluid properties can be obtained, thus providing a data basis for subsequent numerical model parameter assignment.

[0044] Based on the above Figure 1 In the embodiment shown, when obtaining the local pressure gradient field of the target reservoir in S103 above, this application provides a possible implementation method: S301. Discretize the target reservoir using a Cartesian coordinate system to generate a structured grid containing multiple grid cells. Based on the injection-production well network conditions of the target reservoir, configure the well location coordinates of the water injection well-production well pair in the structured grid system and establish an initial injection-production numerical model containing at least one water injection well-production well pair.

[0045] For example, based on the reservoir geological parameters and fluid properties of the low-permeability conglomerate reservoir in the Bai 21 test area of ​​the Baikouquan Formation in Xinjiang Oilfield, a numerical model between injection and production wells was constructed in CMG numerical simulation software. First, the target reservoir was discretized in three dimensions using a Cartesian coordinate system to generate a 50×50×10 structured grid system, achieving uniform partitioning of the reservoir space. Then, the well coordinates of the injection wells and production wells were configured in the grid system to form an initial injection-production numerical model containing a set of injection-production well pairs. The model input parameters included a reservoir depth of 2120m, an effective thickness of 50m, an initial formation pressure of 25.5MPa, an initial formation temperature of 62℃, an initial water saturation of 40%, as well as key reservoir and fluid properties such as porosity, three-dimensional permeability, relative permeability curves of oil-water or oil-gas, crude oil viscosity, and formation water viscosity, providing a complete basic framework and boundary conditions for subsequent embedding of multiphysics models and conducting seepage simulation.

[0046] S302. Embed the threshold pressure gradient model and stress-sensitive model into the initial injection-production numerical model to obtain the injection-production numerical model; iteratively solve the injection-production numerical model in the numerical simulator to obtain the local pressure gradient field of the target reservoir. This includes:

[0047] S3020, Boundary Condition Initialization: Set the water injection well as the constant flow injection boundary, the oil production well as the constant bottom hole flowing pressure boundary, and the reservoir outer boundary as the closed boundary; Based on the original formation pressure distribution of the target reservoir, initialize the pore pressure field of each grid cell as the starting pressure field for the first iteration.

[0048] S3021. Model Embedding and Parameter Input: The minimum starting pressure gradient and linear starting pressure gradient measured in S204 are used as threshold constraints and input into the initial injection-production numerical model established in S301. At the same time, the threshold pressure gradient model and the stress-sensitive model are embedded. The threshold pressure gradient model is used to determine the fluid flow state of each grid cell and implement the corresponding velocity constraints based on the comparison results of the local pressure gradient and the dual thresholds. The stress-sensitive model is used to characterize the change path of porosity with pore pressure using the elastoplastic evolution law, so as to dynamically update the reservoir porosity and permeability, and finally obtain the complete injection-production numerical model.

[0049] S3022, Iterative Step Pressure Field Solution: Start the iterative solution process in the numerical simulator, solve the seepage control equation based on the porosity, permeability distribution and flow state constraints of the current iteration step, update the pore pressure distribution of each grid cell, and calculate the local pressure gradient of each grid cell.

[0050] S3023, Flow State Determination and Reservoir Parameter Correction: (1) Flow state determination: Based on the local pressure gradient obtained by S3022, it is compared with the minimum starting pressure gradient and the linear starting pressure gradient, and the flow state of each grid cell is adjusted in segments: when the local pressure gradient is lower than the minimum starting pressure gradient, the fluid seepage velocity of the grid cell is set to zero and marked as a static zone; when the local pressure gradient is between the minimum starting pressure gradient and the linear starting pressure gradient, the grid cell is marked as a nonlinear seepage zone, and the flow velocity is calculated using the nonlinear seepage relationship that considers the inertial effect; when the local pressure gradient reaches or exceeds the linear starting pressure gradient, the grid cell is marked as a linear seepage zone, and the flow velocity is calculated using Darcy's law.

[0051] (2) Dynamic correction of reservoir parameters: Based on the pore pressure distribution of the current iteration step, the porosity is updated using the elastoplastic evolution law of the stress-sensitive model.

