Reservoir dynamic prediction method, device, medium and terminal based on microgel flooding

Abstract

The present invention discloses a method, device, medium and terminal for predicting oil reservoir dynamics based on microgel drive. The method comprises the following steps: simplifying the oil reservoir injection and production system into a connected unit between wells; establishing a material balance equation with the connected unit as the object, and generating the inter-well flow rate and the well point water saturation; injecting a preset concentration of microgel into the injection well, and establishing a concentration balance equation for microgel drive; calculating the microgel particle phase concentration of the injection well in the direction of each connected unit at the target time; adjusting the water phase permeability reduction coefficient, and calculating the well point water saturation at the target time, so as to predict the production dynamic changes of the target block after adding the microgel. The present invention combines the plugging characteristics of the microgel with the idea of ​​well-to-well connectivity, and dynamically predicts the concentration distribution, plugging situation and oil displacement effect of the microgel particle phase after entering the pore, so as to guide the on-site development plan, improve the oil production of heterogeneous oil reservoirs, and increase economic benefits.

Description

Technical Field

[0001] The invention relates to the field of oil reservoir injection and production, and in particular to an oil reservoir dynamic prediction method, device, medium and terminal based on microgel flooding. Background Art

[0002] At present, water flooding is still the main development method for most oil fields in my country. Long-term development has led to prominent contradictions between injection and production, and multiple levels of dominant flow fields coexist and are difficult to identify. At present, profile control and water plugging have become an important process transformation measure in water injection development. The main profile control prediction methods are field test method, statistical model method and numerical simulation method. Field test method and statistical model method mainly rely on manual experience decision-making, without combining the understanding of formation connectivity. The overall success rate of field profile control and water plugging measures is low, the effect is poor, and the failure is fast. However, the numerical simulation technology for profile control is not mature enough, and it is difficult to accurately simulate and predict the dynamics of profile control. The main problems are: the seepage mechanism of profile control agents is complex and difficult to describe in detail; numerical solution is difficult and cannot be calculated quickly. At the same time, without integrating the information of dominant flow channels connected between wells, it is difficult to perform accurate simulation and scheme optimization, and it is difficult to achieve large-scale application.

[0003] In recent years, Zhao Hui et al. proposed a new data-physics driven model INSIM (Physics-Based Data-Driven model), which only needs to use the production data of oil and water wells and well location information for modeling. Unlike machine learning and other driving models, it makes rapid dynamic predictions under the condition of material balance, and quantitatively characterizes the inter-well connectivity relationship by inverting parameters such as inter-well conductivity and connected volume through historical fitting. Since then, some scholars have carried out relevant research based on INSIM, and established models such as INSIM-F, INSIM-FT-3D, INSIM-FPT and polymer flooding dynamic prediction, which have achieved good results in reducing water and increasing oil in highly homogeneous reservoirs. However, for highly heterogeneous reservoirs, the development effect of polymer flooding in the middle and late stages is poor. This is because the reservoir heterogeneity and polymer retention characteristics will cause "liquid absorption profile reversal", which is not conducive to improving the utilization of medium and low permeability layers, and cannot further expand the swept volume, thereby exacerbating interlayer contradictions and seriously affecting the development effect of polymer flooding. Summary of the invention

[0004] The present invention provides a method, device, medium and terminal for predicting oil reservoir dynamics based on microgel flooding, which solve the above-mentioned technical problems.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: A method for predicting reservoir dynamics based on microgel flooding comprises the following steps:

[0006] Step 1, establishing an initial connectivity model for simulating inter-well oil-water dynamics, wherein the initial connectivity model simplifies the reservoir injection and production system into a connection unit between wells, wherein the connection unit is characterized by two characteristic parameters: conductivity and connection volume;

[0007] Step 2, establishing a material balance equation with the connected unit as the object, and solving the material balance equation at a constant pressure to generate the inter-well flow rate and the well point water saturation;

[0008] Step 3, injecting a microgel of a preset concentration into the water injection well, and establishing a concentration balance equation for microgel flooding according to the inter-well flow rate and the water saturation of the well point;

[0009] Step 4, solving the concentration balance equation to calculate the microgel particle phase concentration of the water injection well in each connected unit direction at the target time after the microgel is injected;

[0010] Step 5, according to the microgel particle phase concentration of the connected unit at the target time, the corresponding water phase permeability reduction coefficient is adjusted, and the well point water saturation at the target time is calculated to predict the production dynamic changes of the target block after adding microgel.

