Evaluation method for oil production potential of oil reservoir in ultra-high water cut period

Abstract

The invention relates to a method for evaluating the oil production potential of an oil reservoir in an ultra-high water cut period, and belongs to the technical field of oil development. According to the method for evaluating the oil production potential of the oil reservoir in the extra-high water cut period, various parameters influencing the oil production capacity and the time-varying property of the permeability in the production process are comprehensively considered, the virtual well method is introduced to calculate the oil production of each grid, and oil production potential distribution diagrams of different areas are generated; therefore, the oil production capacity of oil reservoirs in different areas in the later stage of water drive development is quantitatively and visually represented, potential tapping scheme deployment is quantitatively guided, and the method has important guiding significance for fine development of ultra-high water-cut oil reservoirs. According to the method, various factors influencing the oil production capacity of the oil reservoir are comprehensively considered, and the problem that oil production potential description of remaining oil is not accurate enough is solved; meanwhile, the influence of the accumulated water injection rate of the water injection development oil reservoir in the extra-high water cut period on the permeability is considered, the permeability of the digital-analog model is updated and corrected through the relation between the water passing multiple and the permeability, and the precision of quantitative characterization of the oil reservoir numerical model and the oil production potential is improved.

Description

Technical Field

[0001] The invention relates to an evaluation method for the oil production potential of an oil reservoir in an ultra-high water content period, belonging to the technical field of oil development. Background Art

[0002] After decades of development, most of my country's oil fields have entered a high water content stage (water content greater than 90%), forming ultra-high water content reservoirs. Long-term water flooding will cause rock particles to migrate and reservoir permeability to change. The law of permeability change is related to the amount of water flowing through. The greater the amount of water flowing through, the greater the permeability change. For ultra-high water content reservoirs, nearly 60% of the geological reserves are retained underground. How to understand the distribution of the remaining oil in the underground oil layer and accurately characterize its oil production potential on this basis is the basis for formulating the next step of potential tapping policy.

[0003] At present, the remaining oil potential is mainly characterized by the remaining oil saturation distribution map. The remaining oil distribution map is the distribution of oil saturation when the numerical model is calculated to the last time point. The use of the remaining oil distribution map to characterize the oil production potential only considers the size of the oil saturation, but the actual oil production is also affected by parameters such as viscosity, permeability, and pressure. Therefore, the remaining oil saturation distribution map can only qualitatively but not quantitatively characterize the oil production potential, and it is difficult to meet the needs of further tapping the potential in the ultra-high water cut period. In addition, after long-term water injection development, the reservoir pore structure that affects the oil production capacity of the sandstone reservoir has changed, and parameters such as porosity and permeability change with the change of water injection volume. There is a deviation between the permeability used in the numerical model in the late stage of water injection development and the actual permeability of the mine, and it cannot accurately predict and evaluate the oil production potential of the reservoir in the ultra-high water cut period. Summary of the invention

[0004] The purpose of the present invention is to provide an evaluation method for the oil production potential of an extremely high water-cut reservoir, which can solve the problem of low prediction accuracy and inability to quantitatively characterize the oil production potential of an extremely high water-cut reservoir at present.

[0005] In order to achieve the above objectives, the technical solution adopted by the method for evaluating the oil production potential of an ultra-high water-cut reservoir of the present invention is:

[0006] A method for evaluating the oil production potential of an oil reservoir in an ultra-high water cut period comprises the following steps:

[0007] (1) Based on core samples in the study area, high-power waterflooding experiments were used to construct the corresponding relationship between water flow rate and permeability change rate;

[0008] (2) Based on the three-dimensional geological model and production dynamic data of the study area, a gridded reservoir numerical model of the study area is established;

[0009] (3) calculating numerical simulation results in each time step based on the reservoir numerical model, and during the numerical simulation, iteratively updating the permeability used in the numerical simulation of each time step by using the corresponding relationship in step (1);

[0010] (4) Extract the evaluation index that affects the oil production capacity of the virtual well reservoir in each grid at the last time step, calculate and determine the oil production of each grid, draw the oil production contour map of the study area, and determine the oil production potential of different locations in the study area.

