A well location optimization method and system based on displacement balance analysis

Abstract

The present invention provides a well location optimization method and system based on displacement balance analysis. This method, based on the establishment of a reservoir streamline simulation model, divides the reservoir into production potential and water injection potential based on the model grid parameters, sets the initial values ​​of the well layout parameters by set strategy parameters, forms multiple initial well layout plans, and then calls the streamline simulator in parallel to calculate the fitness value of each initial well layout plan, implements an evaluation based on the displacement uniformity, selects the initial global optimal well layout plan, uses the improved particle swarm algorithm to iteratively calculate the optimization parameters, and determines the final well layout plan based on the set iterative calculation plan. The above-mentioned scheme can solve the problems of low computational efficiency, poor convergence, many invalid plans, and long numerical simulation time in the existing well layout optimization. While ensuring computational efficiency, it can obtain the best well layout plan that is compatible with the actual reservoir, and optimize the reservoir development effect based on the reasonable well layout plan.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas field development, in particular to a well placement optimization method in the oil field development process, and more particularly to a new well placement optimization method and system based on displacement balance degree analysis. Background Art

[0002] In oil and gas field exploration and development, reliable research and practical applications have proven that due to the heterogeneity of reservoirs and fluids, the oil and gas production and production efficiency of wells drilled in different locations in the reservoir vary. Therefore, when developing oil and gas fields, one of the main challenges facing reservoir personnel (such as engineers) is how to develop the optimal well layout plan to maximize recovery and achieve efficient development of the reservoir.

[0003] Currently, there are two main types of well location design methods: traditional well location design methods and automated well location optimization methods based on optimization theory. Traditional well location design methods, based on reservoir engineering methods, utilize numerical simulation technology to select locations within a set reservoir range with high oil saturation, thick layers, and proximity to marginal and bottom water for well placement. This well placement approach suffers from high randomness and a dominant factor of human experience. Decisions made are inevitably influenced by data ambiguity and the sheer volume of information, leading to errors. This makes it difficult to deploy optimal well locations and fails to meet the technical requirements of oil and gas well development.

[0004] For automated well placement optimization methods based on optimization theory, the main steps in well placement are based on optimization theory. First, an objective function (such as net present value, oil production, and recovery degree) is set. Using reservoir numerical simulation as a calculation tool, the optimization algorithm updates well placement parameters (such as well location coordinates) according to specific rules. Through iterative calculations, the objective function's value is optimized. While this method can reduce the influence of human factors, the calculation process produces a large number of invalid well placement plans. Furthermore, the method lacks support from reservoir engineering theory, resulting in low optimization efficiency and long computation time. These drawbacks are particularly prominent when the reservoir model is large, making this well placement method difficult to apply in actual reservoir development.

[0005] Based on the above problems, researchers in the field have also conducted research on optimizing well layout methods, such as the patent number CN201710990712.6 "Optimal Well Location Deployment Method and Device for Small Fault Block Reservoirs" and the document "Application of Genetic Algorithm in Well Location Optimization in Sulige Gas Field".

[0006] The former method, based on the initial well locations, adds injection and production units one by one and optimizes the locations of these newly added units until the recovery factor or net present value no longer increases. This means that each optimized injection and production unit must be calculated using an optimization algorithm, which results in lengthy computational times. Determining the final well placement plan requires multiple rounds of optimization calculations, which consumes significant computational time and computer resources. Consequently, this method cannot be applied to large-scale reservoir development. The latter method utilizes a genetic algorithm in the well placement optimization process. In addition to well location parameters, the optimization variables also consider the controlled area of ​​each well. The more optimization variables there are, the greater the computational cost and computational time required to obtain the optimal solution. Furthermore, the solution provided only considers well location variables, failing to reflect the relationship between oil and gas production parameters and well placement. This fails to guarantee optimal performance at the selected well locations and fails to provide accurate guidance on well placement strategies for reservoir operators. Summary of the Invention

[0007] To solve the above problems, the present invention provides a well location optimization method based on displacement balance analysis. In one embodiment, the method includes:

[0008] Simulation model construction steps: building a three-dimensional digital reservoir geological model based on the set reservoir geological data, and integrating it with the set dynamic production data to build a reservoir flow simulation model of the target reservoir area;

[0009] The well area division step includes calculating the production potential data and water injection potential data of each grid in the target reservoir area based on the established three-dimensional digital reservoir geological model and the set strategy according to the grid parameters, and selecting the initial well area based on the data;

[0010] An initial plan generation step includes setting initial values ​​of well placement parameters and potential requirements based on the set reservoir data and development demand data, and fitting and adjusting the well placement parameters and potential requirements based on the set plan in the initial well placement area to form an initial well placement plan corresponding to each well; wherein the well placement parameters include wellhead coordinates, wellbore length, wellbore azimuth, completion interval, and well working system data;

[0011] Parallel simulation calculation steps, parallel operation of the reservoir streamline simulation model to perform streamline simulation calculations on each initial well layout plan, obtain fitness data representing the degree of balance of the reservoir area, and select the optimal initial well layout plan corresponding to each well;

[0012] The parameter optimization step uses the set improved particle swarm algorithm to optimize the well layout parameters of each optimal initial well layout plan, iteratively generates a series of optimized well layout plans and updates the optimal initial well layout plan based on the optimized well layout plan, evaluates each optimized well layout plan based on fitness data that characterizes the degree of balance in the reservoir area, and determines the final target well layout plan based on the evaluation results.

[0013] Preferably, in one embodiment, the method further comprises: after establishing the reservoir streamline simulation model:

[0014] Obtain reservoir fluid parameters and rock physical parameters through experiments, determine reservoir initial conditions and corresponding oil test data and production test data;

[0015] The corresponding experimental reservoir flow simulation model is constructed based on the reservoir geological data and dynamic production data corresponding to the experiment, and the flow simulation calculation of oil, gas and water in the experimental reservoir is carried out based on the streamline simulation model;

[0016] The simulation results are compared with the experimentally measured historical oil and gas production data to ensure that the operating status of the constructed experimental reservoir flow line simulation model meets the set conditions.

[0017] Furthermore, in one embodiment, the well area division step further includes:

[0018] The calculated production potential data and water injection potential data are stored in two three-dimensional arrays to realize the digitization of reservoir potential. The three-dimensional potential arrays are vertically summed and normalized to form a two-dimensional potential map corresponding to each three-dimensional potential data.

[0019] In one embodiment, the initial solution generation step includes:

[0020] Set the initial values ​​of well placement parameters and potential requirements based on the geological characteristics, reserve size, fluid properties, expected recovery factor, and expected oil production rate of the target reservoir area;

[0021] Using the normalized two-dimensional production potential map, based on the well spacing constraint, grids are randomly selected in the initial well layout area to generate a corresponding number of reservoir wellhead coordinates in sequence. The potential requirements are adjusted according to the matching of the number of generated wellhead coordinates with the corresponding well layout parameter values ​​in the initial well layout plan until the matching of the two meets the set conditions. The initial data values ​​of each well layout parameter that meets the conditions in the target reservoir area are taken as the initial well layout plan.

[0022] Specifically, in one embodiment, the parallel simulation calculation step includes:

[0023] The grid cumulative flow capacity data and grid cumulative storage capacity data of each well layout plan are calculated based on the flight time in the streamline simulation result file. The Lorentz coefficient of each oil well layout plan is then obtained as the corresponding fitness value. The well layout plan corresponding to the minimum Lorentz coefficient is taken as the optimal initial well layout plan.

[0024] Furthermore, in one embodiment, the parallel simulation calculation step further includes:

[0025] The well layout parameters in the optimal initial well layout plan are stored to form a global optimal array for subsequent retrieval for comparative analysis and update. During the update, the entire set of well layout parameters is updated by replacement so that the historically optimal well layout parameter values ​​are always retained in the array.