[0052] ① During the pressure drop stage when the pore pressure decreases: When the pore pressure is higher than the preset plastic critical pressure, the porosity is controlled to decrease reversibly and linearly along the elastic compaction path as the pore pressure decreases; when the pore pressure decreases to or below the plastic critical pressure, the porosity is controlled to decrease irreversibly and nonlinearly along the plastic compaction path as the pore pressure decreases.

[0053] ② During the pressure rise stage of pore pressure recovery: control the porosity to only undergo partial elastic rebound, so that the porosity after the rebound remains lower than the initial porosity state before the pressure drop, forming a hysteretic rebound path; This yields the dynamic porosity of the current iteration step, and updates the permeability of the current iteration step based on the correspondence between dynamic porosity and permeability. The dynamic porosity and permeability of the current iteration step are then assigned to the corresponding grid cells for use in solving the pressure field in the next iteration step.

[0054] S3024. Iteration Convergence Judgment and Result Output: Determine whether the difference between the pressure field distribution of the current iteration step and the previous iteration step meets the preset convergence condition: When the maximum relative change of the overall pressure is lower than the first threshold, and the maximum relative changes of porosity and permeability are both lower than the second threshold, the iteration is determined to be converged, the calculation is stopped, and the local pressure gradient field of the target reservoir is output; otherwise, the updated flow state marker, dynamic porosity, permeability distribution, and pressure field are fed back to the injection-production numerical model, and the process returns to S3022 to continue iterating until the convergence condition is met.

[0055] Furthermore, embodiments of this application provide a possible implementation method for dynamic analysis and operating condition adaptation of inter-well flow zones: Based on the target reservoir local pressure gradient field and zoning criteria obtained from the aforementioned steps, the inter-well flow zoning results of low-permeability conglomerate reservoirs were analyzed. The calculation results show that the inter-well pressure gradient field exhibits a clear "high at both ends and low in the middle" distribution characteristic. The linear displacement zone (LDZ) is mainly concentrated in the high pressure gradient region near the injection well and production well. The local pressure gradient in this region is greater than or equal to the linear initiation pressure gradient, and the fluid is in a stable linear seepage state with high flow efficiency and good reservoir utilization. The critical movable zone (CMZ) is mainly distributed in the middle of the inter-well and its adjacent transition region. The local pressure gradient in this region is between the minimum initiation pressure gradient and the linear initiation pressure gradient. The fluid is in a critical state of nonlinear seepage with slow flow velocity and weak reservoir utilization. It is the key area for subsequent reservoir development optimization.

[0056] To achieve dynamic adaptation of inter-well flow zoning under different development conditions, based on the threshold criteria of fixed minimum starting pressure gradient and linear starting pressure gradient, the iterative solution process of the injection-production numerical model can be restarted by changing key development and reservoir parameters such as production differential pressure, well spacing, reservoir permeability level, or injection medium type. The inter-well pressure gradient field can be recalculated, and the aforementioned seepage zone division steps can be repeated to obtain the inter-well flow zoning results under different conditions. Multiple simulations have clearly demonstrated the impact of different operating conditions on inter-well flow zoning: Increasing the production pressure differential raises the overall inter-well pressure gradient, simultaneously expanding the critical movable zone and the linear displacement zone, thus increasing the overall reservoir utilization range. Increasing the well spacing lowers the inter-well pressure transmission efficiency, further expanding the low-pressure gradient region in the middle, and consequently shrinking the critical movable zone and the linear displacement zone, while expanding the non-utilized zone in the middle of the well. Conversely, increasing reservoir permeability or the flow rate of the injected medium reduces fluid seepage resistance, significantly increasing the inter-well flow activation range, extending the linear displacement zone towards the middle of the well, and adjusting the critical movable zone accordingly, thereby improving the overall reservoir utilization efficiency. This approach allows for a comprehensive understanding of the impact of different development conditions on inter-well flow patterns, providing precise numerical support for optimizing subsequent development plans.

