An Optimization Method for Inter-stage Injection-Production Reconstruction Based on Displacement Potential Energy Gradient

Abstract

This invention discloses an optimization method for inter-stage injection-production reconstruction based on displacement potential energy gradient guidance, relating to the field of oil and gas field development technology. First, a three-dimensional numerical model of the reservoir is established based on reservoir interpretation data. This model is then used in a reservoir numerical simulator to simulate the global pressure field and fluid velocity variations within the reservoir. A fluid potential field model is then constructed to determine the potential energy gradient between each injection-production unit. The optimal objective is to achieve the most balanced distribution of potential energy gradients among all injection-production units in the fluid potential field model. An objective function is constructed and constraints are set. The objective function is solved using a particle swarm optimization algorithm to optimize the inter-stage injection-production scheme in horizontal wells. This yields the optimal inter-stage injection-production scheme for horizontal wells, used to control the inter-stage injection-production process in horizontal wells. This method achieves quantitative characterization and active control of the reservoir fluid potential field, providing a basis for improving the injected fluid swept volume and oil recovery rate.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, specifically to an optimization method for inter-stage injection-production reconstruction based on displacement potential energy gradient guidance. Background Technology

[0002] With the continued growth in global demand for oil and gas resources, unconventional oil and gas reservoirs have become an important replacement area. Horizontal well multi-stage fracturing technology, as a core technology for developing unconventional oil and gas reservoirs, has greatly increased the drainage area. However, this development method treats a single well as a whole for injection, production, and displacement, resulting in serious inter-stage interference and uneven displacement. At the same time, in order to reduce costs and improve efficiency, this development method has developed a well factory model, which statically optimizes construction parameters such as well spacing, stage spacing, and fracturing scale, but lacks fine control over the dynamic displacement behavior during production.

[0003] In multi-stage fracturing horizontal wells, considering the differences in geomechanics and fracturing operations at different formation locations, the fracture morphology and conductivity formed in each fracturing stage vary. Traditional optimization methods treat the multi-stage fracturing horizontal well as a whole for general control, making it difficult to precisely control the direction of injected water flow, severely limiting the swept volume and energy replenishment effect. In addition, high-yield stages can deplete the flow potential of low-yield stages, creating strong inter-stage interference, resulting in uneven reservoir utilization and preventing the effective utilization of oil and gas in many low-permeability zones or secondary fractures.

[0004] In summary, current methods for inter-stage injection-production control in fracturing are mostly based on production dynamics analysis (such as water breakthrough time and water cut rise rate), combined with experience to determine the injection-production relationships and regimes between wells and stages. However, they lack a key indicator that can globally and quantitatively characterize the overall reservoir displacement equilibrium. Therefore, there is an urgent need to propose an inter-stage injection-production reconstruction optimization method based on displacement potential energy gradient guidance to scientifically and accurately design the injection-production regimes for each well and stage. Summary of the Invention

[0005] To address the issues of insufficient swept volume of injected fluid and inadequate utilization of remaining oil caused by unbalanced potential fields in the development of multi-stage fracturing horizontal wells, this invention proposes an inter-stage injection-production reconstruction optimization method based on displacement potential energy gradient guidance. This method enables quantitative characterization and active control of the reservoir fluid potential field, providing technical support for optimizing inter-stage injection-production schemes in multi-stage fracturing horizontal wells.

[0006] The present invention adopts the following technical solution:

[0007] The optimization method for inter-stage injection-production reconstruction based on displacement potential energy gradient includes the following steps:

[0008] Step 1: Based on reservoir interpretation data, determine the porosity, permeability, and pore throat radius at various locations within the reservoir, establish a three-dimensional numerical model of the reservoir, and use the three-dimensional numerical model of the reservoir to simulate the reservoir development process in a reservoir numerical simulator to obtain the reservoir numerical simulation results and obtain the changes in the global pressure field and fluid velocity within the reservoir during the development process.

[0009] Step 2: Based on the results of reservoir numerical simulation, construct a fluid potential field model for the reservoir water injection development stage, and determine the potential energy gradient between each injection and production unit in the fluid potential field model.