[0026] In the parameter optimization step, the velocity update formula and position update formula of the improved particle swarm algorithm are set based on the principles of adjusting the inertia weight, increasing information exchange between particles, and expanding the search space. The well layout parameters of the optimized well layout plan are calculated by combining the two.

[0027] Specifically, in one embodiment, the process of evaluating each optimized well layout plan based on fitness data includes:

[0028] The constructed reservoir streamline simulation model is used to calculate the fitness values ​​corresponding to each optimized well layout scheme, and the calculated results are compared and analyzed with the fitness values ​​of the well layout parameters in the known global optimal array to obtain the evaluation results of the current optimized well layout scheme. Until the evaluation results meet the set final well layout conditions, the optimal well layout scheme obtained will be used as the final target well layout scheme.

[0029] In an optional embodiment, the well layout parameters also include well tail coordinates. In the initial plan generation step, a random function is used to select well layout trajectory parameters that meet the set trajectory conditions in the corresponding initial well layout area, and based on them combined with the coordinates of each well head, the matching effective well tail coordinates are determined, and together with the well head coordinates, they represent the well type data of the initial well layout plan.

[0030] Furthermore, in one embodiment, the well layout parameters also include perforation layer data. In the initial plan generation step, based on the selected well layout trajectory parameters and combined with the production potential data of each well layout plan, the perforation layer data of each initial well layout plan is determined using an inverse distance weighted algorithm.

[0031] In one embodiment, the well layout parameters also include production system parameters. In the initial plan generation step, the total production and injection volumes of the target reservoir area are calculated based on the geological reserves of the reservoir, the expected oil production rate, and the expected injection-production ratio. Then, based on the production potential data corresponding to each initial well layout plan and the calculated total injection-production volume, the corresponding production system parameters are determined.

[0032] Based on other aspects of any one or more of the above embodiments, the present invention further provides a well location optimization system based on displacement balance analysis, which executes the method as described in any one or more of the above embodiments.

[0033] Compared with the closest prior art, the present invention also has the following beneficial effects:

[0034] The present invention provides a well placement optimization method based on displacement balance analysis. Based on the establishment of a reservoir streamline simulation model, the reservoir streamline simulation model of the target reservoir area is constructed by integrating dynamic production data. The streamline simulation can quickly evaluate the displacement balance of the well layout plan, significantly reducing calculation time. In addition, the streamline simulation method has strong stability, significantly reducing calculation time while ensuring the reliability of the displacement balance evaluation.

[0035] In addition, the solution of the present invention introduces the constraint capability of production potential and water injection potential, reasonably constrains the initial well layout area, analyzes specific reservoirs, divides the production potential and water injection potential in the reservoirs, thereby clarifying the scope of well layout, enhancing the targeted well layout, significantly reducing ineffective well layout plans, improving well layout quality, and further enhancing the implementation efficiency of the well layout project;

[0036] Furthermore, the solution of the present invention utilizes an improved particle swarm optimization algorithm to optimize well layout and clearly find the optimal well location plan. Compared with the optimization algorithms in the prior art, the present invention combines streamline simulation with optimization theory, automatically adjusts the values ​​of optimization parameters, and can quickly generate a large number of reasonable alternatives, providing reliable intermediate data support for ultimately obtaining the optimal well layout parameters suitable for the oil reservoir. Based on this, the well layout optimization method of the present invention can not only solve the problems of low computational efficiency, poor convergence, many invalid solutions, and excessively long numerical simulation time in the existing well layout optimization, but also obtain the best well layout plan that is suitable for the actual oil reservoir while ensuring computational efficiency, so that the well layout plan not only well matches the formation characteristics of the target oil reservoir, but also can be better applied to actual production.

[0037] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0039] Figure 1 1 is a flow chart of a well location optimization method based on displacement balance analysis provided by one embodiment of the present invention;

[0040] Figure 2 1 is a schematic diagram of a process flow for generating an initial well layout plan for a well location optimization method based on displacement balance analysis in another embodiment of the present invention;

[0041] Figure 3 1 is a schematic diagram of the well placement process of multiple wells in a single solution of the well placement optimization method based on displacement balance analysis in an embodiment of the present invention;

[0042] Figure 4 It is a mathematical representation display diagram of a vertical well, a deviated well, and a horizontal well provided in one embodiment of the present invention;

[0043] Figure 5 is a schematic diagram of a well passing through a grid in a simulator in a well location optimization method based on displacement balance analysis provided by an embodiment of the present invention;

[0044] Figure 6 1 is a schematic diagram of potential evaluation based on inverse distance weighting of a well location optimization method in one embodiment of the present invention;

[0045] Figure 7 is a schematic diagram of the perforation layer positions of a well in a well location optimization method based on displacement balance analysis provided by another embodiment of the present invention;

[0046] Figure 8 is a Lorenz curve diagram of cumulative storage capacity-cumulative flow capacity of the well location optimization method based on displacement balance analysis in an embodiment of the present invention;

[0047] Figure 9 is an oil saturation distribution diagram of the PUNQ-S3 reservoir model in an embodiment of the present invention;

[0048] Figure 10 is a permeability distribution map of the PUNQ-S3 reservoir in one embodiment of the present invention;

[0049] Figure 11 is a phase permeability curve diagram of the PUNQ-S3 reservoir provided in an embodiment of the present invention;

[0050] Figure 12 This is an example of importing an initial file for a well location optimization method based on displacement balance analysis in another embodiment of the present invention;

[0051] Figure 13 1 is a schematic diagram of the potential evaluation of the PUNQ-S3 oil reservoir based on the well location optimization method of displacement balance analysis in an embodiment of the present invention;

[0052] Figure 14This is a diagram illustrating the iterative optimization process of a well location optimization method based on displacement balance analysis in one embodiment of the present invention;

[0053] Figure 15 Schematic diagram of changes in NPV during the well layout parameter optimization process provided in an embodiment of the present invention;

[0054] Figure 16 is a comparison chart of the recovery degree under different constraints provided by the well location optimization method in an embodiment of the present invention;

[0055] Figure 17 is a comparison chart of NPV changes under different constraints in a well location optimization method provided by another embodiment of the present invention;

[0056] Figure 18 This is the well location deployment diagram and remaining oil distribution diagram of the original well layout plan provided by the embodiment of the present invention

[0057] Figure 19 It is a well location deployment diagram and a remaining oil distribution diagram of a well location optimization method based on displacement balance analysis in one embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following will describe in detail the implementation methods of the present invention in conjunction with the accompanying drawings and embodiments, so that practitioners of the present invention can fully understand how the present invention applies technical means to solve technical problems and achieve the implementation process of technical effects, and can implement the present invention in accordance with the above implementation process. It should be noted that as long as no conflict exists, the various embodiments and various features of each embodiment in the present invention can be combined with each other, and the resulting technical solutions are all within the scope of protection of the present invention.

[0059] Due to the heterogeneity of reservoirs and fluids, well production and efficiency vary depending on the reservoir location. Therefore, one of the key challenges facing reservoir engineers when developing oil and gas fields is developing the optimal well layout to maximize recovery and achieve efficient reservoir development.

[0060] There are currently two main types of well location design methods: traditional well location design methods and automatic well location optimization methods based on optimization theory.

[0061] Traditional well placement planning relies primarily on reservoir engineering methods, using numerical simulations to locate wells in locations with high oil saturation, thick formations, and distance from marginal or bottom water. This approach suffers from high randomness and a dominant influence of human experience. Decisions made are inevitably influenced by data ambiguity and the sheer volume of information, leading to errors and making it difficult to deploy optimal well locations.

