Method for evaluating oil production potential of reservoir in extra-high water cut stage

Abstract

The present application relates to a kind of extra-high water cut reservoir oil production potential evaluation method, belong to the technical field of oil development.The extra-high water cut reservoir oil production potential evaluation method of the present application comprehensively considers various parameters influencing oil production capacity in production process and the time-varying nature of permeability, introduces virtual well method to calculate the oil production of each grid, generates different regional oil production potential distribution map, thereby quantitatively and intuitively characterizes the oil production capacity of different regions of reservoir after water drive development late stage, quantitatively guides potential tapping scheme deployment, and has important guiding significance for fine development of extra-high water cut reservoir.The present application comprehensively considers various factors influencing oil production capacity of reservoir, avoids the problem that remaining oil is not accurate enough in describing oil production potential;At the same time, considering the influence of cumulative water injection amount on permeability in extra-high water cut stage of water injection development reservoir, the permeability of logarithmic module model is updated and corrected by the relationship between water passing multiple and permeability, which improves the accuracy of numerical model of reservoir and quantitative characterization of oil production potential.

Description

TECHNICAL FIELD

[0001] The present application relates to a method for evaluating oil production potential of an extra-high water cut reservoir, and belongs to the technical field of oil development. BACKGROUND

[0002] Most of the oilfields in China have entered the high water cut stage (water cut is greater than 90%) after decades of development, forming an extra-high water cut reservoir. The long-term water flooding process will cause the migration of rock particles, and the change of reservoir permeability. The change rule of permeability is related to the water passing amount, and the greater the water passing amount, the greater the change of permeability. For the extra-high water cut reservoir, nearly 60% of the geological reserves are left in the underground. How to understand the distribution of remaining oil in the underground oil layer and accurately characterize the oil production potential on this basis is the basis for formulating the next step of tapping potential policy.

[0003] At present, the remaining oil saturation distribution map is mainly used to characterize the remaining oil potential. The remaining oil distribution map is the distribution of oil saturation at the last time point of numerical model operation. The remaining oil distribution map is used to characterize the oil production potential, which only considers the size of oil saturation. However, 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 characterize the oil production potential and cannot meet the further tapping demand of the extra-high water cut stage. In addition, after long-term water injection development of sandstone reservoir, the reservoir pore structure affecting the oil production capacity of the reservoir changes, and the parameters such as porosity and permeability change with the change of water injection amount. There is a deviation between the permeability used by the numerical model in the late stage of water injection development and the actual permeability in the field, which cannot accurately predict and evaluate the oil production potential of the extra-high water cut reservoir. SUMMARY

[0004] The purpose of the present application is to provide a method for evaluating the oil production potential of an extra-high water cut reservoir, which can solve the problems of low prediction accuracy and inability to quantitatively characterize the oil production potential of the extra-high water cut reservoir.

[0005] In order to achieve the above purpose, the technical scheme adopted by the method for evaluating the oil production potential of an extra-high water cut reservoir of the present application is as follows:

[0006] A method for evaluating the oil production potential of an extra-high water cut reservoir, comprising the following steps:

[0007] (1) Based on the core samples in the study area, a corresponding relationship between the water passing multiple and the permeability change multiple is constructed by using high-multiple water drive experiment;

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

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

[0010] (4) extracting the evaluation index affecting the virtual well reservoir oil production capacity in each grid at the last time step, calculating and determining the oil production of each grid, drawing the oil production contour map of the study area, and determining the oil production potential of different positions in the study area.

[0011] The evaluation method of the reservoir oil production potential in the ultra-high water cut stage of the application comprehensively considers various parameters affecting the oil production capacity in the production process and the time variation of the permeability, introduces the virtual well method to calculate the oil production of each grid, and generates a different regional oil production potential distribution map, thereby quantitatively and intuitively representing the oil production capacity of the reservoir in different regions in the late water drive development stage, quantitatively guiding the potential tapping scheme deployment, and having important guiding significance for the fine development of the ultra-high water cut reservoir. The application comprehensively considers various factors affecting the oil production capacity of the reservoir, avoids the problem that the residual oil is not accurate enough in describing the oil production potential; meanwhile, the influence of the cumulative water injection amount on the permeability in the ultra-high water cut stage of the water injection development reservoir is considered, the permeability of the logarithmic model is updated and corrected through the relationship between the water injection multiple and the permeability, and the accuracy of the reservoir numerical model and the quantitative representation of the oil production potential is improved.