[0011] In a preferred embodiment, the material balance equation is established with the connected unit as the object, and the material balance equation is solved at a constant pressure to generate the well flow rate and well point water saturation, specifically:

[0012] S201, taking the connected unit as the object, a material balance equation is established:

[0013]

[0014] Among them, i and j represent the well numbers; N is the total number of wells; n is the current moment, and n-1 is the previous moment; and They represent the average reservoir pressures of the i-th well and the j-th well in the drainage area at time n, respectively; Δt is the time interval; is the flow rate of the ith well at the nth moment; C t is the comprehensive compressibility coefficient of the reservoir; T ij represents the average conductivity of the i-th well and the j-th well; V pi is the connected volume of the drainage area of ​​the i-th well;

[0015] S202, using a constant pressure method to perform implicit difference solution on the material balance equation, and generating the inter-well flow rate between the connected units as follows:

[0016]

[0017] Where n is the current time, represents the flow rate of the well-to-well connection unit between the i-th well and the j-th well at the n-th time;

[0018] S203, based on the bottom hole flow reversal caused by shutting down the well and re-injection, the well point water saturation is back-calculated using the interpolation method as follows:

[0019]

[0020] Among them, n is the current moment, n′ is the previous moment, and f w is the moisture content, f′ w (S wijk ) is the water content derivative of the kth layer traced from the jth well to the ith well, f′ w (S wjk ) is the water content derivative of the jth well in the kth layer, S wik is the water saturation of the i-th well in the k-th layer, S wjk is the water saturation of the jth well in the kth layer, S wijk is the water saturation of the connected unit between well i and well j in the kth layer; represents the reverse dimensionless cumulative flow from well j to well i from time n' to time n, represents the dimensionless cumulative flow from well i to well j from time 0 to time n′, Represents the dimensionless cumulative flow from well i to well j from time 0 to time n.

[0021] In a preferred embodiment, the microgel of a preset concentration is injected into the water injection well, and the concentration balance equation of the microgel flooding is established according to the inter-well flow rate and the water saturation of the well point:

[0022]

[0023] Among them, N w is the number of injection and production wells, n is the current time, y pijk S represents the microgel particle concentration of the connected unit between well i and well j in the kth layer; wijk is the water saturation of the connected unit between well i and well j at the kth layer; and are the inflow and outflow flows of well i and well j in the connected unit of layer k at the previous moment respectively; V ijk is the connected volume between well i and well j in the kth layer.

[0024] In a preferred embodiment, the concentration balance equation is solved to calculate the concentration of microgels in the injection well in the direction of each connected unit after the microgels are injected, specifically:

[0025] S401, using historical oil and water production data of the oil reservoir to perform fitting inversion on the water saturation of the well point, adjusting characteristic parameters of the initial connectivity model, and generating an optimized connectivity model;

[0026] S402, calculating the water injection splitting coefficient of the water injection well toward the oil wells in each direction according to the optimized connectivity model;

[0027] S403, calling a preset microgel concentration distribution expression, and calculating the microgel particle phase concentration of the water injection well in each connected unit direction at the initial moment based on the water injection splitting coefficient;

[0028] S404, solving the concentration balance equation according to the microgel particle phase concentration in each connected unit direction at the initial moment, and generating the microgel particle phase concentration in each connected unit direction of the water injection well at the target moment.

[0029] In a preferred embodiment, the preset microgel concentration distribution expression is:

[0030]

[0031] Among them, y i =0 when x i is defined as the critical split flow, denoted as x i represents the split flow of connected unit i, y i represents the microgel particle phase concentration of connected unit i, A is the total number of connected units connected to the injection well, and a is the particle size parameter.

[0032] In a preferred embodiment, the preset formula for adjusting the corresponding water phase permeability reduction coefficient according to the microgel particle phase concentration of the connected unit at the target time and calculating the well point water saturation at the target time is:

[0033]

[0034] Among them, y i is the microgel particle phase concentration of connected unit i at the target time, r, s, t are preset values, R max is the maximum permeability reduction coefficient, k ro , k rw are the relative permeabilities of formation crude oil and formation water, μ o , μ w are the viscosities of the crude oil and formation water in the kth layer, C k is the permeability correlation coefficient, R k is the permeability reduction coefficient, μ l is the viscosity-concentration correlation coefficient, f w is the moisture content.

[0035] A second aspect of an embodiment of the present invention provides a reservoir dynamic prediction device based on microgel flooding, comprising a first creation module, a first calculation module, a second creation module, a second calculation module and a prediction module.