[0062] For automated well placement optimization methods based on optimization theory, the main steps in well placement are based on optimization theory. First, an objective function (such as net present value, oil production, and recovery rate) is set. Using reservoir numerical simulation as a calculation tool, the optimization algorithm updates well placement parameters (such as well location coordinates) according to specific rules. Through iterative calculations, the objective function's value is optimized. While this method can reduce the influence of human factors, the calculation process can produce a large number of invalid well placement plans. This well placement method also lacks support from reservoir engineering theory, resulting in low optimization efficiency and long computation time. These drawbacks are particularly prominent when the reservoir model is large, making it difficult to apply well placement optimization methods based on optimization theory to actual reservoirs.

[0063] Currently, relatively few patents and articles related to well location optimization can be found in China. Examples include patent number CN201710990712.6, "Optimal Well Location Deployment Method and Apparatus for Small Fault-Block Reservoirs," patent number CN201410508034.1, "Well Location Optimization Design Method Based on Reservoir Static Factors," and the article "Application of Genetic Algorithms in Well Location Optimization in the Sulige Gas Field."

[0064] Among them, the patent "Optimal Well Location Deployment Method for Small Fault Block Reservoirs" has the problem of too long calculation time, and the method cannot be applied to actual reservoirs. This is because the patent adds injection and production units one by one on the basis of the initial well positions and optimizes the well positions of the newly added injection and production units until the recovery rate or net present value no longer increases. This means that the optimization algorithm must be used to calculate each time an injection and production unit is optimized. To obtain the final well layout plan, multiple rounds of optimization calculations are required, which requires a lot of computing time and computer resources. The present invention only needs to use the optimization algorithm once to calculate the well layout plan, which significantly reduces the calculation time and can be applied to actual large-scale oil reservoirs.

[0065] In the patent "Well Location Optimization Design Method Based on Reservoir Static Factors," the well location optimization process only considers the static parameters of the reservoir, and does not consider the dynamic parameters of the reservoir, such as the impact of pressure on the development process. Dynamic parameters also play an important role in evaluating the recoverable value and ease of production of different parts of the reservoir. When formulating a well layout plan, it is necessary to consider the impact of both dynamic and static parameters on the development effect. The present invention considers the impact of both static and dynamic parameters on the well layout plan during the well location optimization process, making the calculation process closer to the actual reservoir situation and the calculation results more reliable.

[0066] Meanwhile, in the document "Application of Genetic Algorithms in Well Location Optimization in the Sulige Gas Field," the optimization variables include well location and the controlled area of ​​each well. The more optimization variables are included, the greater the computational cost and time required to obtain the optimal solution. Furthermore, the document does not optimize other parameters that affect production results, such as well injection and production rates, well type, and perforation. The present invention only optimizes well location, while other optimization parameters such as well type, perforation, and injection and production rates are determined using reservoir engineering methods. This means that the present invention can optimize more parameters without incurring a high computational cost, resulting in better production results.

[0067] In response to the above situation, a new method for rapid well location optimization based on the degree of displacement balance is proposed. The method of the present invention uses the dynamic and static parameters of the model to comprehensively consider the production potential and water injection potential as constraints, and improves the optimization algorithm and evaluation indexes to propose a new rapid well location optimization method and technical process to find the optimal well layout plan, which can be applied to the simulation of actual large-scale reservoir models and can achieve rapid optimization of multiple important parameters. It also takes into account the dynamic and static parameters of the reservoir, and combines the reservoir engineering method to make the well layout more targeted, significantly reduce invalid well layout plans, help improve the optimization effect, and ultimately form an efficient well layout plan. The optimal development plan calculated by the method of the present invention can be applied to the practice of optimizing oil field development and can be used as a reference or implementation for on-site engineering personnel.

[0068] Example 1

[0069] The researchers of the present invention analyzed the factors affecting well layout in the actual oil and gas development process, as well as the relationship between well layout and reservoir geological data and production data, and found that considering the influence of reservoir static parameters and dynamic parameters on the well layout plan during well location optimization can make the calculation process closer to the actual situation of the reservoir and the calculation results more reliable. At the same time, the selection of the optimization algorithm and the setting of the optimization parameters will affect the calculation speed of the well layout optimization and the accuracy of the optimization results. Therefore, the researchers of the present invention use a set optimization algorithm to optimize each well layout parameter based on a specific optimization strategy, and at the same time combine reasonable data constraint settings to greatly improve the running speed of the well layout optimization process while ensuring the reliability of the optimization results.

[0070] Specifically, the well placement optimization method provided by the present invention establishes a fine streamline simulation model for the reservoir and utilizes a potential evaluation method to classify the reservoir's production potential and water injection potential by obtaining model grid parameters (grid's original oil saturation, residual oil saturation, oil phase pressure, minimum bottomhole pressure, porosity, permeability, distance from the grid to the nearest boundary, distance from the grid to the oil-water boundary, and distance from the grid to the gas-water boundary). Initial values ​​are set for parameters such as wellhead coordinates, wellbore length, wellbore azimuth, completion interval, and well operating system, and these values ​​are written into the relevant operating files of a commercial streamline simulator to construct an initial well placement plan. The streamline simulator is called in parallel to calculate the fitness value (i.e., flow capacity-storage capacity Lorentz coefficient) of each particle in the initialization (i.e., each well layout scheme), evaluate the displacement uniformity of the initial scheme, and take the scheme with the smallest Lorentz coefficient among the initial well layout schemes as the initial global optimal well layout scheme (which will change dynamically as the iteration proceeds); use the improved particle swarm algorithm to optimize the parameters, generate new well layout schemes, compare the fitness values ​​of particles (i.e., well layout schemes), and update the optimal particle information in the particle swarm (i.e., the global optimal well layout scheme); iterate the calculation until the iteration termination condition is met, exit the program, and obtain the final global optimal well layout scheme.

[0071] The purpose is to achieve the purpose of optimizing parameters under given initial optimization parameters by combining reservoir streamline simulation with optimization algorithm and potential constraints, and then obtain the best well layout plan that is adapted to the actual reservoir while ensuring calculation efficiency, so as to optimize the development effect of the reservoir. It solves the current problems in domestic well layout optimization, such as low calculation efficiency, poor convergence, many invalid plans, and long numerical simulation time, and provides new theoretical methods and technical references for formulating reasonable well layout plans.

[0072] Based on the above concept, the technical researchers of the present invention designed the well location optimization method based on displacement balance analysis of the present invention from the following aspects:

[0073] (1) Obtain actual reservoir data and establish a three-dimensional reservoir fine streamline simulation model;

[0074] (2) Classify the production potential and water injection potential of the oil reservoir;

[0075] (3) Define optimization parameters and set initial values ​​to form an initial well layout plan;

[0076] (4) Call the streamline simulator in parallel to calculate the fitness value of each initial solution;

[0077] (5) Using the improved particle swarm algorithm to optimize parameters, generate new well layout plans and compare the fitness values ​​of each particle (i.e., each well layout plan);

[0078] (6) Iterate the calculation until the iteration conditions are met, exit the program, and obtain the final global optimal well layout plan.

[0079] Next, a detailed description of the method according to an embodiment of the present invention will be provided based on the accompanying drawings. The steps shown in the flowcharts of the accompanying drawings can be executed in a computer system including, for example, a set of computer-executable instructions. Although the flowcharts show a logical order of the steps, in some cases, the steps shown or described may be executed in a different order than that shown or described herein.

[0080] Figure 1 The flow chart of the well location optimization method based on displacement balance analysis provided by the first embodiment of the present invention is shown. Figure 1 It can be seen that the method includes the following operations.