[0012] Preferably, the method for constructing the corresponding relationship between the water injection multiple and the permeability change multiple is as follows: a high-multiple water drive experiment is performed on the core sample, the permeability obtained by testing under the water injection multiple is determined, and the corresponding relationship between the water injection 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 the core data, logging data, structure research results, reservoir research results and reservoir research results of the study area in the early development stage, establishing a three-dimensional geological model of the study area, and based on the three-dimensional geological model and production dynamic data, establishing a gridded reservoir numerical model of the study area.

[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 injection amount of each grid in the i th time step is extracted, the quotient of the cumulative water injection amount and the effective pore volume is calculated to obtain the water injection multiple, the permeability change multiple in the i th time step is determined by using the corresponding relationship in step (1), 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 taken as a virtual well, and evaluation indexes affecting oil production capacity in each grid are extracted in the numerical model field map module.

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

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

[0018] h = D z × NTG

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

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

[0021]

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

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

[0024] r e = r w × X

[0025] In the formula, 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 a grid shape factor, dimensionless; and the grid is a rectangular grid, and the calculation formula of the shape factor X is as follows:

[0026]

[0027] In the formula, X is a shape factor, dimensionless; r w is the radius of the virtual well in each grid, m; and A is a drainage area.

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

[0029]

[0030] In the formula: Q is the daily oil production, m 3 / d; h is the effective reservoir thickness, m; Kt is the geometric mean of reservoir permeability, μm 2 ;k ro The effective permeability of the oil phase is dimensionless; u o B is the oil phase viscosity, mPa·s; o m is the oil phase volume coefficient. 3 / m 3 ;PP wf The production pressure difference is P, where P is the formation pressure (MPa). wf r represents the bottom-hole flowing pressure of the virtual well within the grid, in MPa. e Let m be the equivalent supply radius of the virtual well within the grid; r be the value of the radius. w Let be the radius of the virtual well within the grid, in meters; and S be the skin coefficient of the virtual well within the grid, dimensionless. Attached Figure Description

[0031] Figure 1 This is a flowchart illustrating the method for evaluating the oil production potential of reservoirs in the ultra-high water-cut stage according to an embodiment of the present invention.

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

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

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

[0035] Figure 5 This diagram illustrates a comparison of oil production predicted by the method used in the embodiments of this invention and the numerical simulation method for predicting oil production capacity. Detailed Implementation

[0036] The evaluation method for oil production potential of the ultra-high water cut reservoir is an improved invention. In view of the problems that the residual oil saturation cannot quantitatively represent the oil production potential, and in the long-term water injection development process of the sandstone reservoir, the reservoir pore structure, porosity, permeability and other parameters affecting the oil production capacity of the reservoir change with the water injection amount, and the numerical simulation for predicting the oil production potential has a deviation, the permeability in the numerical model is updated, the deviation between the permeability used in the numerical simulation and the actual permeability in the field is reduced, and various factors affecting the actual oil production capacity are considered. The virtual well method is introduced to extract related parameters in the numerical model, and a quantitative and intuitive oil production potential distribution map is generated, so as to improve the prediction accuracy of the oil production potential of the ultra-high water cut reservoir.

[0037] The evaluation method for oil production potential of the ultra-high water cut reservoir, uses the target block core to carry out high-multiple water drive experiment, obtains the functional relationship between the water passing multiple and the permeability, and according to the core data, logging data, structure research, reservoir research, reservoir research and other results of the target block in the early development stage, establishes a three-dimensional geological model of the target block reservoir, and establishes a reservoir numerical simulation model according to the three-dimensional geological model and the production dynamic data. Then, the reservoir numerical simulation is carried out, the water passing multiple of each grid in the model is extracted, the permeability of each time step in the model is updated based on the relationship between the water passing multiple and the permeability until the last time step, and finally, according to each factor affecting the oil production capacity of the reservoir, the virtual well method is introduced to calculate the oil production of each grid in the last time step of the reservoir numerical simulation calculation result, and the oil production potential distribution map is generated, which quantitatively and intuitively describes the oil production potential of different blocks.