[0036] The first creation module is used to establish an initial connectivity model for simulating inter-well oil-water dynamics, wherein the initial connectivity model simplifies the reservoir injection and production system into a connectivity unit between wells, wherein the connectivity unit is characterized by two characteristic parameters: conductivity and connectivity volume;

[0037] The first calculation module is used to establish a material balance equation with the connected unit as the object, and solve the material balance equation at a constant pressure to generate the inter-well flow rate and well point water saturation;

[0038] The second creation module is used to inject a preset concentration of microgel into the water injection well, and establish a concentration balance equation for microgel flooding according to the inter-well flow rate and the water saturation of the well point;

[0039] The second calculation module is used to solve the concentration balance equation to calculate the microgel particle phase concentration of the water injection well in each connected unit direction at the target time after the microgel is injected;

[0040] The prediction module is used to adjust the corresponding water phase permeability reduction coefficient according to the microgel particle phase concentration of the connected unit at the target time, and calculate the well point water saturation at the target time to predict the production dynamic changes of the target block after adding microgel.

[0041] In a preferred embodiment, the second calculation module includes:

[0042] An optimization unit, used to perform fitting inversion on the water saturation of the well point using historical oil and water production data of the reservoir, adjust characteristic parameters of the initial connectivity model, and generate an optimized connectivity model;

[0043] A splitting coefficient calculation unit, used for calculating the water injection splitting coefficient of the water injection well to the oil wells in various directions according to the optimized connectivity model;

[0044] An initial concentration calculation unit, used to call a preset microgel concentration distribution expression, and calculate the microgel particle phase concentration of the water injection well in the direction of each connected unit at the initial moment based on the water injection splitting coefficient;

[0045] The concentration prediction unit is used to solve the concentration balance equation according to the microgel particle phase concentration in each connected unit direction at the initial moment, and generate the microgel particle phase concentration in each connected unit direction of the water injection well at the target moment.

[0046] A third aspect of an embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for predicting reservoir dynamics based on microgel flooding.

[0047] A fourth aspect of an embodiment of the present invention provides a terminal, comprising the computer-readable storage medium and a processor, wherein the processor implements the steps of the above-mentioned method for predicting reservoir dynamics based on microgel flooding when executing the computer program on the computer-readable storage medium.

[0048] The present invention proposes a method, device, storage medium and terminal for dynamic prediction of oil reservoirs based on microgel flooding, which combines the plugging characteristics of microgels with the idea of ​​well connectivity, and dynamically predicts the concentration distribution, plugging conditions and oil displacement effects of microgel particles after entering the pores, thereby guiding the design of on-site development plans, improving the oil production of heterogeneous oil reservoirs and increasing economic benefits.

[0049] In order to make the above-mentioned objects, features and advantages of the invention more obvious and easy to understand, the preferred embodiments of the present invention are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 is a schematic flow chart of a method for predicting reservoir dynamics based on microgel flooding in one embodiment;

[0052] Figure 2 is a graph showing the relationship between particle phase concentration and fluid fraction flow in one embodiment;

[0053] Figure 3a is a fitting diagram of oil production rate after adding microgel in one embodiment;

[0054] Figure 3b is a fitting diagram of the moisture content after adding microgel in one embodiment;

[0055] Figure 4a is a block water content fitting diagram of an offshore oil field in one embodiment;

[0056] Figure 4b is a block oil production rate fitting diagram of an offshore oil field in one embodiment;

[0057] Figure 5is a graph showing the change in daily oil production before and after microgel injection in one embodiment;

[0058] Figure 6 is a graph showing the change in water content before and after microgel injection in one embodiment;

[0059] Figure 7 is a graph showing the cumulative oil production before and after microgel injection in one embodiment;

[0060] Figure 8 is a schematic structural diagram of an oil reservoir dynamic prediction device based on microgel flooding in an embodiment;

[0061] Fig. 9 The figure is a diagram of the internal structure of a terminal in an embodiment. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and beneficial technical effect of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should be understood that the specific implementation methods described in this specification are only for explaining the present invention, not for limiting the present invention.

[0063] As a new type of particle-type polymer, microgel has a different plugging mechanism from traditional plugging agents and polymers. Studies have shown that after entering porous media, microgel particles show the characteristic of blocking large pores rather than small ones, that is, the particles preferentially enter large pores for plugging, thereby achieving the purpose of improving the recovery rate. The present invention proposes a reservoir dynamic prediction method, device, storage medium and terminal based on microgel flooding, which combines the plugging characteristics of microgel with the idea of ​​well connectivity, and dynamically predicts the concentration distribution, plugging situation and oil displacement effect of microgel particles after entering the pores, thereby guiding the design of field development plans, improving the oil recovery of heterogeneous reservoirs, and increasing economic benefits.