[0081] Simulation model construction step S1: establishing a three-dimensional digital reservoir geological model based on the set reservoir geological data, and integrating the three-dimensional digital reservoir geological model with the set dynamic production data to construct a reservoir flow simulation model of the target reservoir area;

[0082] In this step, the embodiment of the present invention establishes a three-dimensional digital reservoir geological model based on static data such as actual reservoir geology, well logging, and seismic interpretation data;

[0083] Specifically, in one embodiment, by drilling data wells in favorable areas of oil and gas accumulation, the data obtained through logging, coring, drill pipe testing, and logging interpretation are used to obtain the lithology, porosity, permeability, and oil saturation parameters of the formation, conduct detailed stratigraphic comparisons, clarify the properties and distribution of the oil layers, collect wellhead coordinates, well inclination correction data, reservoir top depth, stratification data, and sub-layer data, and establish a three-dimensional digital reservoir geological model based on these data and commercial modeling software.

[0084] Reservoir flowline simulation digitizes the actual reservoir and displays the entire oilfield development process on a computer. It describes the reservoir's geological characteristics, reservoir rocks, and the distribution of fluids. It can simulate the flow of oil, gas, and water in the formation, predict the distribution of remaining oil, oil production, and water content, select the optimal development plan, and guide field production. Furthermore, based on the constructed three-dimensional digital reservoir geological model, a reservoir flowline simulation model is established by integrating static reservoir data and production dynamic data. This model is used to simulate and calculate oil, gas, and water flow data in the target reservoir area.

[0085] In practical applications, in order to ensure that the operating status of the constructed reservoir streamline simulation model can meet the requirements of reservoir development projects, after integrating reservoir static data and production dynamic data to establish the reservoir streamline simulation model, simulation calculations are also performed based on experimental data to ensure the operability of the model.

[0086] Specifically, in one embodiment, after the reservoir streamline simulation model is established, the following operations are performed to verify the established reservoir streamline simulation model:

[0087] Reservoir fluid parameters and rock physical parameters are obtained through experiments to determine the initial reservoir conditions and the corresponding oil test data and trial production data; specifically, reservoir fluid (component) parameters and rock physical parameters are obtained through experiments to determine the initial reservoir conditions (oil-water interface, oil-gas interface, pressure gradient) and oil test and trial production data.

[0088] The corresponding experimental reservoir geological data and dynamic production data are integrated to construct the corresponding experimental reservoir streamline simulation model, and based on it, the flow simulation calculation of oil, gas and water in the experimental reservoir development is carried out; during actual execution, the oil and water production history is given according to static and dynamic data, the reservoir properties are digitized, and the reservoir streamline simulation model is constructed. The flow simulation calculation of oil, gas and water in the reservoir development is carried out to analyze and determine whether the constructed model can operate normally.

[0089] Furthermore, the simulation calculation results are compared with the experimentally measured historical oil and gas production data to ensure that the operating status of the constructed experimental reservoir streamline simulation model meets the set conditions.

[0090] Simply using mathematical methods to find the best solution, although this method is a significant improvement over traditional empirical methods, the characteristics of different oil reservoirs will also vary greatly. In addition, if a comprehensive well layout optimization operation is performed on all grids in the entire oil reservoir area without reasonable well layout targeting, a large number of invalid well layout plans will inevitably be generated, and the data processing pressure and computing pressure will be very high, which will seriously affect the execution efficiency of well layout optimization. Based on this, the embodiment of the present invention uses the production potential data and water injection potential data of the oil reservoir to constrain the effectiveness of well layout, thereby improving the targeting of well layout optimization operations. Therefore, there are:

[0091] Well area division step S2: Based on the established three-dimensional digital reservoir geological model, the production potential data and water injection potential data of each grid in the target reservoir area are calculated using a set strategy according to grid parameters, and the initial well area is selected based on the data.

[0092] In practical applications, there are many methods for evaluating production potential, including the remaining oil reserve abundance method, numerical simulation method, production potential index method, and other methods. Because the production potential index method takes into account the impact of reservoir reserve abundance, reservoir pressure, formation permeability, distance from the boundary, and oil, gas, and water relationship position on production capacity, it can characterize the potential remaining production capacity of oil and gas resources possessed by the substances and energy stored in the reservoir. Generally, water injection wells are selected in locations with relatively developed oil layers and good permeability. Therefore, the present invention uses the production potential index method and the water injection potential index method to quickly divide the high-potential areas of the reservoir and guide the direction of well placement.

[0093] Specifically, in one embodiment, the basic parameters of each grid (i, j, k) are first obtained, including the original oil saturation S of the grid. oijk (t), residual oil saturation S or , oil phase pressure P oijk (t), minimum bottom hole pressure P min , porosity φ ijk , permeability K ijk , the distance r from the grid to the nearest boundary ijk , the distance from the grid to the oil-water boundary h woc,ijk , the distance from the grid to the air-water boundary h wgc,ijk . And save these parameters into the corresponding array for subsequent calculations.

[0094] Furthermore, the grid parameters are substituted into the matching production potential formula and water injection potential formula to calculate the production potential value and water injection potential value of each grid.

[0095] Specifically, based on actual working conditions, the production potential calculation formulas for various types of grid matching are usually set according to the following principles:

[0096] For oil reservoirs with edge-bottom water and gas cap, the production potential evaluation formula is as follows (3):

[0097]

[0098] For reservoirs with only edge and bottom water, the production potential evaluation formula is as follows (4):

[0099]

[0100] For oil reservoirs with only gas caps, the production potential evaluation formula is as follows (5):

[0101]

[0102] For an oil reservoir without edge water or gas cap, the production potential evaluation formula is as follows (6):

[0103]

[0104] For oil reservoirs with water injection demand, the water injection potential evaluation formula is as follows (7); if the oil reservoir has no water injection demand, there is no need to perform water injection potential evaluation.

[0105]

[0106] Where, J ijk (t) is the production potential value of grid (i, j, k), S oijk(t) is the original oil saturation of the grid, S or is the residual oil saturation, P oijk (t) is the oil phase pressure, P min is the minimum bottom hole pressure, φ ijk is the porosity, K ijk is the permeability, r ijk is the distance from the grid to the nearest boundary, h woc,ijk is the distance from the grid to the oil-water boundary, h wgc,ijk is the distance from the grid to the air-water boundary.

[0107] The above potential evaluation formula is used to calculate the production potential and water injection potential of each grid and store them in two three-dimensional arrays to complete the digitization of the reservoir potential. The three-dimensional potential arrays are then vertically summed and normalized. In other words, the three-dimensional potential map is converted into a two-dimensional potential map with values ​​in the range [0, 1], laying the foundation for subsequent well placement. Therefore, in one embodiment, the well placement area division step S2 also includes:

[0108] The calculated production potential data and water injection potential data are stored in two three-dimensional arrays to realize the digitization of reservoir potential. The three-dimensional potential arrays are vertically summed and normalized to form a two-dimensional potential map corresponding to each three-dimensional potential data.

[0109] The formula for superimposing three-dimensional potential data into a two-dimensional potential map is:

[0110]

[0111]

[0112] The formula for normalizing the two-dimensional potential map is:

[0113]

[0114]

[0115] Where Nz is the number of grid layers in the longitudinal direction (i.e., in the Z direction); J ij (t) is the production potential value after the three-dimensional potential is superimposed into a two-dimensional potential map; U ij (t) is the water injection potential value after the three-dimensional potential is superimposed into the two-dimensional potential map; JN ij (t) is the normalized production potential value; UN ij (t) is the normalized water injection potential value.