[0038] The technical solutions of the present application will be described in detail in combination with specific embodiments.

[0039] Embodiment 1

[0040] The evaluation method for oil production potential of the ultra-high water cut reservoir in this embodiment takes a certain ultra-high water cut reservoir as an example, as shown in Figure 1 The specific steps include the following steps:

[0041] (1) Collecting core samples in the research area, conducting high multiple water flooding experiment on the core samples, determining the corresponding relationship between the water multiple and the permeability change multiple through data regression according to the permeability obtained by testing under the water multiple. In this embodiment, the water multiple in the high multiple water flooding experiment is equal to the ratio of the water volume flowing through the pore volume (i.e. the water volume flowing out of the core outlet end) to the pore volume, in units of PV; the permeability change multiple is equal to the ratio of the current measured permeability to the initial permeability. The water multiple used in the high multiple water flooding experiment and the permeability and the permeability change multiple obtained by testing are summarized in Table 1. Among them, the rock sample is saturated with formation water, a long-term water injection flushing experiment is conducted with injected water, constant speed water flooding is conducted, and the permeability change rate data under different water multiples are collected (in the water flooding experiment process, the pressure and flow data monitored by the corresponding instrument can be used to calculate the permeability of the core at each time, and the current permeability divided by the initial permeability is the change multiple); in the early stage of water injection development, the reservoir property changes greatly, and the experimental data needs to be recorded intensively, data is recorded once every 1 PV before the injection multiple reaches 10 PV, and data is recorded once every 10 PV after the injection multiple reaches 10 PV; when the water injection multiple is greater than 100 PV, experimental data is collected every 50 PV.

[0042] Table 1 Water multiple used in high multiple water flooding experiment and permeability and permeability change multiple obtained by testing

[0043]

[0044]

[0045] In this embodiment, the relationship curve between the water multiple and the permeability change multiple established by using the experimental data through data regression is shown in the schematic diagram as shown in Figure 2 and Figure 3 The corresponding fitting equation is as follows:

[0046] M k = 1.06 × R 0.15 1.0 < R < 10

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

[0048] M k = 1.2077 × ln 500 + 0.303 R > 500

[0049] In the formula, M k is the permeability change multiple; R is the water multiple.

[0050] (2) Collect the core data, logging data, structure research results, reservoir research results and oil reservoir research results at the early stage of development of the research area, establish a three-dimensional geological model of the research area, and based on the three-dimensional geological model and production dynamic data (the production dynamic data includes the perforation depth, perforation time, historical oil production, water production, gas production, water injection amount, gas injection amount and production days), establish a gridded reservoir numerical model of the research area. In this embodiment, the gridded reservoir numerical model based on the three-dimensional geological model is a black oil model with 76x81x76, the total number of grids is 467856, the effective number of grids is 34390, and the initial permeability of the model is greater than 50 mD. Among them, 76x81x76 represents the number of grids in the X, Y and Z directions, which is unitless, and the division basis is the numerical value of the length, width and height of the research block divided by the length of each grid (the length of each grid is determined according to the actual situation of each block, the length of each grid is too small, the calculation time is long, the length of each grid is too large, the calculation precision is not high, the length of each grid is 10-30m, and the length of each grid in this embodiment is 21.4m). 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 carried out in the first time step, the cumulative water passing amount of each grid in the first time step is extracted, the quotient of the cumulative water passing amount and the effective pore volume is calculated to obtain the water passing multiple; the calculation result shows that the water passing multiple of each grid in the first time step is between 1.0-10. 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 passing multiple and the permeability change multiple determined in step (1) and the water passing 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 used in the first time step numerical simulation (i.e. the original permeability) is calculated to obtain the permeability used in the second time step numerical simulation.

