Aqueous phase tracking simulation dominant channel identification method considering ultra-low permeability reservoir feature time variation

Abstract

The invention discloses a water phase tracking simulation dominant channel identification method considering ultra-low permeability reservoir characteristic time variation. The method is specifically implemented according to the following steps: step 1, establishing a change rule of permeability and oil-water phase permeability along with a displacement multiple; step 2, establishing a time-varying characterization model considering time-varying characteristics of different reservoirs according to the permeability and the change rule of oil-water relative permeability along with the displacement multiple obtained in the step 1; 3, establishing a source catchment water phase tracking model according to the time-varying characterization model which is established in the step 2 and considers the time-varying characteristics of the different reservoirs; and 4, tracking and identifying the ultra-low permeability fracture type dominant channel according to the source catchment water phase tracking model established in the step 3. According to the method, the problem that the identification accuracy of the dynamic dominant channel is influenced due to difficulty in determining the evolution direction of the dominant channel in the prior art is solved.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas development technology and relates to a method for identifying dominant channels in time-varying aqueous phase tracking simulation considering the characteristics of ultra-low permeability reservoirs. Background Technology

[0002] Currently, most oilfields in China have entered a high water-cut stage. After long-term waterflooding development, sand production and particle migration have led to significant changes in reservoir properties compared to the initial development stage. Simultaneously, due to the strong heterogeneity at the planar, inter-layer, and intra-layer levels, the oil-water flow patterns between injection and production wells are difficult to determine for complex injection-production systems, exacerbating the contradictions in the reservoir both horizontally and vertically. This places higher demands on clarifying the oil-water interaction patterns in the reservoir and achieving accurate identification of dominant channels. Conventional dominant channel identification methods do not consider the changes in particle migration and oil-water flow characteristics caused by strong scouring during long-term waterflooding. Furthermore, for injection-production units affected by multiple wells, existing identification methods struggle to determine the evolution direction of dominant channels, directly impacting the accuracy of dynamic dominant channel identification.

[0003] Waterflooding is a widely adopted and effective method for replenishing formation energy in conventional oilfields in China. However, studies have found that long-term waterflooding affects the microstructure and mineral content of the porous media system in reservoirs. Reservoir properties (such as permeability and wettability) change over time during development, which is particularly significant in the high water-cut stage of oilfields. Fractured reservoirs have complex pore-mass characteristics, including matrix and fracture systems. Time-varying properties significantly affect the oil-water migration patterns and development effectiveness in fractured reservoirs. For the complex injection-production system of low-permeability fractured reservoirs, the long-term scouring effect of injected water leads to a complex dynamic fracture distribution, forming complex seepage channels. Vertically, the difference in seepage resistance between high and low permeability layers increases, making it difficult to clarify the oil-water seepage patterns between injection and production wells. The injection-production relationship becomes increasingly complex, and the efficiency of planar injection-production adjustment continues to decline. This places higher demands on clarifying the oil-water interaction patterns in the reservoir and accurately identifying the dominant channels.

[0004] Conventional methods for identifying dominant channels mainly include well logging data and core well data. These methods require on-site testing or operations, have long operating cycles, and are costly. They also do not make full use of dynamic production data and do not consider changes in particle migration, fracture opening, and oil-water seepage characteristics caused by strong scouring during long-term waterflooding. Furthermore, for injection-production units affected by multiple wells, existing identification methods struggle to determine the evolution direction of dominant channels, which directly impacts the accuracy of dynamic dominant channel identification. Summary of the Invention

[0005] The purpose of this invention is to provide a time-varying aqueous phase tracking simulation method for identifying dominant channels that takes into account the characteristics of ultra-low permeability reservoirs. This method solves the problem in the prior art where the difficulty in determining the evolution direction of dominant channels affects the accuracy of dynamic dominant channel identification.