[0116] Furthermore, the embodiment of the present invention includes an initial plan generation step S3, setting initial values ​​of well layout parameters and potential requirements based on set reservoir data and development demand data, and fitting and adjusting the well layout parameters and potential requirements based on the set plan in the initial well layout area to form an initial well layout plan corresponding to each well; wherein the well layout parameters include well pattern density, well spacing constraint, number of wells, wellhead coordinates, wellbore length, wellbore azimuth, completion interval, and well working system data;

[0117] Before determining the initial well layout plan, reservoir engineering knowledge is acquired to provide data support for determining the number of production wells and water injection wells, the minimum well spacing, the total injection and production volume, and the range of wellbore lengths. Therefore, the initial plan generation step includes:

[0118] The initial values ​​of well layout parameters and potential requirements are set according to the geological characteristics, reserve size, fluid properties, expected recovery rate and expected oil production rate of the target oil reservoir area. The potential requirements ensure that the qualified initial well layout plan needs to meet the potential constraints.

[0119] In actual application, the reasonable well pattern density S and well spacing range [D min ,D max ], the number of oil wells Np, the number of water wells Nw, the total injection and production volume Q, and the range of wellbore length [L min ,L max ], and then using the normalized two-dimensional production potential map, based on the well spacing constraint, randomly select grids in the initial well layout area to generate a corresponding number of reservoir wellhead coordinates in sequence, and adjust the potential requirements according to the matching of the number of generated wellhead coordinates and the corresponding well layout parameter values ​​in the initial well layout plan, until the matching of the two meets the set conditions, and the initial data values ​​of each well layout parameter that meets the conditions in the target reservoir area are taken as the initial well layout plan.

[0120] like Figure 2 As shown in the figure, when generating a well layout plan, grids are randomly selected based on the determined initial well layout area to set the well layout positions, wherein the initial well layout area includes high production potential areas and high water injection potential areas whose production potential values ​​meet the set potential requirements. For development wells, using the normalized two-dimensional production potential map, based on the well spacing constraint, grids are randomly selected in the high potential area with a production potential value greater than 0.9 to generate the wellhead coordinates (x p ,y p ,z p), if the number of high-potential grids is insufficient to meet the well layout requirements, the potential requirement is automatically reduced by 0.05 (i.e. 0.9, 0.85, 0.8...) to adjust the high production potential threshold until the well layout requirements are met. Similarly, for water injection wells, using the normalized two-dimensional water injection potential map, based on the well spacing constraint, randomly select grids in the high-potential area with a water injection potential value greater than 0.9 to generate the wellhead coordinates (x w ,y w ,z w ), if the number of high-potential grids is not enough to meet the well layout requirements, the potential requirement will be automatically reduced by 0.05 (i.e. 0.9, 0.85, 0.8...) to adjust the high water injection potential threshold until the well layout requirements are met.

[0121] For a single well layout plan, the well layout process (i.e. determining the wellhead coordinates) is as follows: Figure 3 As shown in the figure, the production well placement process is used as an example. A random well (wellhead coordinates) is placed in a high-potential area of ​​the reservoir. Then, grids within the well spacing range are set as invalid (blue area B indicates invalid grids), preventing subsequent production wells from falling within this well spacing range. After each production well is located, the production potential value of each grid is recalculated to update the 2D production potential map and define the next well placement range.

[0122] Furthermore, the wellbore length and azimuth can be determined based on the wellhead coordinates and random functions, thereby determining the grid coordinates along the well path. Using 3D production potential constraints, the well path is perforated through high-potential layers in the grid. The production system is also determined based on the quantitative production capacity and water injection capacity of the well area. These initialized parameters are written into a commercial streamline simulation file, and the streamline simulator is used to perform simulation calculations.

[0123] Specifically, in actual application, other parameter data of the initial well layout plan are determined through the following operations:

[0124] (301) Obtaining three-dimensional characteristic attributes within the control range of a single well

[0125] The wellhead coordinates (x ps ,y ps ,z ps ) and then take this point as the center and use 2L max Take the reservoir production potential attribute within a cube for the side length. The cube is regarded as the control area of ​​the well, and the low potential (the area with production potential value less than 0.3) in the three-dimensional data body is ijk All grids with a value less than 0.3 are set as dead grids.

[0126] Similarly, the wellhead coordinates (xws ,y ws ,z ws ) and then take this point as the center and use 2L max Take out the reservoir water injection potential attribute within a cube for the side length. The cube is regarded as the control area of ​​the well, and the high potential (the area with water injection potential value greater than 0.3) in the three-dimensional data body is taken as the control area of ​​the well. ijk All meshes with a value greater than 0.3 are set to dead meshes.

[0127] In combination with practical applications, the description of different well types can be completed by combining and changing parameters. Therefore, in one embodiment, the well layout parameters also include well tail coordinates. In the initial plan generation step, a random function is used to select well layout trajectory parameters that meet the set trajectory conditions in the corresponding initial well layout area, and based on the parameters, the matching effective well tail coordinates are determined in combination with the well head coordinates, and the well type data of the initial well layout plan are jointly characterized with the well head coordinates. That is, the embodiment of the present invention further includes: (302) the description of different well types can be completed by combining and changing parameters;

[0128] Specifically, based on the actual situation, we know that the well can only extend downward from the wellhead coordinates and it is impossible to drill upward from the wellhead, so θ1∈[0°,360°], θ2∈[0°,360°], θ3∈[90°,270°].

[0129] like Figure 4 As shown, take any well as an example to illustrate the process of calculating the well tail coordinates:

[0130] x e -x s =Lcosθ1 (12)

[0131] y e -y s = -Lcos(180°-θ2) (13)

[0132] z e -z s = -Lcos(180°-θ3) (14)

[0133] Among them, (x s ,y s , z s ) is the wellhead coordinate; (x e ,y e , z e ) is the well tail coordinate; L is the wellbore length from the well head to the well tail; θ1 is the angle between the positive X axis and the inclined well section in the clockwise direction; θ2 is the angle between the positive Y axis and the inclined well section in the clockwise direction; θ3 is the angle between the positive Z axis and the inclined well section in the clockwise direction.

[0134] The coordinates of the well tail are:

[0135] x e =x s +Lcosθ1 (15)

[0136] y e =y s +Lcos(180°-θ2) (16)

[0137] z e =z s +Lcos(180°-θ3) (17)

[0138] In particular, when θ1 = 90°, θ2 = 90°, and θ3 = 180°, it is a vertical well; and when θ3 = 90°, it is a horizontal well.

[0139] For a production well, a random function is used to randomly generate parameters (θ1, θ2, θ3, L) in the high-potential area grids of the 3D data volume of the well (all valid grids remaining after removing the low-potential grids). If the number of valid grids in the grids passed through accounts for more than half of the total number of grids along the well trajectory (i.e. Where N1 represents the total number of effective grids in the three-dimensional control volume, M1 represents the total number of grids in the three-dimensional control volume) and the wellbore length L is within the control range [L min ,L max ], the generated well trajectory is considered valid. If the generated well body length is greater than L max The wellbore length is taken as L max ; If the generated wellbore length is less than L min The wellbore length is taken as L min If the number of valid grids passed by the well trajectory is less than half of the total number of grids along the well trajectory, a random function is used to regenerate new parameters (θ1, θ2, θ3, L) until the valid well trajectory is met.

[0140] Similarly, for water injection wells, random functions are used to randomly generate parameters (θ1, θ2, θ3, L) in the low-potential area grids of the three-dimensional data volume of the well (all valid grids remaining after removing the high-potential grids). If the number of valid grids in the grids passed through accounts for more than half of the total number of grids along the well trajectory (i.e. Where N1 represents the total number of effective grids in the three-dimensional control volume, M1 represents the total number of grids in the three-dimensional control volume) and the wellbore length is within the control range [L min ,L max ], the generated well trajectory is considered valid. If the generated well body length is greater than L max The wellbore length is taken as L max ; If the generated wellbore length is less than L min The wellbore length is taken as Lmin If the number of valid grids passed by the well trajectory is less than half of the total number of grids along the well trajectory, a random function is used to regenerate new parameters (θ1, θ2, θ3, L) until the valid well trajectory is met.