[0011] The method for evaluating the oil production potential of an oil reservoir in the ultra-high water-cut period of the present invention comprehensively considers various parameters that affect the oil production capacity during the production process and the time-varying nature of the permeability, introduces a virtual well method to calculate the oil production of each grid, and generates a distribution map of oil production potential in different regions, thereby quantitatively and intuitively characterizing the oil production capacity of oil reservoirs in different regions in the later stage of water flooding development, and quantitatively guiding the deployment of potential tapping plans, which has important guiding significance for the fine development of ultra-high water-cut oil reservoirs. The present invention comprehensively considers various factors that affect the oil production capacity of oil reservoirs, avoiding the problem of inaccurate description of oil production potential by residual oil; at the same time, the influence of the cumulative water injection volume on the permeability during the ultra-high water-cut period of water flooding development of oil reservoirs is considered, and the permeability of the numerical model is updated and corrected through the relationship between the water flow multiple and the permeability, thereby improving the accuracy of the numerical model of the oil reservoir and the quantitative characterization of the oil production potential.

[0012] Preferably, the method for constructing the corresponding relationship between the water flow multiple and the permeability change multiple is as follows: a high-power water flooding experiment is performed on the core sample, and based on the permeability obtained by the test under the water flow multiple, the corresponding relationship between the water flow multiple and the permeability change multiple is determined by data regression.

[0013] Preferably, the three-dimensional gridded reservoir numerical model of the study area is established by the following method: collecting core data, logging data, structural research results, reservoir research results and reservoir research results in the early stage of development of the study area, establishing a three-dimensional geological model of the study area, and establishing a gridded reservoir numerical model of the study area based on the three-dimensional geological model and production dynamic data.

[0014] Preferably, the method for iteratively updating the permeability used in the numerical simulation of each time step is as follows: after the numerical simulation in the i-th time step is completed, the cumulative water flow of each grid in the i-th time step is extracted, the quotient of the cumulative water flow and the effective pore volume is calculated to obtain the water flow multiple, and the permeability change multiple in the i-th time step is determined by using the corresponding relationship in step (1), and the product of the permeability change multiple in the i-th time step and the permeability used in the numerical simulation of the i-th time step is calculated to obtain the permeability of each grid used in the numerical simulation of the i+1-th time step.

[0015] Preferably, each grid is regarded as a virtual well, and evaluation indicators affecting oil production capacity in each grid are extracted in the numerical model field map module.

[0016] Preferably, the evaluation index includes oil phase viscosity u o , oil phase volume coefficient B o , oil phase relative permeability k ro , effective reservoir thickness h, geometric mean reservoir permeability Kt, equivalent supply radius r of virtual wells in the grid e , the radius r of the virtual well in the grid w , formation pressure P, bottom hole flow pressure P of virtual wells in the grid wf and the skin coefficient S of the virtual wells in the grid.

[0017] Preferably, the calculation formula of the effective reservoir thickness h is as follows:

[0018] h=D z ×NTG

[0019] Where D z is the grid step size in the Z direction in the numerical model, m; NTG is the net-to-gross ratio, dimensionless.

[0020] Preferably, the calculation formula of the geometric mean value of permeability Kt is as follows:

[0021]

[0022] In the formula, K x , K y are the reservoir permeabilities of each grid in the numerical model in the X and Y directions, mD, respectively.

[0023] Preferably, the equivalent supply radius r e The calculation formula is as follows:

[0024] r e =r w ×X

[0025] Where: r e is the equivalent supply radius of the virtual well in each grid, m; r w is the radius of the virtual well in each grid, m; X is the grid shape factor, dimensionless; the grid is a rectangular grid, and the calculation formula of the shape factor X is as follows:

[0026]

[0027] Where: X is the shape factor, dimensionless; r w is the radius of the virtual well in each grid, m; A is the oil leakage area.