[0064] Specifically in one embodiment, Figure 1 As shown, a method for predicting reservoir dynamics based on microgel flooding is provided, comprising the following steps:

[0065] S1, establish an initial connectivity model for simulating the oil-water dynamics between wells, the initial connectivity model simplifies the reservoir injection and production system into a connected unit between wells, the connected unit is characterized by two characteristic parameters: conductivity and connected volume. Among them, conductivity represents the seepage velocity under unit pressure difference in the connected unit, reflecting the seepage capacity between wells; and connected volume represents the material basis of the connected unit.

[0066] Then, S2 is executed to establish a material balance equation with the connected unit as the object, and the material balance equation is solved at a constant pressure to generate the inter-well flow rate and the well point water saturation.

[0067] First, taking the i-th well as a reference, the material balance equation is established as:

[0068]

[0069] In the formula, i, j represent the well number; N is the total number of wells; n is the time step; and Respectively represent the average reservoir pressure of the jth well and the ith well in the drainage area at time n; Δt is the time interval; is the flow rate of the ith well at the nth moment, water injection is positive, and oil production is; C t is the comprehensive compressibility coefficient of the reservoir; T ij represents the average conductivity of the i-th well and the j-th well; V pi is the controlled volume of the oil leakage area of ​​the i-th well. After rearranging and solving equation (2-1), we can get:

[0070]

[0071] in, The pressure relationship between time n-1 and time n can be converted to:

[0072]

[0073] By simplifying the calculation form by formula (2-3), the pressure of all wells at time n is:

[0074] P n =β -1 (P n-1 +γ) (2-4)

[0075] After the pressure solution is completed, the flow direction and flow rate of the fluid in the well-to-well communication unit can be further obtained:

[0076]

[0077] In the above formula, Represents the flow rate of the connecting unit between the i-th well and the j-th well at the n-th time.

[0078] Considering the situation of shutting down wells and re-injection causing the bottom hole flow reversal, the following formula is used to calculate the well point water content derivative, and then the well point water saturation is back-calculated by the interpolation method:

[0079]

[0080] In the formula, in represents the reverse dimensionless cumulative flow from well j to well i from time n' to time n, represents the dimensionless cumulative flow from well i to well j from time 0 to time n′, Represents the dimensionless cumulative flow from well i to well j from time 0 to time n.

[0081] Then, S3 is executed to inject a preset concentration of microgel into the water injection well, and a concentration balance equation of microgel flooding is established according to the inter-well flow rate and the water saturation of the well point as follows:

[0082]

[0083] In the formula, represents the microgel particle phase concentration of the k-th layer connected unit between well i and well j at time n; is the water saturation of the connected unit between well i and well j at the kth layer at the previous moment; and are the inflow and outflow rates of well i and well j in the k-th layer connected unit at the previous moment, respectively.

[0084] Then, S4 is executed to solve the concentration balance equation to calculate the microgel particle phase concentration of the water injection well in each connected unit direction at the target time after the microgel is injected. Specifically, the following steps are included:

[0085] S401, using historical oil and water production data of the oil reservoir to perform fitting inversion on the water saturation of the well point, adjusting characteristic parameters of the initial connectivity model, and generating an optimized connectivity model.

[0086] S402, calculating the water injection splitting coefficient of the water injection well toward the oil wells in each direction according to the optimized connectivity model.

[0087] Assume that j is an injection well and i is a production well connected to it. The following formula is used to calculate the injection splitting coefficient and injection efficiency:

[0088]

[0089] Where: A j represents the water injection splitting coefficient of well j; A is the number of connected units connected to well j. According to the definition, the water injection splitting coefficient represents the liquid distribution ratio between its injection volume and the production wells connected to it, and its value range is 0 to 1.

[0090] S403, calling a preset microgel concentration distribution expression, and calculating the microgel particle phase concentration of the water injection well in each connected unit direction at the initial moment based on the water injection splitting coefficient.

[0091] Many empirical formulas in biofluid mechanics describe the separation of red blood cells and plasma particles during blood flow and calculate the concentration distribution of red blood cells in different channels. This method introduces and modifies the expression of red blood cell dendritic fork concentration distribution, and the expression of microgel particle phase concentration in each outlet is:

[0092]

[0093] In the formula, y i =0 when x i is defined as the critical split flow, denoted as x i It represents the split flow of connected unit i, which has the same definition as the split coefficient and can be directly replaced by the split coefficient. i represents the microgel particle phase concentration of connected unit i, A is the total number of connected units connected to the injection well, and a is the particle size parameter.

