Profile control and oil displacement method for high-water-content oil reservoir

Abstract

The invention belongs to the technical field of petroleum engineering, and particularly provides a profile control and oil displacement method for a high-water-content oil reservoir. According to the method, the fluid flowing capacity is described by adopting the water passing multiple, the reservoir physical property change is described by adopting the permeability change value, and the reservoir fluid type change is described by adopting the water saturation. And determining the weight coefficient of each parameter by adopting a variable coefficient method of objective weight. And then, weighting each parameter to obtain a comprehensive reservoir identification index so as to describe the development degree of the optimal flow channel. Profile control agents with different concentrations and different injection amounts are injected according to different dominant seepage channel types, and the pressure gradients in the different dominant seepage channels are adjusted. And finally, a viscosity reducer is injected into the oil reservoir subjected to profile control for displacement. According to the method, the dominant seepage channel of the high-water-content oil reservoir is quantitatively recognized, quantitative profile control and oil displacement of the oil reservoir are achieved, and the recovery efficiency of the high-water-content oil reservoir is greatly improved.

Description

Technical Field

[0001] The invention belongs to the technical field of petroleum engineering, and in particular relates to a profile control and oil recovery method for a high water content oil reservoir. Background Art

[0002] my country's oil and gas fields are mainly distributed in continental sedimentary basins, with the characteristics of high viscosity and high wax content. After continuous water injection development, the eastern sandstone oil fields have gradually entered a high water content period (water content ≥ 80%), but the remaining reserves are still nearly 50%, with great potential for development.

[0003] As an important method to improve oil recovery, water injection development is widely used in various oil fields. During the long-term water injection development process, the water injection flushing effect has different degrees of impact on the reservoir, resulting in significant physical and chemical effects, causing changes in reservoir parameters. Water injection flushing erodes and denudes the mineral particles and intergranular cements of the reservoir rock, making the pore throat smooth or the throat space enlarged, and adding a large high permeability area in the throat to form a "dominant seepage channel", which affects the further development of the oil field. It is extremely important to clarify the development degree of the dominant seepage channel. In addition, after a long period of water flooding in the high water-cut oil field, after the formation of high seepage channels inside the reservoir, inefficient or ineffective circulation of the injected medium is caused, and the remaining oil is highly dispersed, which seriously restricts the improvement of the overall recovery rate of the reservoir. The existing dominant seepage channel identification method can only make qualitative judgments and identifications, and the profile adjustment and water plugging method cannot perform quantitative profile adjustment according to the actual conditions of the reservoir to improve the overall recovery rate of the reservoir in an overall and substantial manner.

[0004] Therefore, it is urgent to develop a profile control and oil recovery method for high water-content reservoirs to increase the swept volume of the reservoir and thus improve the crude oil recovery rate. Summary of the invention

[0005] The present invention aims to solve the technical problems in the above-mentioned related technologies.

[0006] To this end, the purpose of the present invention is to provide a profile control and oil recovery method for high water-content oil reservoirs. On the basis of considering the changes in reservoir physical properties, the degree of development of the dominant seepage channels in the water-drive oil reservoir is quantitatively described, and profile control is quantitatively implemented to achieve rapid and accurate identification of the dominant seepage channels in the water-drive oil reservoir and significantly improve the overall recovery rate of the oil reservoir.

[0007] The present invention provides a method for profile control and oil recovery in a high water content oil reservoir, comprising the following steps:

[0008] S1 uses different water flooding multiples of natural cores with different permeabilities in the reservoir to obtain the permeability change values ​​of cores with different permeabilities at different water flooding multiples, and fits the permeability change value curves of cores with different permeabilities at different water flooding multiples;

[0009] S2 establishes a numerical simulation model for the reservoir. After historical fitting, the water permeability, permeability change, and water saturation of each grid in the reservoir are calculated and used as parameters to describe the degree of development of the dominant seepage channels in the reservoir.