[0010] Step 3: Based on the fluid potential field model of the reservoir water injection development stage, with the optimization objective of the most balanced distribution of potential energy gradient among all injection and production units in the fluid potential field model, construct the objective function and set the constraints.

[0011] Step 4: Solve the objective function based on the particle swarm optimization algorithm, optimize the injection and production scheme between the multi-stage fracturing stages of the horizontal well, and obtain the optimal injection and production scheme between the multi-stage fracturing stages of the horizontal well.

[0012] Step 5: Control the injection and production process device between the multi-stage fractured sections of the horizontal well according to the optimal injection and production scheme between the multi-stage fractured sections of the horizontal well.

[0013] Preferably, in step 1, the coordinates and porosity at each location in the three-dimensional numerical model of the reservoir are set according to the reservoir interpretation data. Penetration rate and throat radius A three-dimensional numerical model of the reservoir was established and imported into a reservoir numerical simulator. After meshing the three-dimensional numerical model, the reservoir development process was simulated using the model. During the simulation, the pressure values ​​at each grid in the three-dimensional numerical model at each time step were obtained, the global pressure field at each time step was acquired, and the fluid velocity at each grid in the three-dimensional numerical model was calculated based on Darcy's law. .

[0014] Preferably, the reservoir interpretation data includes seismic interpretation data, well logging interpretation data, and core data;

[0015] The coordinates of each location in the three-dimensional numerical model of the reservoir are obtained based on the seismic stratigraphic data in the seismic interpretation data. The porosity at each location in the three-dimensional numerical model of the reservoir is set based on the well logging interpretation data. The permeability and pore throat radius at each location in the three-dimensional numerical model of the reservoir are set based on the core analysis results in the core data.

[0016] Preferably, in step 2, the fluid potential field model for the reservoir water injection development stage is:

[0017] ;

[0018] In the formula, For fluid potential field; For potential energy; This is the pressure energy term; It is the kinetic energy term; For interface functions; Density of crude oil; It is the acceleration due to gravity; The depth of the strata; Formation pressure; For fluid velocity; For interfacial tension; For wetting angle; The radius of the throat;

[0019] The fluid potential field model for the reservoir water injection development stage uses each fracturing fracture or each injection-production well formed during fracturing as an injection-production unit, and determines the potential energy gradient between two injection-production units in the fluid potential field model as follows:

[0020] ;

[0021] In the formula, For injection and extraction units With injection and production unit The potential energy gradient between them; For injection and extraction units The fluid potential field; For injection and extraction units The fluid potential field; For injection and extraction units With injection and production unit The distance between them.

[0022] Preferably, in step 3, the objective function is constructed as follows: The goal is to optimize the distribution of the potential energy gradient among all injection and production units in the fluid potential field model to be the most balanced.

[0023] ;

[0024] In the formula, These are the values ​​of the variables used to determine the minimization of the objective function; The objective function is... , All are serial numbers of the injection and extraction units; This represents the total number of injection and production units. To determine the total number of injection-production units by pairing them together, ; This is the average value of the potential energy gradient for all injection and extraction units;

[0025] Determine the decision variable vector for inter-stage injection-production schemes in horizontal well multi-stage fracturing. for:

[0026] ;

[0027] In the formula, For the first Volumetric flow rate of each injection-production unit; When the injection-production unit is called an injection unit, water is injected into the formation. When the injection and production unit is called an oil production unit, oil is extracted from the formation. When this time, it indicates that the injection and extraction unit is closed;

[0028] The objective function is subject to four constraints: total injection volume of the injection unit, total production volume of the production unit, injection volume of the injection unit, and production volume of the production unit.

[0029] The total injection amount constraint of the injection unit is set as follows:

[0030] ;

[0031] In the formula, To optimize the sequence number of the pre-injection unit; To optimize the total number of injected units; To optimize the injection amount in the pre-injection unit; The sequence number of the injected unit after optimization; To optimize the total number of injected cells; To optimize the injection amount of the injection unit;

[0032] The total liquid production constraint of the extraction unit is set as follows:

[0033] ;

[0034] In the formula, For injection-production ratio; This is the sequence number of the sampling unit; This represents the total number of extraction units; The volume of fluid collected by the production unit;

[0035] The injection amount constraint of the injection unit is set as follows:

[0036] ;

[0037] In the formula, This represents the minimum injection amount for the injection unit. This represents the maximum injection volume of the injection unit.