[0006] The technical solution adopted in this invention is a method for identifying dominant channels by simulating the time-varying characteristics of ultra-low permeability reservoirs using aqueous phase tracking, specifically implemented according to the following steps:

[0007] Step 1: Establish the variation law of permeability and oil-water phase penetration with displacement ratio;

[0008] Step 2: Based on the permeability obtained in Step 1 and the variation law of oil-water phase permeability with displacement ratio, establish a time-varying characterization model that considers the time-varying characteristics of different reservoirs.

[0009] Step 3: Based on the time-varying characterization model considering the time-varying characteristics of different reservoirs established in Step 2, establish a source-sink water facies tracing model;

[0010] Step 4: Based on the source-sink phase tracing model established in Step 3, track and identify the dominant channels of ultra-low permeability fractures.

[0011] The invention is further characterized in that:

[0012] Step 1 is as follows:

[0013] Step 1.1: Obtain typical core samples from the target block. In the same core section, select multiple rock samples with different fracture and matrix characteristics.

[0014] Step 1.2, determine the range of displacement ratios;

[0015] Step 1.3: Based on the rock samples selected in Step 1.1 and the displacement ratio range determined in Step 1.2, conduct long-term water phase displacement experiments and long-term water-driven oil displacement experiments respectively, and establish the variation law of permeability with displacement ratio and the variation law of oil-water phase permeability with displacement ratio respectively.

[0016] Step 1.2 specifically involves:

[0017] A numerical simulation model of the target block reservoir was established. Based on the established numerical simulation model, the cumulative water displacement at each time step of all grids was calculated, i.e., the cumulative water displacement. Then, the displacement ratio of all grids was calculated. The minimum and maximum displacement ratios of all grids were selected as the two extreme values ​​of the displacement ratio to determine the range of the displacement ratio during the long-term waterflood experiment. The formula for calculating the displacement ratio is as follows:

[0018]

[0019] In the formula: N is the displacement factor; Q wtLet m be the cumulative water volume passing through a certain grid. 3 V P The effective pore volume of the mesh is m 3 .

[0020] The variation of permeability with displacement factor in step 1.3 is as follows:

[0021] Based on the rock samples selected in step 1.1 and the displacement ratio range determined in step 1.2, long-term aqueous displacement experiments were conducted to establish a time-varying characterization model between the displacement ratio and the rate of change of permeability, specifically as follows:

[0022]

[0023] In the formula: S K K represents the permeability change factor, that is, the ratio of the permeability at a certain water drive factor to the initial permeability; i The initial permeability before displacement is represented by mD; K t mD represents the permeability after a certain displacement.

[0024] The process of establishing the variation law of oil-water phase penetration with displacement ratio in step 1.3 is as follows:

[0025] Based on the rock samples selected in step 1.1 and the displacement ratio range determined in step 1.2, a long-term water-drive oil displacement experiment was conducted. A time-varying characterization model between the displacement ratio and the relative permeability endpoint value was established. Then, by providing new endpoint values ​​for each grid, a new saturation function was generated using a linear transformation method. Finally, new relative permeability and capillary force curves were calculated.

[0026] The implementation process of the time-varying method for relative permeability curves is as follows:

[0027] Based on formula A time-varying regression function was established between the relative permeability of the aqueous phase and the water drive ratio; where...

[0028] K rw K represents the relative permeability of the aqueous phase. rwr S represents the relative permeability of the aqueous phase corresponding to the residual oil saturation. WCR To constrain water saturation, S OWCR S represents the residual oil saturation. w The water saturation is denoted by n, which is a constant determined based on the pore structure of the rock.

[0029] Step 2 is as follows:

[0030] Step 2.1: Establish a dual-medium grid system model, input the permeability and porosity models of the fracture and the matrix respectively, and calculate the initial permeability tensor and conductivity coefficient;

[0031] Step 2.2, perform model initialization, specifically: input the corresponding fluid PVT, oil-water interpenetration, capillary force and other models for fractures and matrix respectively, and establish the original pressure and saturation distribution of the reservoir by considering the pressure coefficient, capillary pressure at the oil-water interface and oil-water interpenetration in the equilibrium system, and gravity capillary force.