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

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

[0055] (7) obtaining the evaluation indexes of the numerical simulation result at the last time step and the virtual well in each grid affecting the oil reservoir oil production capacity, the evaluation indexes including the oil phase viscosity u o , the oil phase volume coefficient B o , the oil phase relative permeability k ro , the reservoir effective thickness h, the reservoir permeability geometric mean Kt, the equivalent supply radius r e of the virtual well in the grid, the radius r w of the virtual well in the grid, the formation pressure P, the bottom hole flowing pressure P wf of the virtual well in the grid and the skin factor S of the virtual well in the grid.

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

[0057] h = D z × NTG

[0058] wherein D z is the grid step length in the Z direction in the numerical model, m; and 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, which is usually calculated and output as a kind of grid attribute during the modeling process and can be directly calculated and applied in the numerical model.

[0059] The formula for calculating the geometric average of permeability Kt is as follows:

[0060]

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

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

[0063] r e = r w × X

[0064] In the formula, 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; and 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 formula for calculating the shape factor is as follows:

[0065]

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

[0067] In this embodiment, the radius of the virtual well is the wellbore radius, and the general numerical 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 drainage area; in the numerical model in this embodiment, the grid is a rectangular grid, and its shape factor is 0.571. The reservoir effective thickness h of the virtual well WELL-1 is 4.05 m, the geometric average of reservoir permeability Kt is 295.7 mD, the relative permeability of the oil phase k ro is 0.19, the viscosity of the oil phase u o is 34 mPa·s, the volume coefficient of the oil phase B o is 1.16 m 3 / m 3 , the equivalent supply radius r e of the virtual well is 12.23 m, the production pressure difference is 10 MPa, the radius r w of the virtual well is 0.1524 m, and the skin factor S of the virtual well is 0. The skin factor is a measure of the degree of pollution of a well, and 0 indicates that the well is not polluted; the skin factor can be determined according to the actual wellbore conditions. Since it is difficult to determine the skin factor in the actual production process, the skin factor 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; the oil production calculation formula is as follows:

[0069]

[0070] In the formula: 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 viscosity of the oil phase, mPa·s; B o is the volume coefficient of the oil phase, m 3 / m 3 ; P-P 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 factor of the virtual well in the grid, dimensionless.

[0071] (9) According to the oil production of each grid calculated and determined in step (8), the oil production contour map of the study area as shown in Figure 4 is drawn to determine the oil production potential at different positions in the study area. The oil production potential is evaluated through the oil production contour map, and the position with greater oil production in the contour map has greater oil production potential. Through the oil production contour map, the oil production potential of different regions can be obtained, thereby quantitatively guiding the deployment of potential tapping schemes.

[0072] To verify the accuracy, the oil production obtained by the method of the embodiment is compared with the standard value, wherein the numerical simulation prediction production is a commonly used method for predicting the actual oil production of a certain block at present, and the current oilfield production process usually uses this method to predict the actual oil production in the future for decades, so this method is selected as the standard method. The numerical simulation prediction production is determined by a numerical simulation oil production capacity prediction method, and the specific steps of the numerical simulation oil production capacity prediction method are as follows: a production well is deployed at a certain grid position, the perforated section is the depth range of the grid, the production well has a constant pressure difference production system, the well is not contaminated, and the skin factor is zero; after setting the production system related to the well, the numerical model is run to calculate the actual oil production of the well at the grid.

[0073] 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 WELL-10) in several grids within the study area was predicted using both the method described in the embodiments and the numerical simulation oil production capacity prediction method. The oil production obtained using the numerical simulation oil production capacity prediction method was expressed as the numerical simulation predicted production, while the oil production obtained using the method described in the embodiments was expressed as the prediction result of the oil production potential evaluation method. The two prediction results were then summarized. Figure 5 In the middle. By Figure 5 It can be seen that the prediction results of the method using the embodiment are basically equal to the yield prediction results of the numerical simulation method. The relative error between the two is small, with the relative errors being between 2.64% and 2.93%, indicating that the prediction results of the method of the present invention are reliable and have high accuracy.