[0094] The concentration of microgel particles in different pores is greatly affected by parameter a. The larger the particle size, the larger the parameter a, the greater the shear force of the fluid on the microgel, and the greater the degree of particle phase separation. When a = 1, the particle phase concentration in each pore is equal to the average concentration, that is, the polymer solution is evenly distributed in different pores.

[0095] Taking N = 2 as an example, the critical split flow rate xi* increases with the increase of a, that is, the intersection of the curve and the horizontal axis gradually moves to the right; when xi≤xi*, yi=0, that is, no particles enter the pores; when xi<0.5, the concentration of particles entering the pores is less than the average concentration; when xi=0.5, the concentration of particles entering the pores is equal to the average concentration; when xi>0.5, the concentration of particles entering the pores is greater than the average concentration, such as Figure 2 shown.

[0096] S404, solving the concentration balance equation according to the microgel particle phase concentration in each connected unit direction at the initial moment, and generating the microgel particle phase concentration in each connected unit direction of the injection well at the target moment. That is, when the microgel particle phase concentration y at the initial moment is obtained i (x i ) and then substitute the result into Formula 2-7 to solve the concentration according to the material balance equation.

[0097] Then, S5 is executed to adjust the corresponding water phase permeability reduction coefficient according to the microgel particle phase concentration of the connected unit at the target time, and calculate the well point water saturation at the target time to predict the production dynamic change of the target block after adding the microgel.

[0098] After obtaining the concentration distribution of microgel particles in each connected unit, the permeability reduction coefficient R of the water phase can be adjusted according to the viscosity-concentration relationship measured experimentally and the adsorption amount in the core. k ,

[0099]

[0100] Where yi To calculate the phase concentration of the obtained microgel particles, the values ​​of r, s, and t are determined experimentally, R max is the maximum permeability reduction coefficient, the calculation process is relatively complicated and can be measured through indoor experiments. ro , k rw are the relative permeabilities of formation crude oil and formation water, μ o , μ w are the viscosities of the crude oil and formation water in the kth layer, C k is the permeability correlation coefficient, R k is the permeability reduction coefficient, μ l is the viscosity-concentration correlation coefficient, f w is the moisture content.

[0101] After obtaining the water content and microgel concentration of a single well at a certain moment, the water production rate and oil production rate of a single well as the oil production rate and water production rate of the block at that moment can be calculated. The expressions of the water production rate and oil production rate of the oil well are as follows:

[0102]

[0103] The expressions for the oil production rate, liquid production rate, water content, and cumulative oil production of the whole area are:

[0104]

[0105] FOPT n =FOPT n-1 +FOPR n Δt n (2-19)

[0106] The above embodiments combine the plugging characteristics of microgels with the idea of ​​well connectivity, and dynamically predict the concentration distribution, plugging conditions and oil displacement effects of microgel particles after entering the pores, thereby guiding the on-site development plan, improving the oil production of heterogeneous reservoirs and increasing economic benefits.

[0107] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0108] The following is an explanation through a specific embodiment.

[0109] A high permeability core with a length of 10 cm and a diameter of 2.5 cm is connected in parallel with a low permeability core of the same size, and water is injected at intervals of 50 minutes. After water flooding for a period of time, 0.3 pv of microgel is added to the injection end. The dynamic prediction method of microgel mentioned in the present invention is used to simulate the change of oil displacement effect after adding microgel. Figure 3a and Figure 3b As shown in the figure, the dotted line interval represents the changes after the addition of microgel. It can be clearly seen from the figure that the oil production rate is significantly improved and the water content is reduced after the microgel injection, which is consistent with the experimental data, proving that the microgel simulation method is accurate and reliable.

[0110] Based on the correctness of the microgel dynamic prediction method verified by experiments, it was applied to an actual block of an offshore oil field to establish a dynamic calculation model for microgel production. The target area of ​​the block well location includes a total of 65 wells, including 24 water injection wells and 41 polymer injection wells, which were put into production in August 1993.

[0111] The well connectivity model was applied to automatically perform historical fitting on the reservoir’s single well oil production rate, block water content, cumulative oil production and other indicators in combination with actual geological parameters. The fitting results are shown in Figure 2. Figure 4a and Figure 4b shown.