[0010] S3 performs normalization calculation processing on the water flow multiple, permeability change value and water saturation obtained in step S2;

[0011] S4 uses the objective weighted coefficient of variation method to calculate the weight coefficient of the permeability change value, water flow multiple, and water saturation of each grid;

[0012] S5 calculates a comprehensive identification index according to the permeability change value, water flow multiple, water saturation and their respective weight coefficients, and identifies the dominant seepage channel according to the comprehensive identification index; the dominant seepage channel is divided into: a strong dominant seepage channel, a weak dominant seepage channel and a non-dominant seepage channel;

[0013] S6, according to the dominant seepage channels identified in step S5, calculating and optimizing the injection concentration and injection amount of the profile control agent, and adjusting the pressure gradients in different dominant seepage channels;

[0014] S7 performs profile control on the actual reservoir according to the optimal injection concentration and injection amount of the profile control agent calculated in step S6, and then injects a viscosity reducer for displacement to improve the recovery rate of the reservoir.

[0015] Preferably, the calculation formula for the water flow rate is:

[0016]

[0017] Where:

[0018] M is the grid water-pass multiple from 0 to t time step, dimensionless;

[0019] Q x,t is the amount of water flowing through the grid in the x direction at time step t, m 3 ;

[0020] Q y,t is the amount of water flowing through the grid in the x direction at time step t, m 3 ;

[0021] Q z,t is the amount of water flowing through the grid in the z direction at time step t, m 3 ;

[0022] PORV is the grid pore volume, m 3 .

[0023] Preferably, the normalization is calculated as follows:

[0024]

[0025] Where:

[0026] X' m,kij - Normalized value of the original data of the mth parameter of the grid of the kth layer, ith row, jth column in the reservoir; X m,kij - The original data of the mth parameter of the reservoir in the i-th row and j-th column of the k-th layer in the reservoir; X m,max - the maximum value of the grid raw data set of the mth parameter in the reservoir;

[0027] X m,min - The minimum value of the gridded raw data set for the mth parameter in the reservoir.

[0028] Preferably, the objectively weighted coefficient of variation method is calculated as follows:

[0029]

[0030] Where:

[0031] -Mean of the grid-normalized data of the mth parameter in the reservoir;

[0032] u m,i - Normalized data of the mth parameter grid in the reservoir;

[0033] σ m - grid-normalized data standard deviation of the mth parameter in the reservoir;

[0034] V m - coefficient of variation of normalized data of the mth parameter grid in the reservoir;

[0035] ω m - Weight coefficient for grid-normalized data of the mth parameter in the reservoir.

[0036] Preferably, the comprehensive recognition index is calculated as follows:

[0037]

[0038] Where:

[0039] E k,ij - the comprehensive recognition index of the grid in the i-th row and j-th column of the k-th grid layer,

[0040] -The weight coefficient of the mth parameter of the i-th row and j-th column of the k-th grid layer,

[0041] X' m,k,ij -The normalized value of the mth parameter of the i-th row and j-th column of the k-th grid layer.

[0042] Preferably, the comprehensive identification index of each grid of the strong dominant seepage channel is in the range of 0.45-0.68, the comprehensive identification index of each grid of the weak dominant seepage channel is in the range of 0.23-0.45, and the comprehensive identification index of each grid of the non-dominant seepage channel is in the range of 0-0.23.

[0043] Preferably, the profile controlling agent is a jelly-type profile controlling agent, a gel-type profile controlling agent, a polymer microsphere profile controlling agent or a foam-type water plugging profile controlling agent.

[0044] Preferably, the injection concentration range of the profile control agent for the strong dominant seepage channel is 1000 mg / L-2500 mg / L, and the injection concentration range of the profile control agent for the weak dominant seepage channel is 100 mg / L-1000 mg / L.

[0045] Preferably, the injection amount of the profile control agent for the strong dominant seepage channel is 0.15-0.3PV; the injection amount of the profile control agent for the weak dominant seepage channel is 0.01-0.15PV.

[0046] Preferably, the viscosity reducing agent is CO2, a surfactant or a nanofluid.

[0047] Preferably, the surfactant is: sodium lauryl sulfate, dodecyl dimethyl betaine, hexadecyl trimethyl ammonium bromide or dodecyl glucoside.