[0057] This application provides a complete flowchart of a method for inter-well flow zoning in low-permeability conglomerate reservoirs. Figure 5 This is the second flowchart illustrating a method for dividing seepage zones in an oil reservoir, as provided in this specification. Figure 5As shown in the figure, first, select the target low-permeability conglomerate reservoir as the research object, collect representative core samples and obtain basic reservoir parameters such as porosity, permeability, formation temperature, formation pressure, water saturation, and fluid physical properties. At the same time, sort out the injection-production well pattern conditions, well spacing, and reservoir heterogeneity characteristics to provide basic data support for subsequent experiments and modeling. Then, under the temperature and pressure conditions of the target reservoir, carry out displacement experiments on the core samples with different displacement media, establish the response relationship between the pressure gradient and the seepage velocity, and identify the minimum starting pressure gradient MTPG corresponding to the start of continuous flow and the linear starting pressure gradient PTPG corresponding to the pressure gradient-seepage velocity relationship entering the near-linear stage according to the experimental curve, so as to obtain the threshold pressure gradient parameters of the target reservoir. Subsequently, based on the reservoir parameters, fluid parameters, and well pattern conditions of the target reservoir, establish a one-injection-one-production inter-well numerical model, input porosity, permeability, relative permeability curve, crude oil viscosity, and initial temperature and pressure parameters, and introduce the threshold pressure gradient model and stress sensitivity model to characterize the non-linear seepage and reservoir pore-permeability evolution characteristics of the low-permeability conglomerate reservoir, providing a model basis for the calculation of the inter-well pressure gradient field. Then, solve each grid unit in the inter-well area based on this inter-well numerical model to obtain the spatial distribution of the local pressure gradient value ∇p, and analyze its distribution law of "high at both ends and low in the middle" to provide a basis for subsequent threshold comparison and flow zone division. Finally, compare the local pressure gradient ∇p at each position between wells with the experimentally measured MTPG and PTPG. When MTPG ≤ ∇p < PTPG, it is divided into the critical mobile zone CMZ, and when ∇p ≥ PTPG, it is divided into the linear displacement zone LDZ, completing the inter-well flow zone division of the target low-permeability conglomerate reservoir and forming an inter-well flow zone division map.

[0058] The embodiment of this application provides the experimental verification results based on the above method, as Figures 6-9 shown: Figure 6This diagram illustrates the pressure gradient-flow velocity relationship and the identification of MTPG and PTPG, as provided in this specification. The horizontal axis represents the distance from the injection well (in meters), and the vertical axis represents the pressure gradient (TGP, in MPa / m). The curve exhibits a typical "high at both ends, low in the middle" distribution: the pressure gradient increases significantly near the injection well (around 0m) ​​and the production well (around 300m), forming two high-gradient zones, while the pressure gradient in the middle region between wells is generally lower. Based on the experimentally measured minimum starting pressure gradient (MTPG) and linear starting pressure gradient (PTPG), the region between wells is divided into three flow zones: the dark shaded area closest to the well is the linear movable zone, where the pressure gradient is ≥ PTPG, and the fluid is in a stable linear flow state; the light-colored transition zone adjacent to the linear movable zone is the movable zone, where the pressure gradient is between MTPG and PTPG, and the fluid is in a critical state of nonlinear flow; the low-gradient region in the middle between wells has a pressure gradient less than MTPG, making it difficult for the fluid to form effective flow, and is therefore a non-movable zone. This figure visually illustrates the pressure gradient differences at different locations between injection and production wells in low-permeability conglomerate reservoirs, clearly delineates the distribution range of different flow states between wells, and provides an intuitive basis for reservoir utilization evaluation and development optimization.

[0059] Figure 7 This specification provides a schematic diagram of the relative permeability curves of an oil-water system and an oil-gas system, including two sets of curves: (a) for the oil-water system and (b) for the oil-gas system. It intuitively reflects the variation of the relative permeability of each phase with saturation when multiphase fluids coexist in the reservoir.

[0060] In (a) the oil-water system, the horizontal axis represents water saturation Sw (%) and the vertical axis represents relative permeability (%). The square curve Krw represents the relative permeability of the water phase and the dotted curve Kro represents the relative permeability of the oil phase. As the water saturation Sw increases, the relative permeability of the water phase Krw rises rapidly from almost zero in the low water cut stage and then gradually decreases. The relative permeability of the oil phase Kro continues to decrease with the increase of water saturation. The two curves intersect at about 55% Sw, which reflects the characteristics of competitive flow between the oil and water phases.