[0028] Preferably, the oil production calculation formula of each grid is as follows:

[0029]

[0030] Where: Q is the daily oil production, m 3 / d; h is the effective thickness of the reservoir, m; Kt is the geometric mean of the reservoir permeability, μm 2 ;k ro is the effective permeability of the oil phase, dimensionless; u o is the oil phase viscosity, mPa·s; B o is the oil phase volume coefficient, m 3 / m 3 PP wf is the production pressure difference, P is the formation pressure, MPa, P wf is the bottom hole flow pressure of the virtual well in the grid, MPa; r e is the equivalent supply radius of the virtual well in the grid, m; r w is the radius of the virtual well in the grid, m; S is the skin coefficient of the virtual well in the grid, dimensionless. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic flow chart of a method for evaluating the oil production potential of an ultra-high water-cut reservoir according to an embodiment of the present invention;

[0032] Figure 2 It is a schematic diagram of a relationship curve between the water flow multiple (the water flow multiple is between 0 and 10) and the permeability change multiple established by using a data regression method in an embodiment of the present invention;

[0033] Figure 3 It is a schematic diagram of the relationship curve between the water flow multiple (the water flow multiple is between 10 and 500) and the permeability change multiple established by data regression in an embodiment of the present invention;

[0034] Figure 4 It is an oil production contour map in an embodiment of the present invention;

[0035] Figure 5 It is a schematic diagram of the comparison results of the oil production predicted by the method of the embodiment and the numerical simulation oil production capacity prediction method in the present invention. DETAILED DESCRIPTION

[0036] The method for evaluating the oil production potential of an extra-high water-cut oil reservoir of the present invention is an improved invention. Aiming at the insufficiency that the residual oil saturation cannot quantitatively characterize the oil production potential, and the problem that the parameters such as reservoir pore structure, porosity, and permeability that affect the oil production capacity of the oil reservoir change with the water injection volume during the long-term water injection development of the sandstone oil reservoir, resulting in deviations in the prediction of the oil production potential through numerical simulation, the present invention updates the permeability in the numerical model, reduces the deviation between the permeability used in the numerical simulation and the actual permeability of the mine, and considers various factors that affect the actual oil production capacity, introduces a virtual well method to extract relevant parameters in the numerical model, generates a quantitative and intuitive oil production potential distribution map, and thus improves the prediction accuracy of the oil production potential of the extra-high water-cut oil reservoir.

[0037] The method for evaluating the oil production potential of an ultra-high water-cut period oil reservoir of the present invention uses the core of a target block to conduct a high-multiple water drive experiment to obtain a functional relationship between the water flow rate and the permeability; and according to the core data, well logging data, structural research, reservoir research, oil reservoir research and other results of the initial development of the target block, a three-dimensional geological model of the oil reservoir of the target block is established, and a reservoir numerical model is established based on the three-dimensional geological model and production dynamic data; then, a reservoir numerical simulation is performed to extract the water flow rate of each grid in the model, and the permeability of each time step of the model is updated until the last time step based on the relationship between the water flow rate and the permeability; finally, according to various factors affecting the oil production capacity of the oil reservoir, a virtual well method is introduced in the numerical simulation calculation result of the oil reservoir in the last time step to calculate the oil production of each grid, generate an oil production potential distribution map, and quantitatively and intuitively describe the oil production potential of different blocks.

[0038] The technical solution of the present invention is described in detail below in conjunction with specific embodiments.