[0038] The fluid collection rate constraint of the production unit is set as follows:

[0039] ;

[0040] In the formula, This is the minimum fluid collection volume for the production unit; This represents the maximum fluid collection rate of the production unit.

[0041] Preferably, step 4 includes the following sub-steps:

[0042] Step 4.1: Set the preset convergence conditions and the total number of particles in the particle swarm optimization.

[0043] Step 4.2: According to the preset constraints, a particle swarm is randomly generated, where each particle in the particle swarm represents a different injection-production scheme between multi-stage fracturing sections of a horizontal well.

[0044] Step 4.3: For each particle in the particle swarm, input the horizontal well multi-stage fracturing inter-stage injection and production scheme represented by the particle into the fluid potential field model. Use the fluid potential field model to calculate the fluid potential field of each injection and production unit under the current horizontal well multi-stage fracturing inter-stage injection and production scheme, obtain the potential energy distribution of the entire reservoir, calculate the root mean square error of the potential energy gradient of the entire field and use it as the fitness of the particle.

[0045] Step 4.4: Based on the current mean square error of the full-field potential energy gradient and the current iteration number, determine whether the preset convergence condition has been met. If the preset convergence condition has been met, proceed to step 4.5. Otherwise, adjust the flight direction and step size of each particle in the particle swarm and return to step 4.3 to continue using the particles in the particle swarm to find the objective function.

[0046] Step 4.5: Output the injection and production scheme between horizontal well multi-stage fracturing stages represented by the globally optimal particle in the particle swarm, and determine the optimal injection and production scheme between horizontal well multi-stage fracturing stages.

[0047] Preferably, in step 4.4, the preset convergence condition is that the mean square error of the full-field potential energy gradient is less than a preset mean square error threshold and the preset maximum number of iterations has been reached. If the current mean square error of the full-field potential energy gradient is less than the preset mean square error threshold or the current number of iterations has reached the preset maximum number of iterations, then it is determined that the current iteration calculation has met the preset convergence condition; otherwise, it is determined that the current generation calculation has not met the preset convergence condition.

[0048] The present invention has the following beneficial effects:

[0049] This invention proposes an optimization method for inter-stage injection-production reconstruction based on displacement potential energy gradient guidance. By constructing a fluid potential field model for the reservoir water injection development stage, the potential energy gradient distribution between each injection-production unit in the fluid potential field model is balanced, effectively suppressing dominant crossflow within the reservoir during development. This successfully solves the problems of limited injected water sweep volume and insufficient utilization of remaining oil caused by potential field imbalance in multi-stage fracturing horizontal well development, achieving fine control from the "well" scale to the "fracture" scale. By optimizing the inter-stage injection-production scheme of multi-stage fracturing horizontal wells, the injected fluid sweep volume and oil recovery rate of the reservoir are significantly improved. Attached Figure Description

[0050] Figure 1 This is a flowchart of an optimization method for inter-stage injection-production reconstruction based on displacement potential energy gradient guidance according to the present invention.

[0051] Figure 2 This is a flowchart illustrating the process of determining the optimal injection and production scheme between multi-stage fracturing sections in a horizontal well based on the particle swarm optimization algorithm of this invention.

[0052] Figure 3 This is a schematic diagram of the initial potential field distribution in a low-permeability reservoir.

[0053] Figure 4 A schematic diagram of the potential field distribution of the optimized low-permeability reservoir.

[0054] Figure 5 To optimize the water cut change curves of low-permeability reservoirs before and after.

[0055] Figure 6 A comparison chart of the injection and production fluid volumes of each injection and production unit before and after optimization.