[0048] Preferably, the nanofluid is: silicon dioxide nanofluid, molybdenum disulfide nanofluid, zirconium dioxide nanofluid, nano-iron tetroxide fluid or nano-graphite fluid.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] In describing the dynamic and static time and space of the reservoir, the present invention uses the water flow multiple to describe the fluid flow capacity, the permeability change value to describe the change in reservoir physical properties, and the water saturation to describe the change in reservoir fluid type. The weight coefficient of each parameter is determined by the objective weight coefficient method. Then, each parameter is weighted to obtain a comprehensive reservoir identification index to describe the degree of development of the preferred flow channel. According to different types of dominant seepage channels, profile control agents of different concentrations and injection amounts are injected to adjust the pressure gradient in different dominant seepage channels. Finally, the viscosity reducer is injected into the profile-adjusted reservoir for displacement. The dominant seepage channels of high-water-content reservoirs are quantitatively identified, and quantitative profile control of the reservoir is achieved, which greatly improves the recovery rate of the reservoir.

[0051] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1This is a flow chart for implementing the method for profile control and oil recovery in a high water content oil reservoir of the present invention;

[0053] Figure 2 A schematic diagram of a grid direction disclosed in an embodiment of the present invention;

[0054] Figure 3 It is a field diagram of the water flow multiples of the grid in the X direction during the water injection process in the embodiment of the present invention;

[0055] Figure 4 It is a surface diagram of the permeability change value after water flooding with different water flow multiples disclosed in the embodiment of the present invention;

[0056] Figure 5 A water saturation field diagram of the water injection process disclosed in an embodiment of the present invention;

[0057] Figure 6 It is a comprehensive identification index field diagram in the water injection process disclosed in an embodiment of the present invention;

[0058] Figure 7 A field diagram of the dominant seepage channel in the water injection process disclosed in an embodiment of the present invention;

[0059] Figure 8 (a) is a tracer concentration monitoring curve disclosed in an embodiment of the present invention;

[0060] Fig. 9 (b) is a tracer concentration monitoring curve disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] The embodiments of the present invention are described in detail below through specific embodiments and application scenarios in conjunction with the accompanying drawings.

[0063] In some embodiments of the present invention, please refer to Figure 1 As shown, a profile control and oil recovery method for a high water-content reservoir is provided. Starting from the description of the dynamic and static time and space of the reservoir, according to the selected parameters, while paying attention to the changes in the physical properties of the reservoir and the changes in the flow capacity of the fluid, and having the screening principles of accumulativity, quantifiability, independence and optimality, the water-permeability multiple describing the sweep of the injected fluid in the reservoir, the permeability change value describing the changes in the physical properties inside the reservoir, and the water saturation describing the changes in the fluid inside the reservoir are selected as parameters to describe the degree of development of the dominant seepage channels in the reservoir.

[0064] The specific implementation steps are as follows:

[0065] S1 uses different water flooding multiples of natural cores with different permeabilities in the reservoir to obtain the permeability change values ​​of cores with different permeabilities at different water flooding multiples, and fits the permeability change value curves of cores with different permeabilities at different water flooding multiples;

[0066] S2 establishes a numerical simulation model for the reservoir. After historical fitting, the water permeability multiple, permeability change value, and water saturation of each grid in the reservoir are calculated as parameters to describe the degree of development of the dominant seepage channels in the reservoir.

[0067] S3 performs normalized calculation processing according to the water flow multiple, permeability change value and water saturation in step S2;

[0068] S4 uses the objective weighted coefficient of variation method to calculate the weight coefficient of permeability change, water flow rate and water saturation of each grid in S3;

[0069] S5: calculating a comprehensive identification index according to the permeability change value, water flow multiple, and water saturation weight coefficient in step S4, and dividing the dominant seepage channel into: a strong dominant seepage channel, a weak dominant seepage channel, and a non-dominant seepage channel;

[0070] S6, according to the dominant seepage channels in step S5, calculating and optimizing the injection concentration and injection amount of the profile control agent, and adjusting the pressure gradient in different dominant seepage channels;

[0071] S7 performs profile control on the actual reservoir according to the optimal injection concentration and injection amount of the profile control agent calculated in step S6, and then injects a viscosity reducer for displacement to improve the recovery rate of the reservoir.