[0112] The connectivity model obtained based on historical matching can be used for dynamic prediction, and two sets of production plans can be formulated for simulation, and the effects of the two sets of plans after simulation can be compared:

[0113] Option 1: At the end of reservoir development, a forecast is conducted under the condition of maintaining the current production system, simulating the changes in production indicators such as daily oil production, cumulative oil production, and water content in the target block within one year.

[0114] Option 2: At the end of reservoir development, two water injection wells D6 and D11 were selected to inject microgels with a concentration of 6000 mg / L. After the microgels entered the channels around the two water injection wells, the development effects of the corresponding production wells were changed by changing the connectivity parameters in each connected unit. The changes in production indicators such as daily oil production, cumulative oil production, and water content in the target block under the production conditions within one year were simulated.

[0115] The effects of each indicator of Scheme 1 and Scheme 2 were compared to observe the difference between the effects of microgel injection and water flooding within one year, and then the blocking of the flow channel in the reservoir by microgel was analyzed. The specific effects are as follows: Figures 5 to 7 shown.

[0116] As can be seen from the figure above, when the microgel injection is 6000mg / L, the average oil production rate of the well group increases by 52m 3 / d and showed a trend of first rising and then stabilizing. The cumulative oil production increased by 15,000m3 in one year.3 The water content showed a trend of first decreasing rapidly and then tending to be stable, achieving the effect of reducing water and increasing oil production, and significantly increasing economic benefits.

[0117] Figure 8 FIG. 1 is a schematic diagram of a reservoir dynamic prediction device based on microgel flooding provided by another embodiment of the present invention. Figure 8 As shown, it includes a first creation module 100, a first calculation module 200, a second creation module 300, a second calculation module 400 and a prediction module 500,

[0118] The first creation module 100 is used to establish an initial connectivity model for simulating inter-well oil-water dynamics, wherein the initial connectivity model simplifies the reservoir injection and production system into a connection unit between wells, wherein the connection unit is characterized by two characteristic parameters: conductivity and connection volume;

[0119] The first calculation module 200 is used to establish a material balance equation with the connected unit as the object, and solve the material balance equation at a constant pressure to generate the inter-well flow rate and the well point water saturation;

[0120] The second creation module 300 is used to inject a preset concentration of microgel into the water injection well, and establish a concentration balance equation of microgel flooding according to the inter-well flow rate and the well point water saturation;

[0121] The second calculation module 400 is used to solve the concentration balance equation and calculate the microgel particle phase concentration of the water injection well in each connected unit direction at the target time after the microgel is injected;

[0122] The prediction module 500 is used to adjust the corresponding water phase permeability reduction coefficient according to the microgel particle phase concentration of the connected unit at the target time, and calculate the well point water saturation at the target time to predict the production dynamic changes of the target block after adding microgel.

[0123] In a preferred embodiment, the second calculation module 400 includes:

[0124] An optimization unit, used to perform fitting inversion on the water saturation of the well point using historical oil and water production data of the reservoir, adjust characteristic parameters of the initial connectivity model, and generate an optimized connectivity model;

[0125] A splitting coefficient calculation unit, used for calculating the water injection splitting coefficient of the water injection well to the oil wells in various directions according to the optimized connectivity model;

[0126] An initial concentration calculation unit, used to call a preset microgel concentration distribution expression, and calculate the microgel particle phase concentration of the water injection well in the direction of each connected unit at the initial moment based on the water injection splitting coefficient;

[0127] The concentration prediction unit is used to solve the concentration balance equation according to the microgel particle phase concentration in each connected unit direction at the initial moment, and generate the microgel particle phase concentration in each connected unit direction of the water injection well at the target moment.

[0128] In one embodiment, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method for predicting reservoir dynamics based on microgel flooding are implemented.

[0129] Fig. 9 The figure is an internal structure diagram of a terminal in an embodiment, and the terminal may be a notebook computer, or other mobile terminal or fixed terminal. Fig. 9 As shown, it includes a memory 81 and a processor 80. The memory 81 stores a computer program 82. When the processor 80 executes the computer program 82, the steps of the reservoir dynamic prediction method based on microgel flooding are implemented.

[0130] Those skilled in the art will understand that Fig. 9 It is only an example of the terminal of the present invention and does not constitute a limitation on the terminal. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include a power management module, an operation processing module, input and output devices, a network access device, a bus, etc.