[0056] Figure 7 A comparison chart of water content in each injection and production unit before and after optimization. Detailed Implementation

[0057] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0058] Example 1

[0059] This invention proposes an optimization method for inter-stage injection-production reconstruction based on displacement potential energy gradient, which specifically includes the following steps:

[0060] Step 1: Based on reservoir interpretation data, determine the porosity, permeability, and pore throat radius at various locations within the reservoir, establish a three-dimensional numerical model of the reservoir, and use the three-dimensional numerical model to simulate the reservoir development process in a reservoir numerical simulator to obtain the reservoir numerical simulation results and acquire the changes in the global pressure field and fluid velocity within the reservoir during the development process.

[0061] Specifically, the reservoir interpretation data includes seismic interpretation data, well logging interpretation data, and core data. A three-dimensional numerical model of the reservoir is established. The coordinates of each location in the three-dimensional numerical model are obtained based on the seismic stratigraphic data in the seismic interpretation data. The porosity at each location in the three-dimensional numerical model is set based on the well logging interpretation data. Based on the core analysis results from the core data, the permeability at various locations in the three-dimensional numerical model of the reservoir is set. and throat radius .

[0062] The three-dimensional numerical model of the reservoir was imported into the reservoir numerical simulator. After meshing the three-dimensional numerical model, the reservoir development process was simulated using the model. During the simulation, the pressure values ​​at each grid in the three-dimensional numerical model were obtained at each time step, and the global pressure field at each time step was obtained. Based on Darcy's law, the fluid velocity at each grid in the three-dimensional numerical model was calculated. , used to calculate the kinetic energy term of a fluid.

[0063] In conjunction with mercury intrusion porosimetry experiments on representative core samples from the core data, the pore throat radius of each representative core sample was obtained. By performing multivariate regression analysis on the pore throat radius of each representative core sample with porosity and permeability, the quantitative relationship between the pore throat radius and porosity and permeability was obtained, and the formula for calculating the pore throat radius was obtained, which is used to calculate the interfacial energy of the fluid.

[0064] The formula for calculating the throat radius is as follows:

[0065] ;

[0066] In the formula, , , All are regression analysis coefficients.

[0067] The pore throat radius can be determined at any location in the three-dimensional numerical model of the reservoir using the formula for calculating the pore throat radius, and the calculated pore throat radius can be directly applied to the subsequently established fluid potential field model.

[0068] Step 2: In order to accurately characterize fluid migration in the reservoir, a fluid potential field model for the reservoir water injection development stage is constructed based on the results of reservoir numerical simulation, and the potential energy gradient between each injection and production unit in the fluid potential field model is determined.

[0069] Furthermore, the fluid potential field model is used to determine the fluid potential, i.e., the sum of fluid mechanical energy, including potential energy, pressure energy, kinetic energy, and interface energy. The potential energy term is used to characterize the potential energy generated by the fluid due to its altitude, the pressure energy term is used to characterize the potential energy generated by the fluid due to formation pressure, the kinetic energy term is used to characterize the kinetic energy generated by the fluid due to its own flow velocity, and the interface energy term is used to characterize the capillary force generated in porous media due to the combined action of interfacial tension, wettability, and pore throat radius of the rock-fluid system.

[0070] Specifically, the fluid potential field model for the reservoir water injection development stage is as follows:

[0071] ;

[0072] In the formula, The fluid potential field, in units of ; Potential energy, unit: ; This is the pressure energy term, with units of... ; This is the kinetic energy term, with units of 1. ; Interface energy, unit: ; Crude oil density, in units of ; This is the acceleration due to gravity, in units of 1. ; The depth is expressed in units of 1000 ppm. ; Formation pressure, unit: ; For fluid velocity, the unit is . ; Interfacial tension, unit: ; The wetting angle is expressed in units of 100°C. ; The throat radius is given in units of . .

[0073] The fluid potential field model for the reservoir water injection development stage uses each fracturing fracture or each injection-production well formed during fracturing as an injection-production unit, and determines the potential energy gradient between two injection-production units in the fluid potential field model as follows:

[0074] ;

[0075] In the formula, For injection and extraction units With injection and production unit The potential energy gradient between them, the larger the potential energy gradient, the higher the driving intensity, and the more the fluid tends to flow in this channel; For injection and extraction units The fluid potential field; For injection and extraction units The fluid potential field; For injection and extraction units With injection and production unit The distance between them.