[0032] Step 2.3, flow simulation calculation: First, in the first time step of the simulation calculation, the flow rate and cumulative displacement ratio of each inner surface of each grid are calculated; starting from the second time step, by reading the flow rate, cumulative displacement ratio, and permeability model of each inner surface of the matrix system or fracture system to which each grid belongs and the grid system of the previous time step, the time-varying simulation switch is triggered, and the permeability and oil-water phase permeability time-varying model obtained in step 1.3 are called.

[0033] Step 2.4: Output the calculation results, realizing the output of the three-dimensional field map of water drive factor, dynamic permeability, dynamic conductivity and dynamic relative permeability for each grid at each time step.

[0034] In step 2.3, different fracture and matrix grid systems have different time-varying characteristics. By writing demand judgments into the model input file, different time-varying models of fracture and matrix are obtained. Specifically, for the permeability time-varying model, the dynamic conductivity of each grid at each time step is calculated based on the time-varying relationship between permeability and displacement ratio. For the oil-water phase permeability time-varying model, new phase permeability curves are updated based on the relationship between the bound water saturation, residual oil saturation, and relative permeability and displacement ratio of the water phase under residual oil obtained from the displacement experiment in step 1, i.e., endpoint calibration.

[0035] Step 3 specifically involves:

[0036] Step 3.1: Different calibrations are performed on the injection water and edge / bottom water of each injection well in the entire reservoir to distinguish and track the source of water drive and identify fracture scour areas;

[0037] Step 3.2: Based on the dual-medium grid system model established in Step 2, the time-varying numerical simulation model is used to discretize the governing equations using a finite difference scheme and then solve for the implicit pressure. Considering the time-varying characteristics of the matrix and cracks, the fluid velocity and cumulative displacement ratio distribution of each grid are calculated using Darcy's law.

[0038] Step 3.3 involves introducing a pseudo-tracer model to calibrate each injected water and performing real-time simulation calculations; then, at the production well, i.e., the production end, the water injection contribution of each injection well is observed.

[0039] The governing equations are:

[0040]

[0041] Where: K is the absolute permeability of the reservoir, mD; K ri Permeability of oil and water phases; μ i γ represents the viscosity of the oil and water phases, in mP·s; i Specific gravity of oil and water phases, N / m3; q vi S represents the flow rate of oil and water phases produced or injected into the reservoir per unit time and unit volume, expressed in kg / s. i φ represents the oil and water phase saturation; φ represents the reservoir porosity; B i is the volume coefficient of the oil-water phase.

[0042] Step 4 is as follows:

[0043] In step 3, the source-sink displacement direction of different water phases was determined. By analyzing the proportion of water from each injection end in the production end in real time and the variation characteristics of its derivative, the development direction of its dominant channel was clarified.

[0044] The beneficial effects of this invention are:

[0045] This invention obtains the time-varying evolution law of permeability and oil-water phase permeation under different matrix and fracture characteristics through long-term waterflood physical simulation experiments, and establishes corresponding time-varying characterization models; it also establishes a source-sink phase water phase pseudo-tracer tracking numerical simulation model, and introduces time-varying characterization models with different time-varying characteristics; by observing the time-varying dynamic evolution law of the production end during the simulation process, it identifies the dominant seepage zone of ultra-low permeability fracture type in waterflooding, which can effectively identify the development and evolution process of dominant seepage channels in ultra-low permeability reservoirs during long-term waterflooding, and provide a basis for subsequent oilfield management measures. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating the method for identifying fracture-type dominant channels in ultra-low permeability reservoirs that considers the injection and production characteristics of source and sink water phases, as presented in this invention.