[0131] The processor 80 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0132] The memory 81 may be an internal storage unit of the terminal, such as a hard disk or a memory. The memory 81 may also be an external storage device of the terminal, such as a plug-in hard disk equipped on the compass calibration terminal, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Further, the memory 81 may also include both an internal storage unit of the compass calibration terminal and an external storage device. The memory 81 is used to store computer programs and other programs and data required by the compass calibration terminal. The memory 81 may also be used to temporarily store data that has been output or is to be output.

[0133] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the terminal is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned device refers to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0134] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0135] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0136] In the embodiments provided by the present invention, it should be understood that the disclosed terminal / terminal device and method can be implemented in other ways. For example, the terminal / terminal device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, the indirect coupling or communication connection of the terminal or unit can be electrical, mechanical or other forms.

[0137] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0138] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0139] The present invention is not limited to what is described in the specification and implementation modes, and therefore additional advantages and modifications can be easily realized by those skilled in the art. Therefore, without departing from the spirit and scope of the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details, representative devices, and illustrative examples shown and described herein.

Claims

1. A method for predicting reservoir dynamics based on microgel flooding, characterized in that: The following steps are involved: Step 1, establishing an initial connectivity model for simulating inter-well oil-water dynamics, wherein the initial connectivity model simplifies the reservoir injection and production system into a connection unit between wells, wherein the connection unit is characterized by two characteristic parameters: conductivity and connection volume; Step 2, establishing a material balance equation with the connected unit as the object, and solving the material balance equation at a constant pressure to generate the inter-well flow rate and the well point water saturation; Step 3, injecting a microgel of a preset concentration into the water injection well, and establishing a concentration balance equation for microgel flooding according to the inter-well flow rate and the water saturation of the well point; Step 4, solving the concentration balance equation to calculate the microgel particle phase concentration of the water injection well in each connected unit direction at the target time after the microgel is injected; Step 5, adjusting the corresponding water phase permeability reduction coefficient according to the microgel particle phase concentration of the connected unit at the target time, and calculating the well point water saturation at the target time to predict the production dynamic changes of the target block after adding the microgel; The material balance equation is: Among them, i and j represent the well numbers; N is the total number of wells; n is the current moment, and n-1 is the previous moment; and They represent the average reservoir pressures of the i-th well and the j-th well in the drainage area at time n, respectively; Δt is the time interval; is the flow rate of the ith well at the nth moment; C t is the comprehensive compressibility coefficient of the reservoir; T ij represents the average conductivity of the i-th well and the j-th well; V pi is the connected volume of the drainage area of ​​the i-th well; The concentration balance equation of the microgel flooding is: Among them, N w is the number of injection and production wells, n is the current time, y pijk S represents the microgel particle concentration of the connected unit between well i and well j in the kth layer; wijk is the water saturation of the connected unit between well i and well j at the kth layer; and are the inflow and outflow rates of the connected unit of well i and well j at the kth layer at the last moment respectively; V ijk is the connected volume between well i and well j in the kth layer.

2. The method for predicting reservoir dynamics based on microgel flooding according to claim 1, characterized in that: The material balance equation is established with the connected unit as the object, and the material balance equation is solved at a constant pressure to generate the inter-well flow rate and well point water saturation, specifically: S201, taking the connected unit as the object, a material balance equation is established: Among them, i and j represent the well numbers; N is the total number of wells; n is the current moment, and n-1 is the previous moment; and They represent the average reservoir pressures of the i-th well and the j-th well in the drainage area at time n, respectively; Δt is the time interval; is the flow rate of the ith well at the nth moment; C t is the comprehensive compressibility coefficient of the reservoir; T ij represents the average conductivity of the i-th well and the j-th well; V pi is the connected volume of the drainage area of ​​the i-th well; S202, using a constant pressure method to perform implicit difference solution on the material balance equation, and generating the inter-well flow rate between the connected units as follows: Where n is the current time, represents the flow rate of the well-to-well connection unit between the i-th well and the j-th well at the n-th time; S203, based on the bottom hole flow reversal caused by shutting down the well and re-injection, the well point water saturation is back-calculated using the interpolation method as follows: Among them, n is the current moment, n′ is the previous moment, and f w is the moisture content, f′ w (S wijk ) is the water content derivative of the kth layer traced from the jth well to the ith well, f′ w (S wjk ) is the water content derivative of the jth well in the kth layer, S wik is the water saturation of the i-th well in the k-th layer, S wjk is the water saturation of the jth well in the kth layer, S wijk is the water saturation of the connected unit between well i and well j in the kth layer; represents the reverse dimensionless cumulative flow from well j to well i from time n' to time n, represents the dimensionless cumulative flow from well i to well j from time 0 to time n′, Represents the dimensionless cumulative flow from well i to well j from time 0 to time n.