[0076] Step 3: Based on the fluid potential field model of the reservoir water injection development stage, with the optimization objective of achieving the most balanced distribution of potential energy gradients among all injection and production units in the fluid potential field model, construct the objective function and set the constraints.

[0077] Furthermore, the optimization objective is to achieve the most balanced distribution of potential energy gradients among all injection and production units in the fluid potential field model, which means that the distribution of potential energy gradients among all injection and production unit pairs in the entire field reaches the most balanced state.

[0078] Specifically, the objective function is:

[0079] ;

[0080] In the formula, These are the values ​​of the variables used to determine the minimization of the objective function; The objective function is... , All are serial numbers of the injection and extraction units; This represents the total number of injection and production units. To determine the total number of injection-production units by pairing them together, ; This is the average value of the potential energy gradient for all injection and production units.

[0081] Determine the decision variable vector for inter-stage injection-production schemes in horizontal well multi-stage fracturing. for:

[0082] ;

[0083] In the formula, For the first Volumetric flow rate of each injection / production unit, in units of ; When the injection-production unit is called an injection unit, water is injected into the formation. When the injection and production unit is called an oil production unit, oil is extracted from the formation. When this time, it indicates that the injection and extraction unit is closed.

[0084] The objective function has four constraints: the total injection volume constraint of the injection unit, the total production volume constraint of the production unit, the injection volume constraint of the injection unit, and the production volume constraint of the production unit. The total injection volume constraint of the injection unit is set as follows:

[0085] ;

[0086] In the formula, To optimize the sequence number of the pre-injection unit; To optimize the total number of injected units; To optimize the injection amount in the pre-injection unit, the unit is... ; The sequence number of the injected unit after optimization; To optimize the total number of injected cells; To optimize the injection amount of the post-injection unit, the unit is... .

[0087] The total liquid production constraint of the extraction unit is set as follows:

[0088] ;

[0089] In the formula, The injection-production ratio is determined based on existing technologies such as water drive characteristic curves, material balance methods, or Logistic model methods. This is the sequence number of the sampling unit; This represents the total number of extraction units; The volume of fluid collected by the production unit is expressed in units of... .

[0090] The injection amount constraint of the injection unit is set as follows:

[0091] ;

[0092] In the formula, The minimum injection amount for the injection unit, in units of ; The maximum injection volume of the injection unit, in units of .

[0093] The fluid collection rate constraint of the production unit is set as follows:

[0094] ;

[0095] In the formula, The minimum fluid collection volume for the production unit, in units of ; The maximum fluid recovery rate of the production unit, in units of .

[0096] Step 4: Solve the objective function using the particle swarm optimization algorithm to optimize the injection-production scheme between multi-stage fracturing stages in horizontal wells, obtaining the optimal injection-production scheme between multi-stage fracturing stages in horizontal wells, such as... Figure 2 As shown, the specific steps include the following:

[0097] Step 4.1: Set the preset convergence conditions and the total number of particles in the particle swarm optimization.

[0098] Step 4.2: Based on preset constraints, a particle swarm is randomly generated, where each particle in the particle swarm represents a different injection-production scheme between multi-stage fracturing sections in a horizontal well.

[0099] Step 4.3: For each particle in the particle swarm, input the horizontal well multi-stage fracturing inter-stage injection and production scheme represented by the particle into the fluid potential field model. Use the fluid potential field model to calculate the fluid potential field of each injection and production unit under the current horizontal well multi-stage fracturing inter-stage injection and production scheme, obtain the potential energy distribution of the entire reservoir, calculate the root mean square error of the potential energy gradient of the entire field and use it as the fitness of the particle.

[0100] Step 4.4: Based on the current mean square error of the global potential energy gradient and the current iteration number, determine whether the preset convergence condition has been met. If the preset convergence condition has been met, proceed to step 4.5. Otherwise, adjust the flight direction and step size of each particle in the particle swarm and return to step 4.3 to continue using the particles in the particle swarm to find the objective function.