[0061] In the (b) hydrocarbon system, the horizontal axis represents gas saturation Sg (%), and the vertical axis represents relative permeability (%). The block curve Krg represents the relative permeability of the gas phase, and the dotted curve Kro represents the relative permeability of the oil phase. As the gas saturation Sg increases, the relative permeability Krg of the gas phase shows a continuous decreasing trend, while the relative permeability Kro of the oil phase increases slowly with increasing gas saturation. The two curves intersect near Sg at approximately 50%, reflecting the dynamic change in the flow capacity of the hydrocarbon and gas phases in the reservoir pores with saturation. These two sets of curves provide crucial phase-permeability relationships for simulating multiphase flow in subsequent injection-production numerical models and are important foundational data for nonlinear flow characterization and inter-well flow zoning in low-permeability conglomerate reservoirs.

[0062] Figure 8 This document provides a three-dimensional schematic diagram and cross-sectional view of an inter-well numerical model. (a) is a three-dimensional solid view showing the overall structure of the inter-well numerical model for injection and production: the model is discretized using a Cartesian structured grid and presents a three-dimensional cuboid shape. The "PRO" marked on the left indicates the location of the production well, and the "INJ" marked on the right indicates the location of the injection well. The different gray-scale layers inside the model reflect the vertical heterogeneity of the reservoir. (b) is a two-dimensional cross-sectional schematic diagram of the model, including three grid cross-sections in different directions: the IJ-2DAreal plane, the IK-2DAreal plane, and the JK-2DAreal plane. It shows the grid division of the model in the plane and vertical directions. The IJ plane is the horizontal inter-well cross-section, and the IK and JK planes are the vertical cross-sections. It intuitively presents the spatial grid distribution and layered structure of the reservoir, providing a basic model framework for subsequent calculation of inter-well pressure gradient field and flow zoning.

[0063] Figure 9 This specification provides a numerical model of an injection-production well and a schematic diagram of the pressure gradient field and flow zoning between wells, comprising three parts (a), (b), and (c), which visually compare the distribution characteristics of pressure and pressure gradient between wells under different injection medium conditions.

[0064] (a) shows the formation pressure variation curves with well spacing under different injection media. The horizontal axis represents the well spacing (unit: m) and the vertical axis represents the formation pressure (unit: MPa). The curves correspond to four media: water, crude oil, CO2, and polymer solution. It can be seen that the formation pressure of all media decreases with the increase of well spacing. Moreover, the overall pressure levels of polymer solution and water are higher than those of crude oil and CO2, which reflects the influence of different media mobility on inter-well pressure transmission.

[0065] (b) The pressure gradient distribution curves between wells under different injection media are shown. The horizontal axis is the well spacing (unit: m) and the vertical axis is the pressure gradient (unit: MPa / m). The curves show a typical distribution characteristic of "high at both ends and low in the middle". The range of the linear displacement zone corresponding to different media is also marked: the width of the linear displacement zone of polymer solution is only 18.63 m, crude oil is 38.63 m, water is 59.9 m, and CO2 has the largest linear displacement zone width, reaching 111.85 m. This clearly reflects the rule that the higher the fluidity of the medium, the farther the linear displacement zone extends to the middle of the well.

[0066] (c) is a cloud map of formation pressure distribution under different injection media. The four sub-maps correspond to the pressure field distribution of water, crude oil, CO2 and polymer solution, respectively, which intuitively shows the pressure gradient distribution between injection and production wells. The color scale on the right shows the pressure value range, which is consistent with the curve data in (a) and (b). It fully presents the influence of different injection media on the pressure gradient field and flow zoning between wells in low-permeability conglomerate reservoirs, and provides a visual basis for the subsequent selection of development media.

[0067] The reservoir seepage region division device provided by the present invention is described below. The reservoir seepage region division device described below and the reservoir seepage region division method described above can be referred to in correspondence.

[0068] Figure 10 This is a schematic diagram of a reservoir seepage zone delineation device provided by the present invention. For example, please refer to [link to relevant documentation]. Figure 10 As shown, the reservoir seepage zone delineation device may include: The data acquisition module is used to acquire core samples and parameters of the target reservoir.

[0069] The pressure gradient threshold acquisition module is used to acquire, under the temperature and pressure conditions of the target reservoir, the minimum starting pressure gradient required for the target fluid to generate continuous seepage in the core sample, and the linear starting pressure gradient corresponding to the transition of the seepage velocity of the target fluid in the core sample from nonlinear to linear change.