[0072] Preferably, the calculation formula for the water flow rate is:

[0073]

[0074] Where:

[0075] M is the grid water-pass multiple from 0 to t time step, dimensionless;

[0076] Q x,t is the amount of water flowing through the grid in the x direction at time step t, m 3 ;

[0077] Q y,t is the amount of water flowing through the grid in the x direction at time step t, m 3 ;

[0078] Q z,t is the amount of water flowing through the grid in the z direction at time step t, m 3 ;

[0079] PORV is the grid pore volume, m 3 .

[0080] The normalization calculation is as follows:

[0081]

[0082] Where:

[0083] X' m,kij - Normalized value of the original data of the mth parameter of the grid of the kth layer, ith row, jth column in the reservoir; X m,kij - The original data of the mth parameter of the reservoir in the i-th row and j-th column of the k-th layer in the reservoir; X m,max - the maximum value of the grid raw data set of the mth parameter in the reservoir;

[0084] X m,min - The minimum value of the gridded raw data set for the mth parameter in the reservoir.

[0085] The coefficient of variation method of objective weighting is calculated as follows:

[0086]

[0087] Where:

[0088] -Mean of the grid-normalized data of the mth parameter in the reservoir;

[0089] u m,i - Normalized data of the mth parameter grid in the reservoir;

[0090] σ m - grid-normalized data standard deviation of the mth parameter in the reservoir;

[0091] V m - coefficient of variation of normalized data of the mth parameter grid in the reservoir;

[0092] ω m - Weight coefficient for grid-normalized data of the mth parameter in the reservoir.

[0093] The comprehensive recognition index is calculated as follows:

[0094]

[0095] Where:

[0096] E k,ij - the comprehensive recognition index of the grid in the i-th row and j-th column of the k-th grid layer,

[0097] -The weight coefficient of the mth parameter of the i-th row and j-th column of the k-th grid layer,

[0098] X' m,k,ij -The normalized value of the mth parameter of the i-th row and j-th column of the k-th grid layer.

[0099] The comprehensive identification index of each grid for strong dominant seepage channels ranges from 0.45 to 0.68, the comprehensive identification index of each grid for weak dominant seepage channels ranges from 0.23 to 0.45, and the comprehensive identification index of each grid for non-dominant seepage channels ranges from 0 to 0.23.

[0100] The profile control agent is a jelly-type profile control agent, a gel-type profile control agent, a polymer microsphere profile control agent or a foam-type water plugging profile control agent.

[0101] The injection concentration range of the profile control agent for the strong dominant seepage channel is 1000mg / L-2500mg / L, and the injection concentration range of the profile control agent for the weak dominant seepage channel is 100mg / L-1000mg / L.

[0102] The injection amount of the profile control agent for the strong dominant seepage channel is 0.15-0.3PV; the injection amount of the profile control agent for the weak dominant seepage channel is 0.01-0.15PV.

[0103] The viscosity reducing agent is CO2, a surfactant or a nanofluid.

[0104] The surfactant is: sodium lauryl sulfate, dodecyl dimethyl betaine, hexadecyl trimethyl ammonium bromide or dodecyl glucoside.

[0105] The nanofluid is: silicon dioxide nanofluid, molybdenum disulfide nanofluid, zirconium dioxide nanofluid, nano-iron tetroxide or nano-graphite.

[0106] The following is an analysis of the changing laws of the parameters. In order to study the changing laws of water flow rate, permeability change value and water saturation, an ideal model of a 5-point well network (including 1 injection well and 4 production wells) is established. The model contains water and oil phases. Under the fixed production liquid production system, the continuous injection volume of the injection well is 56m 3 / d, and the simulation lasts for 10 years. 0.20PV, 0.35PV, 0.50PV, and 0.65PV were injected respectively. Other basic parameters of the ideal model are shown in Table 1.