[0101] In this embodiment, the preset convergence condition is that the mean square error of the full-field potential energy gradient is less than the preset mean square error threshold and the preset maximum number of iterations has been reached. If the current mean square error of the full-field potential energy gradient is less than the preset mean square error threshold or the current number of iterations has reached the preset maximum number of iterations, then it is determined that the current iteration calculation has met the preset convergence condition; otherwise, it is determined that the current generation calculation has not met the preset convergence condition.

[0102] Step 4.5: Output the injection and production scheme between horizontal well multi-stage fracturing stages represented by the globally optimal particle in the particle swarm, and determine the optimal injection and production scheme between horizontal well multi-stage fracturing stages.

[0103] Step 5: Control the injection and production process device between the multi-stage fractured sections of the horizontal well according to the optimal injection and production scheme between the multi-stage fractured sections of the horizontal well.

[0104] Specifically, control commands are generated based on the optimal injection and production scheme between the multi-stage fracturing sections of a horizontal well. These commands are then sent to the injection and production process devices (such as downhole intelligent injectors and in-well injection and production packers) through the control system. This allows for precise control of the flow rate of the injected fluid into each target layer or fracture segment downhole, based on the optimal injection and production scheme between the multi-stage fracturing sections of a horizontal well, thereby achieving efficient reservoir development.

[0105] Example 2

[0106] This embodiment applies the fracturing-inter-stage injection-production reconstruction optimization method based on displacement potential energy gradient guidance described in Example 1 to a low-permeability oil reservoir block. The main reservoir parameters of this low-permeability oil reservoir block include a porosity of 10.1% and a permeability of [missing information]. The average pore diameter is 2.28 mm. A three-dimensional numerical model of the reservoir was established based on reservoir interpretation data of the low-permeability reservoir block. The model includes six vertical wells and two horizontal wells. Vertical wells J1, J2, J3, J4, J5, and J6 are water injection wells. Fractured sections were formed in horizontal wells H1 and H2. Ten fractures from the six vertical wells and two horizontal wells were defined as injection-production units, resulting in a total of 16 injection-production units. The injection-production regime adopted by each unit is shown in Table 1. The development process of the low-permeability reservoir was simulated in a reservoir numerical simulator using the three-dimensional numerical model. Under the initial horizontal well multi-stage fracturing injection-production scheme, the initial potential field distribution of the low-permeability reservoir was calculated, as shown in Table 1. Figure 3 As shown, the initial root mean square error of the overall potential energy gradient was calculated to be 0.85. Under the premise that the total injection and production volume remains constant and within the reasonable injection and production parameter limits, after 500 iterations of optimization calculations based on the particle swarm optimization algorithm to optimize the inter-stage fracturing injection and production scheme of the horizontal well, the potential field distribution of the low-permeability reservoir is as follows: Figure 4 As shown, the mean square error of the potential energy gradient across the entire field decreased to 0.32, satisfying the preset convergence condition. The optimization of the injection and production scheme between the multi-stage fracturing sections of the horizontal well was stopped, and the optimal injection and production scheme between the multi-stage fracturing sections of the horizontal well was obtained. The optimized injection and production regime of each injection and production unit was obtained, as shown in Table 1.

[0107] Table 1. Injection and production regimes for each injection and production unit before and after optimization.

[0108] .

[0109] By comparing the injection and production regimes of each injection and production unit before and after optimization in Table 1, the second and fourth fractured sections of horizontal well H1 and the second and fourth fractured sections of horizontal well H2 were changed from oil production to water injection after optimization. The development of low-permeability reservoirs was predicted based on the initial horizontal well multi-stage fracturing inter-section injection and production scheme and the optimal horizontal well multi-stage fracturing inter-section injection and production scheme, respectively. The water cut change curves of the low-permeability reservoirs before and after optimization were obtained, as shown below. Figure 5 As shown, by Figure 5 It can be seen that after adopting the optimal injection-production scheme between horizontal well multi-stage fracturing stages, the water cut during the development of low-permeability reservoirs decreased significantly. Further comparison of the injection and production fluid volume and water cut of each injection-production unit before and after optimization shows that... Figure 6 and Figure 7As shown in the comparison, it can be seen that reducing injection in injection units H2-1, J3-1, and J6-1 reduces ineffective circulation of injected water in high water-cut areas. For injection units J1-1 and J5-1, increasing the output improves edge displacement. After transferring injection to injection units H1-2, H1-4, H2-2, and H2-4, the output of injection units H1-3, H2-3, and H2-5 is significantly increased, and the water content of injection units H1-1, H1-3, H1-5, H2-1, H2-3, and H2-5 decreases substantially.