[0070] The seepage simulation module is used to establish an initial injection-production numerical model based on the injection-production well network conditions of the target reservoir. A threshold pressure gradient model and a stress-sensitive model are embedded in the initial injection-production numerical model to obtain the injection-production numerical model. Based on the injection-production numerical model, the seepage of the target fluid is simulated to obtain the local pressure gradient field of the target reservoir. Among them, the threshold pressure gradient model constrains the seepage velocity of the target fluid according to the minimum starting pressure gradient and the linear starting pressure gradient, and the stress-sensitive model dynamically corrects the target reservoir parameters based on the pressure changes of the target reservoir caused by injection-production disturbances.

[0071] The region division module is used to divide the target reservoir into seepage regions based on the comparison results of the local pressure gradient field with the minimum starting pressure gradient and the linear starting pressure gradient.

[0072] Specific limitations regarding the reservoir seepage zone delineation device can be found in the above-mentioned limitations on reservoir seepage zone delineation, and will not be repeated here. Each module in the aforementioned reservoir seepage zone delineation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0073] This specification also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 The provided method for delineating seepage zones in oil reservoirs.

[0074] This instruction manual also provides Figure 11 The schematic diagram of the computer device shown is as follows: Figure 11 As shown, at the hardware level, this computer device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then executes it to achieve the above. Figure 1 The provided method for delineating seepage zones in oil reservoirs.

[0075] 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 methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0076] 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.

Claims

1. A method for delineating seepage zones in low-permeability oil reservoirs, characterized in that, include: Obtain core samples and parameters of the target reservoir; Under the target reservoir temperature and pressure conditions, obtain the minimum starting pressure gradient required for the target fluid to generate continuous seepage in the core sample, and the linear starting pressure gradient corresponding to the change of the seepage velocity of the target fluid in the core sample from nonlinear to linear. An initial injection-production numerical model is established based on the injection-production well network conditions of the target reservoir. A threshold pressure gradient model and a stress-sensitive model are embedded in the initial injection-production numerical model to obtain an injection-production numerical model. Based on the injection-production numerical model, the seepage of the target fluid is simulated to obtain the local pressure gradient field of the target reservoir. The threshold pressure gradient model constrains the seepage velocity of the target fluid in segments according to the minimum starting pressure gradient and the linear starting pressure gradient. The stress-sensitive model dynamically corrects the target reservoir parameters based on the pressure changes in the target reservoir caused by injection-production disturbances. Based on the comparison results of the local pressure gradient field with the minimum starting pressure gradient and the linear starting pressure gradient, the seepage region of the target reservoir is divided.

2. The method for delineating reservoir seepage zones as described in claim 1, characterized in that, The establishment of the initial injection-production numerical model based on the injection-production well network conditions of the target reservoir specifically includes: The target reservoir is discretized using a Cartesian coordinate system to generate a structured grid containing multiple grid cells; Based on the injection-production well network conditions of the target reservoir, the well location coordinates of water injection wells and oil production wells are configured in the structured grid to establish an initial injection-production numerical model containing at least one water injection well-oil production well pair.

3. The method for delineating reservoir seepage zones as described in claim 2, characterized in that, The threshold pressure gradient model applies piecewise constraints to the target fluid seepage velocity based on the minimum initiation pressure gradient and the linear initiation pressure gradient, specifically including: When the local pressure gradient of a grid cell is less than the minimum starting pressure gradient, the target fluid seepage velocity in the corresponding grid cell is set to zero. When the local pressure gradient of a grid cell is greater than or equal to the minimum starting pressure gradient and less than the linear starting pressure gradient, the target fluid seepage velocity in the corresponding grid cell is calculated using a nonlinear seepage equation. When the local pressure gradient of a grid cell is greater than or equal to the linear starting pressure gradient, Darcy's law is used to calculate the target fluid seepage velocity in the corresponding grid cell.

4. The method for delineating reservoir seepage zones as described in claim 3, characterized in that, The step of simulating the seepage of the target fluid based on the injection-production numerical model to obtain the local pressure gradient field of the target reservoir specifically includes: The injection-machining numerical model is solved iteratively in a numerical simulator; In each iteration step, the local pressure gradient of each grid cell is obtained based on the current pressure field distribution; The local pressure gradient is compared with the minimum starting pressure gradient and the linear starting pressure gradient, and the flow state of the target fluid in each grid cell and the target reservoir parameters of each grid cell are adjusted according to the comparison results. The adjusted target reservoir parameters are fed back into the injection-production numerical model for the next iteration until the pressure field distribution converges, thus obtaining the local pressure gradient field of the target reservoir.