[0107] Table 1 Basic parameters of ideal model

[0108]

[0109] (1) Changes in water flow rate

[0110] During the water injection process, when the water is injected along the x, y, and z directions, the calculated water injection volume value is positive; when the water is injected along the x, y, and z directions, the calculated water injection volume value is negative ( Figure 2 ). Therefore, in the process of calculating the water flow rate, it is necessary to take the absolute value of the water injection volume of the grid, then add up the absolute values ​​of the water injection volume in each direction at each time step, and finally divide it by the pore volume of each grid to get the actual grid water flow rate. With the increase of water injection volume, the water flow rate around the water well and oil well becomes significantly larger, and the water flow rate on the main flow line of the water well and oil well increases faster. Between the injection well and the production well, as the injection volume increases, the scope of the injected water gradually expands, and the water flow rate gradually increases, as shown in Figure 2. Figure 3 .

[0111] (2) Change pattern of permeability change value

[0112] Through different permeability (100-2500×10 -3 μm 2 ) In the core indoor water flooding experiment, the permeability change rate before and after water flooding is logarithmically related to the original permeability. Through the logarithmic relationship between the permeability change rate and the original permeability, it can be found that after long-term water flooding, the original permeability of the core is less than 920×10 -3 μm 2 The core permeability is reduced, and the original permeability is greater than 920×10 -3 μm 2 The permeability of the core before and after water flooding tends to increase, and the range of change is 0.5%-10%. Through fitting, the surface diagram of the permeability change value after water flooding with different water flow multiples is finally obtained. Figure 4 .

[0113] (3) Variation of water saturation

[0114] As the amount of water injected increases, the water saturation around the water well increases significantly, and the water saturation on the main flow line between the water well and the oil well increases rapidly. Between the injection well and the production well, as the injection amount increases, the scope of the injected water gradually expands, and the degree of crude oil production increases, resulting in a decrease in the oil saturation in the reservoir and a gradual increase in the water saturation. Figure 5 .

[0115] Based on the above analysis of the identification parameters of the dominant seepage channels in the reservoir, it can be seen that the water flow multiples, permeability change values, and water saturation parameters of the reservoir at different periods are cumulative and quantifiable over time, and also fully consider the physical properties of the reservoir and the changes in fluid fluidity, and thus can comprehensively describe the development degree of the dominant seepage channels in the reservoir in terms of dynamic and static time and space.

[0116] The dominant seepage channel identification method was applied to a field example, and the monitoring results of the field isotope tracer were used to verify whether the dominant seepage channel identified by this method was correct. In order to ensure the reliability of the verification, the reservoir properties of the reservoir should be similar to the core properties used in the water drive permeability change experiment. Therefore, the D reservoir of typical continental sedimentary type was selected. The reservoir has strong heterogeneity in the plane and has undergone 60 years of water injection development. Due to the high water injection intensity, dominant seepage channels are easily formed between the water injection wells and the oil production wells. The reservoir has 6 water wells and 6 oil wells, and the corresponding corner point grid black oil numerical model was established, in which the grid plane specifications are: length × width = 20 × 20m 2 The reserve fitting degree of the model is 97.7%, and the water cut fitting degree reaches 82.0% (taking the month as the time node, water cut error = (actual water cut - calculated water cut) / actual water cut, when the water cut error value is less than or equal to 1.0%, it is a water cut time node that meets the standard; water cut fitting degree = water cut time node that meets the standard / all water cut time nodes).

[0117] In the process of quantitatively characterizing the development degree of the dominant seepage channel of the reservoir, the range of parameters such as water flow multiple, permeability change value, water saturation, etc. in the target study area is first normalized and calculated. Then the weight coefficient is calculated. The range of each parameter is shown in Table 2.

[0118] Table 2 Water flow multiples, permeability change values, water saturation standardization and weight coefficient calculation table

[0119]

[0120] By calculating the standardized values ​​of parameters such as water permeability, permeability change, and water saturation of each grid in the reservoir at the current stage, the standardized values ​​of each parameter are multiplied by the corresponding weight coefficient, and then the sum is obtained to obtain the comprehensive identification index of each grid in the reservoir at the current stage. According to the calculated comprehensive identification index, the size of the comprehensive identification index is evenly divided into 3 ranges, and then the development degree of the dominant seepage channel in the reservoir at the current stage is divided into 3 different levels, such as Figure 3 shown.