[0141] The solution of the above-described embodiment of the present invention can optimize various well types, including vertical, deviated, and horizontal wells. The combination of seven variables—three parameters, namely wellhead coordinates, wellbore azimuth, and wellbore length—can describe a variety of well types. This makes the well location optimization method applicable not only to common vertical wells, but also to other well types, such as deviated and horizontal wells.

[0142] In combination with practical applications, the researchers of the present invention have considered combining well location optimization, well type optimization, perforation optimization, and injection-production optimization, so that the well layout scheme can be better applied to actual production. In actual oil field production, it is necessary not only to determine the location of the well but also to determine the well type, the perforation layer of each well, and the oil production or water injection volume of a single well. However, common well location optimization methods can only optimize the well location or the well location + well type, and cannot optimize multiple parameters at the same time, resulting in many limitations in the actual application of the well layout scheme, and may not achieve a better mining effect. A big difference between the present invention and the common well location optimization method is that the present invention takes into account the characteristics of the oil reservoir, and utilizes the constraints of the oil reservoir production potential and water injection potential to achieve the optimization of multiple parameters, that is, the well location optimization is combined with the well type optimization, perforation optimization, and injection-production optimization, so that the well layout scheme can be better applied to actual production.

[0143] Furthermore, in one embodiment, the well layout parameters also include perforation layer data. In the initial plan generation step, the perforation layer data of each initial well layout plan is determined using an inverse distance weighted algorithm based on the selected well layout trajectory parameters and the production potential data of each well layout plan. Therefore, the embodiment of the present invention includes: (303) completing the optimization of the perforation layer based on the production potential constraint;

[0144] Among them, completing the description of the well type is equivalent to determining the grid that the feasible well passes through in the digital model, and the perforation layer of a single well can be determined using the production potential constraint of inverse distance weighting.

[0145] In order to more clearly illustrate the process of determining the perforation layer, a relatively simple production well is used for description. The schematic diagram of the well passing through the grid in the simulator is shown as follows Figure 5 As shown, there is a horizontal well in the XY plane, and the well head coordinate is (x s ,y s , z s ), the well tail coordinate is (x e ,y e , z eThe red area I is the high potential area, the green area II is the medium potential area, and the blue area III is the low potential area (Note: the high, medium and low potential thresholds are related to the specific reservoir analysis and the potential value and number of grids. In this invention, the grids with potential values ​​greater than 0.7 are considered high potential, the grids with potential values ​​between 0.3 and 0.7 are considered medium potential, and the grids with potential values ​​less than 0.3 are considered low potential).

[0146] The operation in (302) can determine the parameters (θ1, θ2, θ3, L) and then calculate the equation of the straight line segment determined by the well head coordinate and the well tail coordinate, and the coordinates of the path grid. The coordinates (x, y, z) of the path grid are converted into sequence coordinates (i, j, k) under digital model conditions, where (i∈N*, j∈N*, k∈N*). The well head coordinates (x s ,y s , z s )The sequence coordinates after transformation are (i s ,j s ,k s ), well tail coordinate (x e ,y e , z e )The sequence coordinates after transformation are (i e ,j e ,k e ).

[0147] Since the way of representing well trajectory in the digital model file is different from the actual well trajectory, such as Figure 5 As shown, the black solid line is the actual well trajectory, and the black dotted line is the well trajectory in the digital model file. Figure 5 The grid marked with black dots in the figure is used as an example to illustrate the method of determining the perforation layer position using production potential weighting.

[0148] Since there is fluid flow between grids during the actual production process, the influence of other grids at different distances from the central drainage grid on the central grid is different. The introduction of the production potential evaluation formula based on inverse distance weighting can more accurately evaluate the actual production potential of the grid where the well track is located. This paper considers that the influence range of a single grid on the well track path is R = 0.5D min (where D min is the minimum well spacing), that is, when calculating the inverse distance weighted production potential, take the side length D min The cube is the range controlled by a single discharge grid. At the same time, it can be calculated that there are r grids between the center grid and the boundary of the control volume (if the r calculated in the XYZ directions are different, the minimum value can be taken), as shown in the following example: Figure 6 shown.

[0149] The production potential evaluation formula based on inverse distance weighting of the present invention is as follows:

[0150]

[0151] The calculation formula to determine the grid weight is:

[0152] where r≠0 (19)

[0153] where r≠0 (20)

[0154] Among them: JD ijk is the production potential value of the center grid (i, j, k) based on inverse distance weighting; J ijk is the production potential value of grid (i, j, k) at the initial time; is the potential weight of grid (i+m,j+m,k+m), which decreases as the distance between grid (i+m,j+m,k+m) and the center grid (i,j,k) increases; d (i+m)(j+m)(k+m) is the distance from the grid (i+m,j+m,k+m) to the center grid (i,j,k);

[0155] The sum of the weights is 1, and the index value P of the present invention is 2, which means that the potential evaluation of the central grid mainly depends on itself and the surrounding adjacent grids. The farther the grid is, the smaller its potential contribution is.

[0156] After calculating the inverse distance weighted production potential value of the grid and shooting the potential value greater than 0.5 (ie JD ijk >0.5) of all grids, such as Figure 7 If the number of perforated grids is less than one-third of the total number of grids passing through the well, the perforation potential threshold is adjusted down (for example, decreasing by 0.05, 0.45, 0.4, etc.) until the number of perforated grids is greater than or equal to one-third of the total number of grids passing through the well.

[0157] Similarly, the optimization of the perforation layer of the water injection well is similar to that of the production well, including calculating the inverse distance weighted production potential value of the grid penetrated by the water injection well and perforating the grid with a potential value less than 0.1 (i.e. JD ijk If the number of grids opened is less than one-third of the total number of grids passed by the well, the threshold of potential opening is increased (for example, by increments of 0.05, 0.15, 0.2, etc.) until the number of grids opened is equal to one-third of the total number of grids passed by the well.

[0158] The process of determining the perforation position of the water injection well is similar to that of the production well. The perforation position is also determined using the potential evaluation method based on inverse distance weighting, so it will not be repeated here.

[0159] In one embodiment, the well layout parameters also include production system parameters. In the initial plan generation step, the total production volume and injection volume of the target reservoir area are calculated based on the geological reserves of the reservoir, the expected oil production rate, and the expected injection-production ratio. Then, based on the production potential data corresponding to each initial well layout plan and the calculated total injection-production volume, the corresponding production system parameters are determined. Therefore, the embodiment of the present invention further includes: (304) completing the optimization of the production system based on the constraints of the production potential and the total injection-production volume:

[0160] Specifically, the total oil production QL and injection volume Qwis of the reservoir can be calculated based on the geological reserves N of the reservoir, the given oil production rate Vo, and the injection-production ratio Rip.

[0161] Q L =N×V o (twenty one)

[0162] Q wis =Q L ×R ip (twenty two)

[0163] According to the method (331), the three-dimensional characteristic attributes within the control range of any single production well or injection well can be obtained, and the potential values ​​of all grids within the control range can be added together. That is, the potential value JT controlled by a single production well is ijk，,n for:

[0164] JT ijk，,n =∑J ijk (twenty three)

[0165] The potential value UT controlled by a single injection well ijk,m for:

[0166] UT ijk,m =∑U ijk (twenty four)

[0167] The total injection and production volume is allocated according to the proportion of the control potential of the single well, that is, for any production well, its production volume Q Ln for:

[0168]

[0169] For any production well, its injection volume Q wism for:

[0170]

[0171] Through the above method, each particle (one particle is a well layout plan) can be initialized, and the wellhead coordinates, wellbore length, wellbore azimuth, completion layer, well working system and other parameters of the well can be written into the corresponding numerical model file through the program. That is, in actual application, the wellhead coordinates and other parameters in the initial well layout plan are determined, including the wellbore length L, wellbore azimuth (θ1, θ2, θ3), completion layer (i cs ,j cs ,k cs )~(i ce ,j ce ,k ce ), and the working system of the well is set, and the initial values ​​of these parameters are written into the relevant operation files of the commercial streamline simulator to prepare for the streamline simulation calculations of the subsequent initial well layout plans.