[0047] Figure 2a This is a schematic diagram of the time-varying experimental regression analysis of the fracture and matrix permeability-displacement ratio regression relationship in Example 4 of the present invention;

[0048] Figure 2b A schematic diagram of the time-varying experimental regression analysis of the regression relationship between the oil-water phase permeation-displacement ratio of the crack and the matrix in Example 4 of the present invention;

[0049] Figure 3a This is a schematic diagram showing the water drive multiple range of <= 1PV in the reservoir numerical model of the present invention in Embodiment 4;

[0050] Figure 3b This is a schematic diagram showing the water drive multiple range > 1 PV in the reservoir numerical model of embodiment 4 of the present invention;

[0051] Figure 4aThis is a schematic diagram of the initial permeability field distribution in Embodiment 4 of the present invention;

[0052] Figure 4b This is a partial schematic diagram of the time-varying permeability field considering the time-varying permeability in Embodiment 4 of the present invention;

[0053] Figure 5a This is a schematic diagram showing the distribution of the relative permeability field of the original aqueous phase in Embodiment 4 of the present invention;

[0054] Figure 5b This is a schematic diagram of the time-varying relative permeability field distribution of the water phase after considering the time-varying permeability of the water phase in Embodiment 4 of the present invention.

[0055] Figure 6a This is a schematic diagram showing the distribution of the relative permeability field of the original oil phase in Example 4 of the present invention;

[0056] Figure 6b This is a schematic diagram of the distribution of the time-varying relative permeability field of the oil phase considering the time-varying permeability of the oil phase in Embodiment 4 of the present invention;

[0057] Figure 7a This is a schematic diagram of the water produced in the non-dominant channel in Embodiment 4 of the present invention;

[0058] Figure 7b This is a schematic diagram of the water production characteristics of the advantageous channel in Embodiment 4 of the present invention. Detailed Implementation

[0059] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0060] Example 1

[0061] This invention considers a time-varying aqueous phase tracking simulation method for identifying dominant channels in ultra-low permeability reservoirs, the process of which is as follows: Figure 1 As shown, the specific steps are as follows:

[0062] Step 1: Establish the variation law of permeability and oil-water phase penetration with displacement ratio, specifically as follows:

[0063] Step 1.1: Obtain typical core samples from the target block. In the same core section, select multiple rock samples with different fracture and matrix characteristics.

[0064] Step 1.2, determine the range of displacement multiples, specifically as follows:

[0065] A numerical simulation model of the target block reservoir was established. Based on the established numerical simulation model, the cumulative water displacement at each time step of all grids was calculated, i.e., the cumulative water displacement. Then, the displacement ratio of all grids was calculated. The minimum and maximum displacement ratios of all grids were selected as the two extreme values ​​of the displacement ratio to determine the range of the displacement ratio during the long-term waterflood experiment. The formula for calculating the displacement ratio is as follows:

[0066]

[0067] In the formula: N is the displacement factor; Q wt Let m be the cumulative water volume passing through a certain grid. 3 V P m is the effective pore volume of the mesh. 3 ;

[0068] Step 1.3: Based on the rock samples selected in Step 1.1 and the displacement ratio range determined in Step 1.2, conduct long-term water phase displacement experiments and long-term water-driven oil displacement experiments respectively, and establish the variation law of permeability with displacement ratio and the variation law of oil-water phase permeability with displacement ratio respectively.

[0069] The relationship between permeability and displacement factor is established as follows:

[0070] Based on the rock samples selected in step 1.1 and the displacement ratio range determined in step 1.2, long-term single-phase (aqueous phase) displacement experiments were conducted to establish a time-varying characterization model between the displacement ratio and the rate of change of permeability, specifically as follows:

[0071]

[0072] In the formula: S K K represents the permeability change factor, that is, the ratio of the permeability at a certain water drive factor to the initial permeability; i The initial permeability before displacement is represented by mD; K t mD represents the permeability after a certain displacement.