3. The method for predicting reservoir dynamics based on microgel flooding according to claim 2, characterized in that: The concentration balance equation is solved to calculate the concentration of microgels in the injection well in each connected unit direction after the microgels are injected, specifically: S401, using historical oil and water production data of the oil reservoir to perform fitting inversion on the water saturation of the well point, adjusting characteristic parameters of the initial connectivity model, and generating an optimized connectivity model; S402, calculating the water injection splitting coefficient of the water injection well toward the oil wells in each direction according to the optimized connectivity model; S403, calling a preset microgel concentration distribution expression, and calculating the microgel particle phase concentration of the water injection well in each connected unit direction at the initial moment based on the water injection splitting coefficient; S404, solving the concentration balance equation according to the microgel particle phase concentration in each connected unit direction at the initial moment, and generating the microgel particle phase concentration in each connected unit direction of the water injection well at the target moment.

4. The method for predicting reservoir dynamics based on microgel flooding according to claim 3, characterized in that: The preset microgel concentration distribution expression is: Among them, y i =0 when x i is defined as the critical split flow, denoted as x i represents the split flow of connected unit i, y i represents the microgel particle phase concentration of connected unit i, A is the total number of connected units connected to the injection well, and a is the particle size parameter.

5. The method for predicting reservoir dynamics based on microgel flooding according to claim 4, characterized in that: The preset formula for adjusting the corresponding water phase permeability reduction coefficient according to the microgel particle phase concentration of the connected unit at the target time and calculating the well point water saturation at the target time is: Among them, y i is the microgel particle phase concentration of connected unit i at the target time, r, s, t are preset values, R max is the maximum permeability reduction coefficient, k ro , k rw are the relative permeabilities of formation crude oil and formation water, μ o , μ w are the viscosities of the crude oil and formation water in the kth layer, C k is the permeability correlation coefficient, R k is the permeability reduction coefficient, μ l is the viscosity-concentration correlation coefficient, f w is the moisture content.

6. A reservoir dynamic prediction device based on microgel flooding, using the reservoir dynamic prediction method based on microgel flooding according to any one of claims 1 to 5, characterized in that: It includes a first creation module, a first calculation module, a second creation module, a second calculation module and a prediction module, The first creation module is used to establish an initial connectivity model for simulating inter-well oil-water dynamics, wherein the initial connectivity model simplifies the reservoir injection and production system into a connectivity unit between wells, wherein the connectivity unit is characterized by two characteristic parameters: conductivity and connectivity volume; The first calculation module is used to establish a material balance equation with the connected unit as the object, and solve the material balance equation at a constant pressure to generate the inter-well flow rate and the well point water saturation; The second creation module is used to inject a preset concentration of microgel into the water injection well, and establish a concentration balance equation for microgel flooding according to the inter-well flow rate and the well point water saturation; The second calculation module is used to solve the concentration balance equation to calculate the microgel particle phase concentration of the water injection well in each connected unit direction at the target time after the microgel is injected; The prediction module is used to adjust the corresponding water phase permeability reduction coefficient according to the microgel particle phase concentration of the connected unit at the target time, and calculate the well point water saturation at the target time to predict the production dynamic changes of the target block after adding microgel.

7. The reservoir dynamic prediction device based on microgel flooding according to claim 6, characterized in that: The second calculation module includes: An optimization unit, used to perform fitting inversion on the water saturation of the well point using historical oil and water production data of the reservoir, adjust characteristic parameters of the initial connectivity model, and generate an optimized connectivity model; A splitting coefficient calculation unit, used for calculating the water injection splitting coefficient of the water injection well to the oil wells in various directions according to the optimized connectivity model; An initial concentration calculation unit, used to call a preset microgel concentration distribution expression, and calculate the microgel particle phase concentration of the water injection well in the direction of each connected unit at the initial moment based on the water injection splitting coefficient; The concentration prediction unit is used to solve the concentration balance equation according to the microgel particle phase concentration in each connected unit direction at the initial moment, and generate the microgel particle phase concentration in each connected unit direction of the water injection well at the target moment.

8. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the method for predicting reservoir dynamics based on microgel flooding according to any one of claims 1 to 5 is implemented.

9. A terminal, characterized in that: It comprises the computer-readable storage medium and a processor as described in claim 8, and when the processor executes the computer program on the computer-readable storage medium, it implements the steps of the reservoir dynamic prediction method based on microgel flooding as described in any one of claims 1 to 5.

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