[0172] Next, the constructed reservoir streamline simulation model is called in parallel to calculate the fitness value of each particle in the initialization (i.e., each well layout scheme), evaluate the displacement uniformity of the initial scheme, and select the scheme with the smallest Lorentz coefficient among the initial well layout schemes as the global optimal well layout scheme (which will change dynamically as the iteration proceeds). Therefore, in one embodiment, there is a parallel simulation calculation step S4, in which the reservoir streamline simulation model is run in parallel to perform streamline simulation calculations on each initial well layout scheme, obtain fitness data representing the degree of reservoir displacement balance, and select the optimal initial well layout scheme corresponding to each well;

[0173] In one embodiment, the parallel simulation calculation step includes:

[0174] The grid cumulative flow capacity data and grid cumulative storage capacity data of each well layout plan are calculated based on the flight time in the streamline simulation result file. The Lorentz coefficient of each oil well layout plan is then obtained as the corresponding fitness value. The well layout plan corresponding to the minimum Lorentz coefficient is taken as the optimal initial well layout plan.

[0175] In conjunction with practical applications, streamline simulators are called in parallel to calculate the objective function values ​​of the initial solution. Using computer parallel computing, streamline simulations can be performed simultaneously for multiple particles being initialized (i.e., multiple well layouts). The result file generated by the streamline simulation includes flight time, which can be used to calculate the dynamic Lorentz coefficient. The specific calculation process is as follows:

[0176] (401) Calculate the total flight time FOTi of each grid using the data of TIME_BEGi and TIME_ENDi in the result file.

[0177] FOT i =TIME_BEG i +TIME_END i (27)

[0178] (402) Calculate the storage capacity of each grid, i.e., the pore volume Φ i The pore volume Φ of each grid in the model can be obtained by reading the keyword in the simulator i , cumulative pore volume Φ ACUi , normalized pore volume Φ NOMi And save it in the form of an array for subsequent calculation.

[0179]

[0180]

[0181] (403) Calculate the flow capacity F of each grid i (Φ). The flow capacity is defined as the ratio of the pore volume of the grid to the flight time of the corresponding grid. The cumulative flow capacity F of the grid is calculated. ACUi (Φ), normalized grid cumulative flow capacity F NOMi (Φ). The calculation results are saved in the form of an array for subsequent calculations.

[0182]

[0183]

[0184]

[0185] (414) The storage capacity and flow capacity of all grids are arranged in ascending order according to storage capacity. According to the normalized grid cumulative flow capacity F NOMi (Φ) and the normalized pore volume Φ NOMi Draw the Lorenz curve of cumulative storage capacity-cumulative flow capacity. Figure 8 As shown, the shaded area is the degree of flow heterogeneity, that is, the Lorentz coefficient L of the objective function c , the calculation formula is as follows:

[0186]

[0187] In the parallel simulation calculation step, it also includes:

[0188] The well layout parameters in the optimal initial well layout plan are stored to form a global optimal array for subsequent retrieval for comparative analysis and update. When updating, the entire set of well layout parameters is updated by direct replacement so that the historically optimal well layout parameter values ​​are always retained in the array.

[0189] Specifically, in one embodiment, based on the calculated Lorentz coefficient of each particle (i.e., each well layout scheme), the scheme corresponding to the minimum Lorentz coefficient is taken as the optimal well layout scheme in initialization, and each parameter in the optimal well layout scheme is stored in an array as the global optimal well layout scheme. g (t), and the array in the global optimum is replaced and updated every time it is calculated iteratively. The initial value of each particle (i.e., each parameter in the well layout plan) is regarded as the historical optimal value (i.e., individual extreme value) of the particle. i (t) and save it to the corresponding array. After each iterative calculation, the historical optimal value of particle i will be replaced and updated.

[0190] Parameter optimization step S5: Utilize the set improved particle swarm algorithm to optimize the well layout parameters of each optimal initial well layout plan, iteratively execute to generate a series of optimized well layout plans, and update the optimal initial well layout plan based on the optimized well layout plan. Evaluate each optimized well layout plan based on fitness data that characterizes the degree of reservoir displacement balance until the evaluation results meet the set final well layout conditions, and use the obtained optimal well layout plan as the final target well layout plan.

[0191] The basic particle swarm algorithm suffers from problems such as weak inter-particle relationships and limited information exchange during local and global search; all particles search toward the current optimal solution, resulting in a single, single-direction search that can easily lead to local optima. Therefore, in the parameter optimization step, the present invention implements an improved particle swarm algorithm with velocity and position update formulas based on the principles of adjusting inertia weight, increasing information exchange between particles, and expanding the search space. These formulas are combined to calculate the well placement parameters for the optimized well placement scheme. By adjusting inertia weight, increasing information exchange between particles, and expanding the search space, the improved particle swarm algorithm can improve the algorithm's optimization performance and convergence speed.

[0192] Specifically, the process of using the improved particle swarm algorithm to optimize parameters and generate an optimized well layout plan includes:

[0193] (51) Calculate the updated optimization parameters according to the speed and position update formula, i.e., the new well layout plan;

[0194] In one embodiment, the speed formula of the improved particle swarm optimization algorithm is:

[0195] v i (t+1)=w(t)v i (t)+c1r1[p ai (t)-x i (t)]+c2r2[p i-1 (t)-x i (t)] (34)

[0196] in,

[0197]

[0198]

[0199] The position update formula of particle i is:

[0200] x i (t+1)=x i (t)+K(t)v i (t+1) (37)

[0201]

[0202] In the above formula, M is the number of particles (well layout scheme); i is the number of particles and i∈{0,1,2...M}; v i (t+1) is the velocity of particle i at iteration t+1; v i (t) is the velocity of particle i at iteration t; w is the inertia weight, w max =0.9, w min =0.1; t is the current iteration number; t max is the maximum number of iterations; c1 is the “cognitive” weight; c2 is the “social” weight; p ai (t) is the new historical optimal position of particle i; p i (t) is the historical optimal position of particle i; p i-1 (t) is the new global optimal position; x i (t+1) is the position at the t+1 iteration; x i (t) is the position at the tth iteration; r1, r2 are random numbers in the range [0,1]; f(x i ) is the current fitness value of particle i; K(t) is the flight time factor, and K0 is the initial time factor, which is generally taken as 1.5.

[0203] In one embodiment, the updated optimization parameters are calculated according to the following operations:

[0204] (511) Get the new wellhead coordinates:

[0205] The update formula of speed and position can be used to update the coordinates of the well head. Specifically, the position of particle i in the next iteration can be calculated based on the position update formula of particle i using the above update formula.

[0206] (512) Determine the well type, perforation layer, and injection and production volume of each well in the well layout plan

[0207] After particle i (one particle represents a set of well layout plans) updates its position, it will obtain the updated wellhead coordinates. Then, the method program of steps (301) to (304) will automatically complete the determination of the well type, perforation layer, and injection and production volume of a single well to obtain a new well layout plan.

[0208] Particle i calculates a series of new parameters through steps (501) to (502), including the wellhead coordinates, the grid coordinates of the wellbore, the coordinates of the perforated layer, and the injection and production volume of a single well. These updated parameters are written to the numerical simulation file where particle i is located, which is then called by the streamline simulator.