[0073] The process of establishing the variation law of oil-water phase penetration with displacement ratio is as follows:

[0074] Based on the rock samples selected in step 1.1 and the displacement ratio range determined in step 1.2, a long-term water-drive oil displacement experiment was conducted. A time-varying characterization model between the displacement ratio and the relative permeability endpoint value was established. Then, by providing new endpoint values ​​for each grid, a new saturation function was generated using a linear transformation method. Finally, new relative permeability and capillary force curves were calculated.

[0075] The implementation process of the time-varying method for relative permeability curves is as follows:

[0076] Based on formula A time-varying regression function was established between the relative permeability of the aqueous phase and the water drive ratio; where...

[0077] K rw K represents the relative permeability of the aqueous phase. rwr S represents the relative permeability of the aqueous phase corresponding to the residual oil saturation. WCR To constrain water saturation, S OWCR S represents the residual oil saturation. w The water saturation is denoted by n, which is a constant determined based on the pore structure of the rock.

[0078] Step 2: Based on the permeability and the variation of oil-water phase permeability with displacement ratio obtained in Step 1, establish a time-varying characterization model considering the time-varying characteristics of different reservoirs; specifically:

[0079] Step 2.1: Establish a dual-medium grid system model, input the permeability and porosity models of the fracture and the matrix respectively, and calculate the initial permeability tensor and conductivity coefficient;

[0080] Step 2.2, perform model initialization, specifically: input the corresponding fluid PVT, oil-water interpenetration, capillary force and other models for fractures and matrix respectively, and establish the original pressure and saturation distribution of the reservoir by considering the pressure coefficient, capillary pressure at the oil-water interface and oil-water interpenetration in the equilibrium system, and gravity capillary force.

[0081] Step 2.3, flow simulation calculation: First, in the first time step of the simulation calculation, the flow rate and cumulative displacement ratio of each inner surface of each grid are calculated; starting from the second time step, by reading the flow rate, cumulative displacement ratio, and permeability model of each inner surface of the matrix system or fracture system to which each grid belongs and the grid system of the previous time step, the time-varying simulation switch is triggered, and the permeability and oil-water phase permeability time-varying model obtained in step 1.3 are called.

[0082] Step 2.4: Output the calculation results, realizing the output of the three-dimensional field map of water drive factor, dynamic permeability, dynamic conductivity and dynamic relative permeability for each grid at each time step.

[0083] Step 3: Based on the time-varying characterization model considering the time-varying characteristics of different reservoirs established in Step 2, establish a source-sink facies tracing model; specifically:

[0084] Step 3.1: Different calibrations are performed on the injection water and edge / bottom water of each injection well in the entire reservoir to distinguish and track the source of water drive and identify fracture scour areas;

[0085] Step 3.2: Based on the dual-medium grid system model established in Step 2, the time-varying numerical simulation model is used to discretize the governing equations using a finite difference scheme and then solve for the implicit pressure. Considering the time-varying characteristics of the matrix and cracks, the fluid velocity and cumulative displacement ratio distribution of each grid are calculated using Darcy's law.

[0086] Step 3.3 involves introducing a pseudo-tracer model to calibrate each injected water and performing real-time simulation calculations; then, at the production well, i.e., the production end, the water injection contribution of each injection well is observed.

[0087] Step 4: Based on the source-sink phase tracing model established in Step 3, track and identify the dominant channels of ultra-low permeability fractures. Specifically:

[0088] In step 3, the source-sink displacement direction of different water phases was determined. By analyzing the proportion of water from each injection end in the production end in real time and the variation characteristics of its derivative, the development direction of its dominant channel was clarified.