[0039] Example 1

[0040] The method for evaluating the oil production potential of an extremely high water-cut reservoir in this embodiment takes an extremely high water-cut reservoir as an example. Figure 1 As shown, the specific steps include:

[0041] (1) Core samples were collected in the study area, and high-power water flooding experiments were performed on the core samples. Based on the permeability obtained under the water flooding multiple, the corresponding relationship between the water flooding multiple and the permeability change multiple was determined by data regression. In this embodiment, during the high-power water flooding experiment, the water flooding multiple was equal to the ratio of the amount of water flowing through the pore volume (i.e., the amount of water flowing out of the core outlet) to the pore volume, in units of PV; the permeability change multiple was equal to the ratio of the current measured permeability to the initial permeability. The water flooding multiple used in the high-power water flooding experiment and the permeability and permeability change multiple obtained by the test are summarized in Table 1. Among them, the rock samples are saturated with formation water, and long-term water injection flushing experiments are carried out with injected water. Constant-speed water drive is used to collect permeability change rate data at different water flow multiples (during the water drive experiment, the permeability of the core at each moment can be calculated through the pressure and flow data monitored by the corresponding instruments, and the current permeability divided by the initial permeability is the change multiple); in the early stage of water injection development, the reservoir properties change greatly, and it is necessary to record the experimental data intensively. Before the injection multiple reaches 10PV, data is recorded every 1PV, and after reaching 10PV, data is recorded every 10PV; when the water injection multiple is greater than 100PV, experimental data is collected every 50PV.

[0042] Table 1 Water flow multiples used in high-power water flooding experiments and the permeabilities and permeability change multiples obtained from the tests

[0043]

[0044]

[0045] In this embodiment, the relationship curve between the water flow rate and the permeability change multiple is established by using the experimental data and the data regression method as shown in the figure. Figure 2 and Figure 3 As shown, the corresponding fitting equation is as follows:

[0046] M k =1.06×R 0.15 1.0 <R<10

[0047] M k =1.2077×lnR+0.303 10 <R<500

[0048] M k =1.2077×ln500+0.303 R>500

[0049] Where: M k is the permeability change multiple; R is the water flow multiple.

[0050] (2) Collect core data, logging data, structural research results, reservoir research results and oil reservoir research results in the early stage of development of the study area, establish a three-dimensional geological model of the study area, and establish a gridded reservoir numerical model of the study area based on the three-dimensional geological model and production dynamic data (production dynamic data include perforation depth, perforation time, historical oil production, water production, gas production, water injection, gas injection and production days). In this embodiment, the gridded reservoir numerical model established based on the three-dimensional geological model is a 76×81×76 black oil model, with a total number of grids of 467856, an effective number of grids of 34390, and the initial permeability of the model is greater than 50mD. Among them, 76×81×76 represents the number of grids in the X, Y, and Z directions, without units. The division is based on the length, width, and height of the research block divided by the length of each grid (the length of each grid is determined based on the actual situation of each block. If the grid length is too small, the calculation time will be long, and if the grid length is too large, the calculation accuracy will be low. The grid length is 10 to 30 meters, and the grid length in this embodiment is 21.4 meters). According to the grid length, the equivalent supply radius can be determined.

[0051] (3) Based on the gridded reservoir mathematical model established in step (2), numerical simulation is performed in the first time step, the cumulative water flow of each grid in the first time step is extracted, and the quotient of the cumulative water flow and the effective pore volume is calculated to obtain the water flow multiple; the calculation results show that in the first time step, the water flow multiple of each grid is between 1.0 and 10. In this embodiment, the reservoir starts production on September 1, 1985, and the period from September 1, 1985 to October 1, 1985 is the first time step.

[0052] (4) Based on the corresponding relationship between the water flow multiple and the permeability change multiple determined in step (1) and the water flow multiple of each grid in the first time step determined in step (3), the permeability change multiple of each grid in the first time step is determined; the product of the permeability change multiple and the permeability (i.e., the original permeability) used in the numerical simulation of the first time step is calculated to obtain the permeability used for each grid in the numerical simulation of the second time step.