5. The method for delineating reservoir seepage zones as described in claim 4, characterized in that, The stress-sensitive model dynamically corrects the target reservoir parameters based on the target reservoir pressure changes caused by injection-production disturbances, and uses elastoplastic evolution law to characterize the porosity change path with pore pressure, specifically including: During the pressure drop phase when the pore pressure decreases, when the pore pressure is higher than the preset plastic critical pressure, the porosity is controlled to decrease reversibly and linearly along the elastic compaction path as the pore pressure decreases; when the pore pressure decreases to or below the plastic critical pressure, the porosity is controlled to decrease irreversibly and nonlinearly along the plastic compaction path as the pore pressure decreases. During the pressure rise phase of pore pressure recovery, the porosity is controlled to only undergo partial elastic rebound, so that the porosity after the rebound is kept lower than the initial porosity state before the pressure drop, forming a hysteretic rebound path to obtain the dynamic porosity under the current iteration step. The dynamic porosity of the current iteration step is assigned to the corresponding mesh element for use in the next iteration step.

6. The method for delineating reservoir seepage zones as described in claim 2, characterized in that, The method of dividing the target reservoir into seepage zones based on the comparison results of the local pressure gradient field with the minimum initiation pressure gradient and the linear initiation pressure gradient specifically includes: When the local pressure gradient of a grid cell is less than the minimum starting pressure gradient, the corresponding grid cell is divided into an unused area. When the local pressure gradient of a grid cell is greater than or equal to the minimum starting pressure gradient and less than the linear starting pressure gradient, the corresponding grid cell is divided into a critical movable region. When the local pressure gradient of a grid cell is greater than or equal to the linear initiation pressure gradient, the corresponding grid cell is divided into a linear displacement region.

7. The method for delineating reservoir seepage zones as described in claim 1, characterized in that, The minimum initiation pressure gradient and the linear initiation pressure gradient were obtained through a combination of unsteady-state and steady-state displacement experiments; the displacement experiments included: The airtightness of the displacement experimental system was tested, and the core samples were sequentially subjected to vacuuming and formation water saturation treatment to establish the bound water state. A constant flow displacement was performed on the core sample under the bound water state, and the displacement inlet pressure, displacement outlet pressure, and volume data of the produced target fluid were continuously collected during the displacement process. Based on the collected inlet pressure, outlet pressure, and volume data of the target fluid produced, a curve showing the relationship between pressure gradient and seepage velocity is established. From the corresponding relationship curve, the inflection point pressure gradient corresponding to the establishment of continuous flow is identified as the minimum starting pressure gradient, and the inflection point pressure gradient corresponding to the transition from the nonlinear curved segment to the near-linear straight segment of the corresponding relationship curve is identified as the linear starting pressure gradient.

8. A device for delineating seepage zones in an oil reservoir, characterized in that, include: The data acquisition module is used to acquire core samples and parameters of the target reservoir. The pressure gradient threshold acquisition module is used to acquire, under the temperature and pressure conditions of the target reservoir, the minimum starting pressure gradient required for the target fluid to generate continuous seepage in the core sample, and the linear starting pressure gradient corresponding to the change of the seepage velocity of the target fluid in the core sample from nonlinear to linear. The seepage simulation module is used to establish an initial injection-production numerical model based on the injection-production well network conditions of the target reservoir, and to embed a threshold pressure gradient model and a stress-sensitive model into the initial injection-production numerical model to obtain an injection-production numerical model; based on the injection-production numerical model, target fluid seepage simulation is performed to obtain the local pressure gradient field of the target reservoir; wherein, the threshold pressure gradient model constrains the target fluid seepage velocity in segments according to the minimum starting pressure gradient and the linear starting pressure gradient, and the stress-sensitive model dynamically corrects the target reservoir parameters based on the target reservoir pressure changes caused by injection-production disturbances; The region division module is used to divide the target reservoir into seepage regions based on the comparison results of the local pressure gradient field with the minimum starting pressure gradient and the linear starting pressure gradient.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the reservoir seepage zone delineation method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the reservoir seepage zone delineation method as described in any one of claims 1 to 7.

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