[0089] Example 2

[0090] Based on Example 1, in step 2.3, different fracture and matrix grid systems have different time-varying characteristics. By writing demand judgments into the model input file, different time-varying models of fracture and matrix are obtained. Specifically, for the permeability time-varying model, the dynamic conductivity of each grid at each time step is calculated based on the time-varying relationship between permeability and displacement ratio. For the oil-water phase permeability time-varying model, new phase permeability curves are updated based on the relationship between the bound water saturation, residual oil saturation, and relative permeability and displacement ratio of the water phase under residual oil obtained from the displacement experiment in step 1, i.e., endpoint calibration.

[0091] Example 3

[0092] Based on Example 2, the governing equation in step 3.2 is:

[0093]

[0094] Where: K is the absolute permeability of the reservoir, mD; K ri Permeability of oil and water phases; μ i γ represents the viscosity of the oil and water phases, in mP·s; i Specific gravity of oil and water phases, N / m3; q vi S represents the flow rate of oil and water phases produced or injected into the reservoir per unit time and unit volume, expressed in kg / s. i φ represents the oil and water phase saturation; φ represents the reservoir porosity; B i is the volume coefficient of the oil-water phase.

[0095] Example 4

[0096] To verify the feasibility of this invention, a simulation was conducted, and the process is as follows:

[0097] (1) Analysis of the time-varying laws of reservoir properties

[0098] Typical core samples from the target block were obtained. Combined with field core data, several rock samples with different fracture and matrix characteristics were selected. The reservoir pore volume, total waterflooding volume, edge and bottom water extent, and preliminary numerical simulation results were considered, along with the feasibility of the experiment, to ultimately determine the waterflooding multiple range for the target block. (See attached figure.) Figure 3a and 3b As shown, Figure 3a For water drive multiples <= 1 PV, Figure 3b The range of water drive multiples is >1 PV.

[0099] Analysis of the experimental results revealed the time-varying relationships between core permeability and water drive ratio for both fractures and the matrix. (See attached diagram) Figure 2a The figure shows the time-varying relationship between cracks and matrix with water drive ratio; as shown in the attached figure. Figure 2b This is a time-varying graph showing the oil-water phase permeability curve under different displacement ratios. Similarly, the regression relationships between permeability, oil-water phase permeability, and displacement ratio can be obtained for all rock samples.

[0100] (2) Establishment of time-varying characterization models considering the time-varying characteristics of different reservoirs

[0101] A numerical simulation model considering permeability and time-varying oil-water interpenetration was established for the target block, along with a program for calculating the water displacement ratio for each grid cell. Based on the different time-varying relationships obtained from the water-drive experiment in step one, different time-varying models were established: a time-varying model was introduced in the first time step of the simulation; in each calculation step, the cumulative displacement ratio for each grid cell was first calculated, then the grid system (matrix or fracture) satisfied by each grid cell was determined, and then the corresponding permeability and oil-water time-varying models were called.

[0102] As attached Figures 4a-6b The image shows a comparison of the field distribution of permeability and oil-water phase permeability in a certain layer before and after considering the time-varying model, obtained through simulation calculations.

[0103] (3) Establishment of a source-sink water phase simulant tracing model

[0104] Based on the numerical simulation model established in step two that considers different time-varying characteristics, all water source and sink points (injection wells and bottom water) in the model are calibrated; based on Darcy's law, after completing the grid velocity solution, a tracking model of source and sink phases (water phases) is established with the injection-production connection unit as the research object.

[0105] (4) Establishment of a superior channel identification method based on source-sink phase tracing.

[0106] As attached Figure 7a , 7b The figures show the characteristic curves of the mass fraction and its derivative distribution of produced water under conditions of no dominant flow channel and presence of dominant flow channel between injection and production wells. It is clear from the figures that a single-peaked mass fraction derivative curve indicates the absence of a dominant flow channel, while a double-peaked curve indicates the presence of a dominant flow channel. The peak value of the first characteristic point reflects the seepage characteristics of the injected water along the first flow path (fracture-type dominant seepage channel). The peak value of the second characteristic point reflects the seepage characteristics of the injected water along the second flow path (matrix seepage channel).