[0053] (5) updating the permeability of each grid in the gridded numerical model from the permeability used in the numerical simulation of the first time step (i.e., the original permeability) to the permeability used in the numerical simulation of the second time step, performing numerical simulation in the second time step, extracting the cumulative water flow of each grid in the second time step, calculating the quotient of the cumulative water flow and the effective pore volume, and obtaining the water flow multiple; determining the permeability change multiple of each grid in the second time step based on the corresponding relationship between the water flow multiple and the permeability change multiple determined in step (1) and the water flow multiple of each grid in the second time step; calculating the product of the permeability change multiple and the original permeability, and obtaining the permeability of each grid at the end of the second time step.

[0054] (6) Repeat step (5) until the numerical simulation of all time steps is completed.

[0055] (7) Obtain the numerical simulation results at the last time step and the evaluation indicators of the virtual wells in each grid that affect the oil production capacity of the reservoir. The evaluation indicators include the oil phase viscosity u o , oil phase volume coefficient B o , oil phase relative permeability k ro , effective reservoir thickness h, geometric mean reservoir permeability Kt, equivalent supply radius r of virtual wells in the grid e , the radius r of the virtual well in the grid w , formation pressure P, bottom hole flow pressure P of virtual wells in the grid wf and the skin coefficient S of the virtual wells in the grid.

[0056] Among them, the oil phase viscosity u of each grid is o , oil phase volume coefficient B o , oil phase relative permeability k ro , the formation pressure P is obtained by extracting the numerical simulation results at the last time step; the bottom hole flow pressure P of the virtual well in the grid wf , the skin coefficient S is equal to the bottom hole flow pressure and skin coefficient set during actual production of the reservoir; the effective thickness of the reservoir h, the geometric mean value of the reservoir permeability Kt, and the equivalent supply radius r of the virtual well in the grid e Obtained by calculation; the calculation formula of the effective reservoir thickness h is as follows:

[0057] h=D z ×NTG

[0058] Where D z is the grid step size in the Z direction in the numerical model, m; NTG is the net-to-gross ratio, dimensionless. The net-to-gross ratio is the ratio of the effective thickness of the oil layer to the total thickness of the sandstone. It is usually calculated during the modeling process and output as a grid attribute, and can be directly calculated and applied in the numerical model.

[0059] The calculation formula of the geometric mean permeability Kt is as follows:

[0060]

[0061] In the formula, K x , K y are the reservoir permeabilities of each grid in the numerical model in the X and Y directions, mD, respectively.

[0062] The equivalent supply radius r of the virtual well in the grid e The calculation formula is as follows:

[0063] r e =r w ×X

[0064] Where: r e is the equivalent supply radius of the virtual well in each grid, m; r w is the radius of the virtual well in each grid, m; X is the grid shape factor, dimensionless; in this embodiment, each grid is a rectangular grid, and the virtual well is located at the center of the grid, so the calculation formula of the shape factor is as follows:

[0065]

[0066] Where: X is the shape factor, dimensionless; r w is the radius of the virtual well in each grid, m; A is the oil drainage area, which is equal to the area of ​​the rectangular grid in this embodiment;

[0067] In this embodiment, the virtual well radius is the wellbore radius, and the general value of the virtual well radius in the numerical simulation software is referred to; the equivalent supply radius is equal to the product of the virtual well radius and the shape factor, and the shape factor is related to the shape of the oil leakage area. The grid in the numerical model in this embodiment is a rectangular grid, and its shape factor is 0.571. The effective thickness h of the reservoir of the virtual well WELL-1 is 4.05m, the geometric mean value Kt of the reservoir permeability is 295.7mD, and the relative permeability k of the oil phase is 2. ro is 0.19, oil phase viscosity u o is 34mPa·s, and the oil phase volume coefficient B o 1.16m 3 / m 3 , the equivalent supply radius r of the virtual well e is 12.23 m, the production pressure difference is 10 MPa, and the radius of the virtual well is r w The surface thickness of the virtual well is 0.1524m, and the skin coefficient S of the virtual well is 0. The skin coefficient is the degree of contamination of a well, and 0 means no contamination. It can be judged according to the actual conditions around the well. Since the skin coefficient is difficult to determine in the actual production process, the skin coefficient is usually set to 0 in the current numerical simulation process.