Claims

1. A method for evaluating the oil production potential of reservoirs in the ultra-high water-cut stage, characterized in that, Includes the following steps: (1) Based on core samples in the study area, a high-expansion water drive experiment was used to establish the correspondence between the water flow ratio and the permeability change ratio; (2) Based on the three-dimensional geological model and production dynamic data of the study area, establish a gridded reservoir numerical model of the study area; (3) Based on the reservoir numerical model, calculate the numerical simulation results in each time step. During the numerical simulation, use the correspondence in step (1) to iteratively update the permeability used in the numerical simulation of each time step. (4) Treat each grid as a virtual well, extract the evaluation index that affects the oil production capacity of each grid in the numerical model field map module, 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 virtual well, take it as the oil production of the corresponding 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 reservoirs in the ultra-high water-cut stage as described in claim 1, characterized in that, The method for establishing the correspondence between the water flow ratio and the permeability change ratio is as follows: a high-multiplier water drive experiment is conducted on the core sample, and the correspondence between the water flow ratio and the permeability change ratio is determined by data regression based on the permeability obtained at the water flow ratio.

3. The method for evaluating the oil production potential of reservoirs in the ultra-high water-cut stage as described in claim 1, characterized in that, The three-dimensional gridded reservoir numerical model of the study area was established by the following methods: collecting core data, well logging data, structural research results, reservoir research results and oil reservoir research results from 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 reservoirs in the ultra-high water-cut stage as described in claim 1, characterized in that, The method for iteratively updating the permeability used in the numerical simulation at each time step is as follows: After the numerical simulation at the i-th time step is completed, the cumulative water flow of each grid at 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 ratio, and the permeability change ratio at the i-th time step is determined by the correspondence in step (1). The permeability change ratio at the i-th time step and the permeability used in the numerical simulation at the i-th time step are calculated to obtain the permeability of each grid used in the numerical simulation at the (i+1)-th time step.

5. The method for evaluating the oil production potential of reservoirs in the ultra-high water-cut stage as described in any one of claims 1-4, characterized in that, The evaluation indicators include oil phase viscosity. Oil phase volume coefficient Relative permeability of oil phase Effective reservoir thickness h Geometric mean of reservoir permeability Kt Equivalent supply radius of virtual wells within the grid The radius of the virtual well within the grid Formation pressure Bottom-hole flowing pressure of virtual wells within the grid Skin coefficient of virtual wells within the grid S .

6. The method for evaluating the oil production potential of reservoirs in the ultra-high water-cut stage as described in claim 5, characterized in that, Effective reservoir thickness h The calculation formula is as follows: In the formula, In numerical models Z The grid step size in the direction, in meters; NTG The net hair ratio is dimensionless.

7. The method for evaluating the oil production potential of reservoirs in the ultra-high water-cut stage as described in claim 5, characterized in that, Geometric mean of permeability Kt The calculation formula is as follows: In the formula, , Each grid in the numerical model is represented by X , Y Reservoir permeability in the direction of direction, mD.

8. The method for evaluating the oil production potential of reservoirs in the ultra-high water-cut stage as described in claim 5, characterized in that, Equivalent supply radius The calculation formula is as follows: In the formula: Let m be the equivalent supply radius of the virtual wells within each grid. Let be the radius of the virtual well within each grid, in meters. The shape factor is a dimensionless grid shape factor; the grid is a rectangular grid. The calculation formula is as follows: In the formula: The shape factor is dimensionless. Let be the radius of the virtual well within each grid, in meters; and A be the drainage area.

9. The method for evaluating the oil production potential of reservoirs in the ultra-high water-cut stage as described in any one of claims 1-4, characterized in that, The formula for calculating the oil production of each grid is as follows: In the formula: Q Daily oil production, m 3 / d; h The effective thickness of the reservoir is in meters (m). Kt The geometric mean of reservoir permeability, in μm 2 ; The effective permeability of the oil phase is dimensionless. The viscosity of the oil phase is mPa·s; m is the oil phase volume coefficient. 3 / m 3 ; To produce pressure differential, Formation pressure, MPa The bottom-hole flowing pressure of the virtual well within the grid, in MPa; Let m be the equivalent supply radius of the virtual well within the grid; The radius of the virtual well within the grid, in meters; S is the skin coefficient of the virtual wells within the grid, which is dimensionless.

Images