[0121] Table 3 Parameter ranges and their standardization

[0122]

[0123] After calculating the comprehensive identification index of all grids, the data file is output through the visualization processing software. The comprehensive identification index field of the dominant seepage channel of the D reservoir at the current stage of calculation is shown in Figure 6The calculation results show that there is an obvious strong dominant seepage channel between WAT3 water well and PRO4 oil well, and a weak dominant seepage channel between WAT3 water well and PRO5 oil well; there is a strong dominant seepage channel between WAT2 water well and PRO1 oil well, and a weak dominant seepage channel between WAT2 water well and PRO2 oil well.

[0124] Interwell tracer interpretation verification:

[0125] Interwell tracer technology is considered to be one of the most direct and accurate methods for identifying dominant seepage channels. Through the monitoring results of trace element tracers, the water drive rate from the injected water to the oil well can be calculated, and then the dominant seepage channel of the reservoir can be determined. In the D reservoir, 0.4 kg of tracer Pr was injected from the WAT3 water well using trace element tracers. The first tracer Pr was seen in the PRO4 oil well 35 days later. The initial tracer concentration was 0.79 μg / L. The calculated water drive rate was 5.6 m / d. Figure 8 The first tracer Pr was observed in the PRO5 oil well after 55 days. The initial tracer concentration was 0.56 μg / L. The calculated water drive rate was 3.4 m / d. Fig. 9 There is a strong dominant seepage channel between WAT3 water well and PRO4 oil well, while there is a weak dominant seepage channel between WAT3 water well and PRO4 oil well. The results of the two dominant seepage channels interpreted by interwell tracers are consistent with the results calculated by the new method.

[0126] Numerical simulation software was used to simulate the D reservoir. 0.15 PV of profile control agent was injected into each injection well, and then water was driven until the water cut reached 98%. The final recovery factor was only increased by 15%.

[0127] Numerical simulation software was used to simulate the injection of profile control agent in reservoir D, and then sodium dodecyl sulfate viscosity reducer was injected. After numerical simulation optimization, the injection concentration of profile control agent in strong dominant seepage channel was 1800mg / L, and the injection volume was 0.2PV. The injection concentration of profile control agent in weak dominant seepage channel was 600mg / L, and the injection volume was 0.1PV. Then, 0.2PV viscosity reducer was injected, and finally the water drive reached 98% water cut, and the recovery rate increased by 27%.

[0128] Parts of the present invention that are not described in detail are well known to those skilled in the art.

[0129] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A method for profile control and oil recovery in a high water content oil reservoir, characterized in that: The method includes: S1 uses different water flooding multiples of natural cores with different permeabilities in the reservoir to obtain the permeability change values ​​of cores with different permeabilities at different water flooding multiples, and fits the permeability change value curves of cores with different permeabilities at different water flooding multiples; S2 establishes a numerical simulation model for the reservoir. After historical fitting, the water permeability, permeability change, and water saturation of each grid in the reservoir are calculated and used as parameters to describe the degree of development of the dominant seepage channels in the reservoir. S3 performs normalization calculation processing on the water flow multiple, permeability change value and water saturation obtained in step S2; S4 uses the objective weighted coefficient of variation method to calculate the weight coefficient of the permeability change value, water flow multiple, and water saturation of each grid; S5 calculates a comprehensive identification index according to the permeability change value, water flow multiple, water saturation and their respective weight coefficients, and identifies the dominant seepage channel according to the comprehensive identification index; the dominant seepage channel is divided into: a strong dominant seepage channel, a weak dominant seepage channel and a non-dominant seepage channel; S6, according to the dominant seepage channels identified in step S5, calculating and optimizing the injection concentration and injection amount of the profile control agent, and adjusting the pressure gradients in different dominant seepage channels; S7 performs profile control on the actual reservoir according to the optimal injection concentration and injection amount of the profile control agent calculated in step S6, and then injects a viscosity reducer for displacement to improve the recovery rate of the reservoir.