[0068] (8) Based on the evaluation index obtained in step (7), the oil production of each grid is calculated and determined; the oil production calculation formula is as follows:

[0069]

[0070] Where: Q is the daily oil production, m 3 / d; h is the effective thickness of the reservoir, m; Kt is the geometric mean of the reservoir permeability, μm 2 ;k ro is the effective permeability of the oil phase, dimensionless; u o is the oil phase viscosity, mPa·s; B o is the oil phase volume coefficient, m 3 / m 3 PP wf is the production pressure difference, P is the formation pressure, MPa, P wf is the bottom hole flowing pressure, MPa; r e is the equivalent supply radius of the virtual well in the grid, m; r w is the radius of the virtual well in the grid, m; S is the skin coefficient of the virtual well in the grid, dimensionless.

[0071] (9) Based on the oil production of each grid determined in step (8), draw the following Figure 4 The oil production contour map of the study area shown in the figure determines the oil production potential of different locations in the study area. The oil production potential is evaluated through the oil production contour map. The greater the oil production in the contour map, the greater the oil production potential. The oil production contour map can be used to obtain the oil production potential of different areas, and then quantitatively guide the deployment of potential tapping plans.

[0072] In order to verify the accuracy, the numerical simulation prediction of production is used as the standard, and the oil production obtained by the method of the embodiment is compared with the standard value. Among them, the numerical simulation prediction of production is a common method for predicting the actual oil production of a block. The current oil field production process usually uses this method to predict the actual oil production in the next few decades, so this method is selected as the standard method. The numerical simulation prediction of production is determined by the numerical simulation oil production capacity prediction method. The specific steps of the numerical simulation oil production capacity prediction method are as follows: deploy a production well at a certain grid position, the perforation section is the depth range of the grid, the working system of the production well is constant pressure difference production, the well is not polluted, and the skin coefficient is zero; after setting the relevant production system of the well, running the numerical model can calculate the actual oil production of the well at the grid.

[0073] The method of the embodiment and the numerical simulation oil production capacity prediction method are used to predict the daily oil production of 10 virtual wells WELL-1, WELL-2, WELL-3, WELL-4, WELL-5, WELL-6, WELL-7, WELL-8, WELL-9, and WELL10 in several grids in the study area. The oil production obtained by the numerical simulation oil production capacity prediction method is expressed as the numerical simulation predicted output, and the oil production obtained by the method of the embodiment is expressed as the prediction result of the oil production potential evaluation method. The two prediction results are summarized in Figure 5 In. By Figure 5 It can be seen that the prediction result of the method of the embodiment is basically equal to the output prediction result of the numerical simulation method, and the relative errors of the two are small, and the relative errors are between 2.64% and 2.93%, respectively, indicating that the prediction result of the method of the present invention is reliable and accurate.

Claims

1. A method for evaluating the oil production potential of an ultra-high water-cut reservoir, characterized in that: The following steps are involved: (1) Based on core samples in the study area, high-power waterflooding experiments were used to construct the corresponding relationship between water flow rate and permeability change rate; (2) Based on the three-dimensional geological model and production dynamic data of the study area, a gridded reservoir numerical model of the study area is established; (3) calculating numerical simulation results in each time step based on the reservoir numerical model, and during the numerical simulation, iteratively updating the permeability used in the numerical simulation of each time step by using the corresponding relationship in step (1); (4) Extract the evaluation index that affects the oil production capacity of the virtual well reservoir in each grid at the last time step, calculate and determine the oil production of each grid, draw the oil production contour map of the study area, and determine the oil production potential of different locations in the study area.

2. The method for evaluating the oil production potential of an ultra-high water-cut reservoir according to claim 1, characterized in that: The method for constructing the corresponding relationship between the water flow multiple and the permeability change multiple is as follows: a high-power water flooding experiment is performed on the core sample, and based on the permeability obtained under the water flow multiple, the corresponding relationship between the water flow multiple and the permeability change multiple is determined by data regression.