Claims

1. A method for identifying dominant channels by simulating time-varying aqueous phase characteristics of ultra-low permeability reservoirs, characterized in that: The specific steps are as follows: Step 1: Establish the variation law of permeability and oil-water phase penetration with displacement ratio; Step 2: Based on the permeability obtained in Step 1 and the variation law of oil-water phase permeability with displacement ratio, establish a time-varying characterization model that considers the time-varying characteristics of different reservoirs. Step 3: Based on the time-varying characterization model considering the time-varying characteristics of different reservoirs established in Step 2, establish a source-sink water facies tracing model; Step 4: Based on the source-sink phase tracing model established in Step 3, track and identify the dominant channels of ultra-low permeability fractures.

2. The method for identifying dominant channels in time-varying aqueous phase tracking simulation considering the characteristics of ultra-low permeability reservoirs according to claim 1, characterized in that, Step 1 specifically involves: Step 1.1: Obtain typical core samples from the target block. In the same core section, select multiple rock samples with different fracture and matrix characteristics. Step 1.2, determine the range of displacement ratios; Step 1.3: Based on the rock samples selected in Step 1.1 and the displacement ratio range determined in Step 1.2, conduct long-term water phase displacement experiments and long-term water-driven oil displacement experiments respectively, and establish the variation law of permeability with displacement ratio and the variation law of oil-water phase permeability with displacement ratio respectively.

3. The method for identifying dominant channels in time-varying aqueous phase tracking simulation considering the characteristics of ultra-low permeability reservoirs according to claim 2, characterized in that, Step 1.2 specifically includes: A numerical simulation model of the target block reservoir is established. Based on the established numerical simulation model, the cumulative water displacement at each time step of all grids is calculated, i.e., the cumulative water displacement. Then, the displacement ratio of all grids is calculated. The minimum and maximum displacement ratios of all grids are selected as the two extreme values ​​of the displacement ratio to determine the range of the displacement ratio during the long-term waterflood experiment. The calculation formula of the displacement ratio is as follows: In the formula: N is the displacement factor; Q wt Let m be the cumulative water volume passing through a certain grid. 3 V P The effective pore volume of the mesh is m 3 .

4. The method for identifying dominant channels in time-varying aqueous phase tracking simulation considering ultra-low permeability reservoir characteristics according to claim 3, characterized in that, The variation of permeability with displacement factor in step 1.3 is as follows: Based on the rock samples selected in step 1.1 and the displacement ratio range determined in step 1.2, long-term aqueous displacement experiments were conducted to establish a time-varying characterization model between the displacement ratio and the rate of change of permeability, specifically as follows: In the formula: S K K represents the permeability change factor, that is, the ratio of the permeability at a certain water drive factor to the initial permeability; i The initial permeability before displacement is represented by mD; K t mD represents the permeability after a certain displacement.

5. The method for identifying dominant channels in water phase tracking simulation considering the characteristics of ultra-low permeability reservoirs according to claim 4, wherein the process of establishing the variation law of oil-water phase permeability with displacement ratio in step 1.3 is as follows: Based on the rock samples selected in step 1.1 and the displacement ratio range determined in step 1.2, a long-term water-drive oil displacement experiment was conducted. A time-varying characterization model between the displacement ratio and the relative permeability endpoint value was established. Then, by providing new endpoint values ​​for each grid, a new saturation function was generated using a linear transformation method. Finally, new relative permeability and capillary force curves were calculated. The implementation process of the time-varying method for the phase permeation curve is as follows: Based on formula A time-varying regression function was established between the relative permeability of the aqueous phase and the water drive ratio; where... K rw K represents the relative permeability of the aqueous phase. rwr S represents the relative permeability of the aqueous phase corresponding to the residual oil saturation. WCR To constrain water saturation, S OWCR S represents the residual oil saturation. w The water saturation is denoted by n, which is a constant determined based on the pore structure of the rock.