2. The method for profile control and oil recovery in a high water content reservoir according to claim 1, characterized in that: The calculation formula of the water overflow multiple is: Where: M is the grid water-pass multiple from 0 to t time step, dimensionless; Q x,t is the amount of water flowing through the grid in the x direction at time step t, m 3 ; Q y,t is the amount of water flowing through the grid in the x direction at time step t, m 3 ; Q z,t is the amount of water flowing through the grid in the z direction at time step t, m 3 ; PORV is the grid pore volume, m 3 .

3. The method for profile control and oil recovery in a high water content reservoir according to claim 1, characterized in that: The normalization calculation is as follows: Where: X' m,kij - Normalized value of the original data of the mth parameter of the grid at the i-th row and j-th column of the k-th layer in the reservoir; X m,kij - The original data of the mth parameter of the reservoir in the i-th row and j-th column of the k-th layer in the reservoir; X m,max - the maximum value of the grid raw data set of the mth parameter in the reservoir; X m,min - The minimum value of the gridded raw data set for the mth parameter in the reservoir.

4. The method for profile control and oil recovery in a high water content reservoir according to claim 1, characterized in that: The coefficient of variation method of objective weighting is calculated as follows: Where: -Mean of the grid-normalized data of the mth parameter in the reservoir; u m,i - Normalized data of the mth parameter grid in the reservoir; σ m - grid-normalized data standard deviation of the mth parameter in the reservoir; V m - coefficient of variation of normalized data of the mth parameter grid in the reservoir; ω m - Weight coefficient for grid-normalized data of the mth parameter in the reservoir.

5. The method for profile control and oil recovery in a high water content reservoir according to claim 1, characterized in that: The comprehensive recognition index is calculated as follows: Where: E k,ij - the comprehensive recognition index of the grid in the i-th row and j-th column of the k-th grid layer, -The weight coefficient of the mth parameter of the i-th row and j-th column of the k-th grid layer, X' m,k,ij -The normalized value of the mth parameter of the i-th row and j-th column of the k-th grid layer.

6. The method for profile control and oil recovery in a high water content reservoir according to claim 1, characterized in that: The comprehensive identification index of each grid of the strong dominant seepage channel ranges from 0.45 to 0.68, the comprehensive identification index of each grid of the weak dominant seepage channel ranges from 0.23 to 0.45, and the comprehensive identification index of each grid of the non-dominant seepage channel ranges from 0 to 0.

23.

7. The method for profile control and oil recovery in a high water content reservoir according to claim 1, characterized in that: The profile control agent is a jelly-type profile control agent, a gel-type profile control agent, a polymer microsphere profile control agent or a foam-type water plugging profile control agent.

8. The method for profile control and oil recovery in a high water content reservoir according to claim 1, characterized in that: The injection concentration range of the profile control agent for the strong dominant seepage channel is 1000 mg / L-2500 mg / L, and the injection concentration range of the profile control agent for the weak dominant seepage channel is 100 mg / L-1000 mg / L.

9. The method for profile control and oil recovery in a high water content reservoir according to claim 1, characterized in that: The injection amount of the profile control agent for the strong dominant seepage channel is 0.15-0.3PV; the injection amount of the profile control agent for the weak dominant seepage channel is 0.01-0.15PV.

10. The method for profile control and oil recovery in a high water content reservoir according to claim 1, characterized in that: The viscosity reducing agent is CO2, a surfactant or a nanofluid.

11. The method for profile control and oil recovery in a high water content reservoir according to claim 9, characterized in that: The surfactant is sodium lauryl sulfate, dodecyl dimethyl betaine, hexadecyl trimethyl ammonium bromide or dodecyl glucoside.

12. The method for profile control and oil recovery in a high water content reservoir according to claim 9, characterized in that: The nanofluid is: silicon dioxide nanofluid, molybdenum disulfide nanofluid, zirconium dioxide nanofluid, nano ferroferric oxide fluid or nano graphite fluid.

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