3. The method for evaluating the oil production potential of an ultra-high water-cut reservoir according to claim 1, characterized in that: The three-dimensional gridded reservoir numerical model of the study area was established by the following method: collecting core data, logging data, structural research results, reservoir research results and reservoir research results in the early stage of development of the study area, establishing a three-dimensional geological model of the study area, and establishing a gridded reservoir numerical model of the study area based on the three-dimensional geological model and production dynamic data.

4. The method for evaluating the oil production potential of an ultra-high water-cut reservoir according to claim 1, characterized in that: The method for iteratively updating the permeability used in the numerical simulation of each time step is as follows: after the numerical simulation in the i-th time step is completed, the cumulative water flow of each grid in the i-th time step is extracted, the quotient of the cumulative water flow and the effective pore volume is calculated to obtain the water flow multiple, and the permeability change multiple in the i-th time step is determined by using the corresponding relationship in step (1), and the product of the permeability change multiple in the i-th time step and the permeability used in the numerical simulation of the i-th time step is calculated to obtain the permeability of each grid used in the numerical simulation of the i+1-th time step.

5. The method for evaluating the oil production potential of an ultra-high water-cut reservoir according to claim 1, characterized in that: Each grid is regarded as a virtual well, and the evaluation indexes affecting the oil production capacity in each grid are extracted in the numerical model field map module.

6. The method for evaluating the oil production potential of an ultra-high water-cut reservoir according to any one of claims 1 to 5, characterized in that: The evaluation index includes oil phase viscosity u o , oil phase volume coefficient B o , oil phase relative permeability k ro , effective reservoir thickness h, geometric mean reservoir permeability Kt, equivalent supply radius r of virtual wells in the grid e , the radius r of the virtual well in the grid w , formation pressure P, bottom hole flow pressure P of virtual wells in the grid wf and the skin coefficient S of the virtual wells in the grid.

7. The method for evaluating the oil production potential of an ultra-high water-cut reservoir according to claim 6, characterized in that: The calculation formula of reservoir effective thickness h is as follows: h=D z ×NTG Where D z is the grid step size in the Z direction in the numerical model, m; NTG is the net-to-gross ratio, dimensionless.

8. The method for evaluating the oil production potential of an ultra-high water-cut reservoir according to claim 6, characterized in that: The calculation formula of the geometric mean permeability Kt is as follows: In the formula, K x , K y are the reservoir permeabilities of each grid in the numerical model in the X and Y directions, mD, respectively.

9. The method for evaluating the oil production potential of an ultra-high water-cut reservoir according to claim 6, characterized in that: Equivalent supply radius r e The calculation formula is as follows: r e ＝r w ×X Where: r e is the equivalent supply radius of the virtual well in each grid, m; r w is the radius of the virtual well in each grid, m; X is the grid shape factor, dimensionless; the grid is a rectangular grid, and the calculation formula of the shape factor X is as follows: Where: X is the shape factor, dimensionless; r w is the radius of the virtual well in each grid, m; A is the oil leakage area.

10. The method for evaluating the oil production potential of an ultra-high water-cut reservoir according to any one of claims 1 to 5, characterized in that: The calculation formula for the oil production of each grid is as follows: Where: Q is the daily oil production, m 3 / d; h is the effective thickness of the reservoir, m; Kt is the geometric mean of the reservoir permeability, μm 2 ;k ro is the effective permeability of the oil phase, dimensionless; u o is the oil phase viscosity, mPa·s; B o is the oil phase volume coefficient, m 3 / m 3 PP wf is the production pressure difference, P is the formation pressure, MPa, P wf is the bottom hole flow pressure of the virtual well in the grid, MPa; r e is the equivalent supply radius of the virtual well in the grid, m; r w is the radius of the virtual well in the grid, m; S is the skin coefficient of the virtual well in the grid, dimensionless.

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