6. The method for identifying dominant channels in time-varying aqueous phase tracking simulation considering ultra-low permeability reservoir characteristics according to claim 5, characterized in that, Step 2 specifically involves: Step 2.1: Establish a dual-medium grid system model, input the permeability and porosity models of the fracture and the matrix respectively, and calculate the initial permeability tensor and conductivity coefficient; Step 2.2, perform model initialization, specifically: input the corresponding fluid PVT, oil-water interpenetration, capillary force and other models for fractures and matrix respectively, and establish the original pressure and saturation distribution of the reservoir by considering the pressure coefficient, capillary pressure at the oil-water interface and oil-water interpenetration in the equilibrium system, and gravity capillary force. Step 2.3, flow simulation calculation: First, in the first time step of the simulation calculation, the flow rate and cumulative displacement ratio of each inner surface of each grid are calculated; starting from the second time step, by reading the flow rate, cumulative displacement ratio, and permeability model of each inner surface of the matrix system or fracture system to which each grid belongs and the grid system of the previous time step, the time-varying simulation switch is triggered, and the permeability and oil-water phase permeability time-varying model obtained in step 1.3 are called. Step 2.4: Output the calculation results, realizing the output of the three-dimensional field map of water drive factor, dynamic permeability, dynamic conductivity and dynamic relative permeability for each grid at each time step.

7. The method for identifying dominant channels in time-varying aqueous phase tracking simulation considering ultra-low permeability reservoir characteristics according to claim 6, characterized in that, In step 2.3, different fracture and matrix grid systems have different time-varying characteristics. By writing demand judgments into the model input file, different time-varying models of fracture and matrix are obtained. Specifically, for the permeability time-varying model, the dynamic conductivity of each grid at each time step is calculated based on the time-varying relationship between permeability and displacement ratio. For the oil-water phase permeability time-varying model, new phase permeability curves are updated based on the relationship between the bound water saturation, residual oil saturation, and relative permeability and displacement ratio of the water phase under residual oil obtained from the displacement experiment in step 1, i.e., endpoint calibration.

8. The method for identifying dominant channels in time-varying aqueous phase tracking simulation considering ultra-low permeability reservoir characteristics according to claim 7, characterized in that, Step 3 specifically involves: Step 3.1: Different calibrations are performed on the injection water and edge / bottom water of each injection well in the entire reservoir to distinguish and track the source of water drive and identify fracture scour areas; Step 3.2: Based on the dual-medium grid system model established in Step 2, the time-varying numerical simulation model is used to discretize the governing equations using a finite difference scheme and then solve for the implicit pressure. Considering the time-varying characteristics of the matrix and cracks, the fluid velocity and cumulative displacement ratio distribution of each grid are calculated using Darcy's law. Step 3.3 involves introducing a pseudo-tracer model to calibrate each injected water and performing real-time simulation calculations; then, at the production well, i.e., the production end, the water injection contribution of each injection well is observed.

9. The method for identifying dominant channels in time-varying aqueous phase tracking simulation considering ultra-low permeability reservoir characteristics according to claim 8, characterized in that, The governing equation is: Where: K is the absolute permeability of the reservoir, mD; K ri Permeability of oil and water phases; μ i γ represents the viscosity of the oil and water phases, in mP·s; i Specific gravity of oil and water phases, N / m3; q vi S represents the flow rate of oil and water phases produced or injected into the reservoir per unit time and unit volume, expressed in kg / s. i φ represents the oil and water phase saturation; φ represents the reservoir porosity; B i is the volume coefficient of the oil-water phase.

10. The method for identifying dominant channels in time-varying aqueous phase tracking simulation considering ultra-low permeability reservoir characteristics according to claim 9, characterized in that, Step 4 specifically involves: In step 3, the source-sink displacement direction of different water phases was determined. By analyzing the proportion of water from each injection end in the production end in real time and the variation characteristics of its derivative, the development direction of its dominant channel was clarified.

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