[0110] In summary, the optimal injection-production scheme between horizontal well multi-stage fracturing sections determined by the method of this invention can effectively improve the balanced displacement between wells and fractures, achieving increased oil production and reduced water content. Furthermore, by using the optimal injection-production scheme between horizontal well multi-stage fracturing sections determined by this invention for inter-section injection-production reconstruction optimization, the crude oil utilization effect in this low-permeability reservoir block can be effectively improved, the increase in water cut in the low-permeability reservoir can be suppressed, and the development life of the oilfield can be extended.

[0111] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. An optimization method for inter-stage injection-production reconstruction based on displacement potential energy gradient, characterized in that, Includes the following steps: Step 1: Based on reservoir interpretation data, determine the porosity, permeability, and pore throat radius at various locations within the reservoir, establish a three-dimensional numerical model of the reservoir, and use the three-dimensional numerical model of the reservoir to simulate the reservoir development process in a reservoir numerical simulator to obtain the reservoir numerical simulation results and obtain the changes in the global pressure field and fluid velocity within the reservoir during the development process. Step 2: Based on the results of reservoir numerical simulation, construct a fluid potential field model for the reservoir water injection development stage, and determine the potential energy gradient between each injection and production unit in the fluid potential field model. Step 3: Based on the fluid potential field model of the reservoir water injection development stage, with the optimization objective of the most balanced distribution of potential energy gradient among all injection and production units in the fluid potential field model, construct the objective function and set the constraints. Step 4: Solve the objective function based on the particle swarm optimization algorithm, optimize the injection and production scheme between the multi-stage fracturing stages of the horizontal well, and obtain the optimal injection and production scheme between the multi-stage fracturing stages of the horizontal well. Step 5: Control the injection and production process device between the multi-stage fractured sections of the horizontal well according to the optimal injection and production scheme between the multi-stage fractured sections of the horizontal well.

2. The optimization method for inter-stage injection-production reconstruction based on displacement potential energy gradient as described in claim 1, characterized in that, In step 1, the coordinates and porosity at each location in the three-dimensional numerical model of the reservoir are set based on reservoir interpretation data. Penetration rate and throat radius A three-dimensional numerical model of the reservoir was established and imported into a reservoir numerical simulator. After meshing the three-dimensional numerical model, the reservoir development process was simulated using the model. During the simulation, the pressure values ​​at each grid in the three-dimensional numerical model at each time step were obtained, the global pressure field at each time step was acquired, and the fluid velocity at each grid in the three-dimensional numerical model was calculated based on Darcy's law. .

3. The optimization method for inter-stage injection-production reconstruction based on displacement potential energy gradient as described in claim 2, characterized in that, The reservoir interpretation data includes seismic interpretation data, well logging interpretation data, and core data; The coordinates of each location in the three-dimensional numerical model of the reservoir are obtained based on the seismic stratigraphic data in the seismic interpretation data. The porosity at each location in the three-dimensional numerical model of the reservoir is set based on the well logging interpretation data. The permeability and pore throat radius at each location in the three-dimensional numerical model of the reservoir are set based on the core analysis results in the core data.

4. The optimization method for inter-stage injection-production reconstruction based on displacement potential energy gradient as described in claim 1, characterized in that, In step 2, the fluid potential field model for the reservoir water injection development stage is as follows: ； In the formula, For the fluid potential field; For potential energy; This is the pressure energy term; It is the kinetic energy term; For interface functions; Density of crude oil; It is the acceleration due to gravity; The depth of the strata; Formation pressure; For fluid velocity; For interfacial tension; For wetting angle; The radius of the throat; The fluid potential field model for the reservoir water injection development stage uses each fracturing fracture or each injection-production well formed during fracturing as an injection-production unit, and determines the potential energy gradient between two injection-production units in the fluid potential field model as follows: ； In the formula, For injection and extraction units With injection and production unit The potential energy gradient between them; For injection and extraction units The fluid potential field; For injection and extraction units The fluid potential field; For injection and extraction units With injection and production unit The distance between them.

5. The inter-stage injection-production reconstruction optimization method based on displacement potential energy gradient guidance according to claim 4, characterized in that, In step 3, the objective function is to optimize the distribution of the potential energy gradient among all injection and production units in the fluid potential field model to be the most balanced. ； In the formula, These are the values ​​of the variables used to determine the minimization of the objective function; The objective function is... , All are serial numbers of the injection and extraction units; This represents the total number of injection and production units. To determine the total number of injection-production units by pairing them together, ; This is the average value of the potential energy gradient for all injection and extraction units; Determine the decision variable vector for inter-stage injection-production schemes in horizontal well multi-stage fracturing. for: ； In the formula, For the first Volumetric flow rate of each injection-production unit; When the injection-production unit is called an injection unit, water is injected into the formation. When the injection and production unit is called an oil production unit, oil is extracted from the formation. When this time, it indicates that the injection and extraction unit is closed; The objective function is subject to four constraints: total injection volume of the injection unit, total production volume of the production unit, injection volume of the injection unit, and production volume of the production unit. The total injection amount constraint of the injection unit is set as follows: ； In the formula, To optimize the sequence number of the pre-injection unit; To optimize the total number of pre-injection units; To optimize the injection amount in the pre-injection unit; The sequence number of the injected unit after optimization; The total number of injected cells after optimization; To optimize the injection amount of the injection unit; The total liquid production constraint of the extraction unit is set as follows: ； In the formula, For injection-production ratio; This is the sequence number of the sampling unit; This represents the total number of extraction units; The volume of fluid collected by the production unit; The injection amount constraint of the injection unit is set as follows: ； In the formula, This represents the minimum injection amount for the injection unit. This represents the maximum injection volume of the injection unit; The fluid collection rate constraint of the production unit is set as follows: ； In the formula, This is the minimum fluid collection volume for the production unit; This represents the maximum fluid collection rate of the production unit.

6. The optimization method for inter-stage injection-production reconstruction based on displacement potential energy gradient as described in claim 5, characterized in that, Step 4 includes the following sub-steps: Step 4.1: Set the preset convergence conditions and the total number of particles in the particle swarm optimization. Step 4.2: According to the preset constraints, a particle swarm is randomly generated, where each particle in the particle swarm represents a different injection-production scheme between multi-stage fracturing sections of a horizontal well. Step 4.3: For each particle in the particle swarm, input the horizontal well multi-stage fracturing inter-stage injection and production scheme represented by the particle into the fluid potential field model. Use the fluid potential field model to calculate the fluid potential field of each injection and production unit under the current horizontal well multi-stage fracturing inter-stage injection and production scheme, obtain the potential energy distribution of the entire reservoir, calculate the root mean square error of the potential energy gradient of the entire field and use it as the fitness of the particle. Step 4.4: Based on the current mean square error of the full-field potential energy gradient and the current iteration number, determine whether the preset convergence condition has been met. If the preset convergence condition has been met, proceed to step 4.

5. Otherwise, adjust the flight direction and step size of each particle in the particle swarm and return to step 4.3 to continue using the particles in the particle swarm to find the objective function. Step 4.5: Output the injection and production scheme between horizontal well multi-stage fracturing stages represented by the globally optimal particle in the particle swarm, and determine the optimal injection and production scheme between horizontal well multi-stage fracturing stages.

7. The optimization method for inter-stage injection-production reconstruction based on displacement potential energy gradient as described in claim 6, characterized in that, In step 4.4, the preset convergence condition is that the mean square error of the full-field potential energy gradient is less than the preset mean square error threshold and the preset maximum number of iterations has been reached. If the mean square error of the current full-field potential energy gradient is less than the preset mean square error threshold or the current number of iterations has reached the preset maximum number of iterations, then it is determined that the current iteration calculation has met the preset convergence condition; otherwise, it is determined that the current generation calculation has not met the preset convergence condition.

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