[0209] Furthermore, in one embodiment, it includes:

[0210] (52) Based on the fitness data, each optimized well layout scheme is evaluated. The fitness value corresponding to each optimized well layout scheme is calculated using the constructed reservoir streamline simulation model. The calculated results are compared and analyzed with the fitness values ​​of the well layout parameters in the known global optimal array to obtain the evaluation results of the current optimized well layout scheme.

[0211] In combination with practical applications, the embodiment of the present invention uses the following operations to calculate the objective function of each particle (well layout plan) and update the optimal particle information in the particle swarm:

[0212] (511) Run the streamline simulator in parallel to calculate the fitness value f(x) of each particle (well layout scheme) in the tth iteration i ) (i.e. flow capacity - storage capacity Lorentz coefficient).

[0213] (512) The function adaptation value f(x i ) (i.e. the calculated Lorentz coefficient) and the individual extreme value of particle i (i.e. the historical optimal value of particle i) p i (t) to compare, if f(x i )<p i (t) then use f(x i ) replace p i (t) and f(x i ) and the corresponding well layout plan information (including wellhead coordinates, grid coordinates of the wellbore, coordinates of the perforated layer, and injection and production volume of a single well) are saved.

[0214] (513) The function adaptation value f(x i ) and the global optimal value p g (t) to compare, if f(x i )<p g (t) then use f(x i ) replace p g (t) and f(x i) and the corresponding well layout plan information (including wellhead coordinates, grid coordinates of the wellbore, coordinates of the perforated layer, and injection and production volume of a single well) are saved.

[0215] Based on the above logic, the calculation is iterated until the iteration condition is met, and then the program is exited to obtain the final globally optimal well layout plan. Specifically, if the iteration termination condition is not met, the updated optimization parameters are written into the streamline simulator and steps (51-52) are repeated. If the iteration termination condition is met, the global optimal solution of the last iteration is the final optimized well layout plan.

[0216] The above method can be used to obtain the parameters such as wellhead coordinates, wellbore length, wellbore azimuth, completion interval, and well working system corresponding to the optimal well layout plan. After correction according to the actual situation of the oil field, it can be implemented on site.

[0217] The rapid well placement optimization method proposed in this paper, based on displacement balance analysis, overcomes the shortcomings of traditional well placement design, where well placement plans are highly random and experience plays a major role. By combining reservoir streamline simulation with optimization algorithms and potential-constrained optimization parameters, this method rapidly and efficiently optimizes well placement while ensuring computational efficiency. This method yields optimal well location coordinates, perforation layers, production schedules, and other parameters useful for field engineers, ultimately resulting in an optimal well placement plan tailored to the actual reservoir. This method, when applied to optimizing oilfield development, provides a new approach and method for reservoir engineers to formulate well placement plans.

[0218] Compared with the traditional well layout scheme, the above technical solution of the present invention can achieve the following technical effects:

[0219] 1. By utilizing the constraints of production potential and water injection potential, the targetedness of well layout is enhanced.

[0220] Simply using mathematical methods to find the best solution, although this method is a significant improvement over traditional empirical methods, the characteristics of different oil reservoirs can also vary greatly. Introducing the evaluation of production potential and water injection potential can analyze specific oil reservoirs, divide the production potential and water injection potential in the oil reservoirs, and thus clarify the scope of well layout. Production wells are deployed in areas with high production potential, and water injection wells are deployed in areas with high water injection potential. This enhances the targetedness of well layout, can significantly reduce ineffective well layout plans, and thus improve the quality of well layout.

[0221] 2. Using the improved particle swarm optimization algorithm to optimize well layout can find the optimal well location plan.

[0222] Manually setting multiple sets of well location parameters for simulation and comparison can yield relatively optimal well placement plans, but this method is highly random, offers a limited number of options, and relies heavily on human experience when setting parameters, making it difficult to arrive at an optimal well placement plan. The greatest advantage of this invention lies in its integration of streamline simulation with optimization theory, automatically adjusting the values ​​of optimization parameters to generate a large number of reasonable options. Ultimately, the optimal well placement plan for the reservoir is designed, yielding parameters such as optimal wellhead coordinates, wellbore length, wellbore azimuth, completion interval, and well operating system, which can be used as a reference for engineers.

[0223] 3. Ability to optimize various well types, including vertical wells, inclined wells, and horizontal wells.

[0224] The combination of seven variables, including wellhead coordinates, wellbore azimuth, and wellbore length, can describe a variety of well types, making this well location optimization method applicable not only to common vertical wells, but also to other well types such as inclined wells and horizontal wells.

[0225] 4. Streamline simulation can be used to quickly evaluate the displacement balance of the well layout plan, significantly reducing calculation time.

[0226] Calculating real-world reservoirs using traditional differential reservoir simulators often takes hours or even days. When there are numerous well placement options, the computational time is excessive, making efficient and rapid well placement difficult. Compared to traditional differential reservoir simulation methods, streamline simulation offers two major advantages: rapid computational speed and robust stability. Therefore, using streamline simulators to calculate flight times and evaluate displacement balance within well placement options is an efficient and rapid method that can significantly reduce computational time.

[0227] 5. Combine well location optimization with well type optimization, perforation optimization, and injection-production optimization to enable the well layout plan to be better applied to actual production.

[0228] In actual oilfield production, it is necessary not only to determine the location of the well but also the well type, the perforation layer of each well, and the oil production or water injection volume of a single well. However, common well location optimization methods can only optimize the well location or the well location + well type, and cannot optimize multiple parameters at the same time, which leads to many limitations in the actual application of the well layout scheme, and may not achieve a better mining effect. A big difference between the present invention and the common well location optimization method is that the present invention takes into account the characteristics of the oil reservoir, and utilizes the constraints of the oil reservoir production potential and water injection potential to achieve the optimization of multiple parameters, that is, the well location optimization is combined with the well type optimization, perforation optimization, and injection and production optimization, so that the well layout scheme can be better applied to actual production.

[0229] In addition, based on the method of any one or more of the above embodiments of the present invention, an embodiment of the present invention further provides a storage medium, which stores program codes that can implement the method of any one or more of the above embodiments.

[0230] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0231] Supplementary Notes

[0232] The present invention uses the PUNQ-S3 reservoir engineering model as an example to analyze the feasibility of the well location optimization scheme described in any one or more of the above embodiments of the present invention:

[0233] (1) Introduction to the reservoir

[0234] PUNQ-S3 is a small-scale synthetic reservoir engineering model, which was jointly constructed by some EU oil companies, research institutions and Imperial College London. This model is often used for algorithm testing, and this invention will use it as a test case.

[0235] The PUNQ-S3 reservoir model is a three-dimensional three-phase reservoir model with a grid size of 19×28×25, of which the number of effective grids is 1761, and the grid size is 180m×180m×4m. It is divided into 5 layers vertically, with high permeability strips in layers 1, 3, and 5, and interlayers in layers 2 and 4. There is an active water body in the north and west of the reservoir, and a large fault in the south and east. Due to the sufficient energy of the water body, the original model does not contain water injection wells, but only 6 production wells. There is a gas cap at the top of the reservoir structure, and the production wells are surrounded by the oil-water interface. The oil saturation distribution diagram is shown as follows: Figure 9 The permeability distribution diagram is shown in Figure 10 shown.

[0236] Other reservoir parameters are shown in Table 1:

[0237] Table 1 PUNQ-S3 reservoir model related parameters

[0238] Parameter name Parameter value Parameter name Parameter value Original formation pressure 23.8MPa Top surface depth 2340m Average penetration rate 269.37mD Average porosity 0.2 Crude oil viscosity 3.47 mPa·s Initial oil saturation 0.8 geological reserves 1584×104 tons Recovery rate of original well layout plan 24％

[0239] In actual application, for the PUNQ-S3 reservoir, the corresponding phase permeability curve is as follows Figure 11 As shown;

[0240] (2) Implementation of well layout optimization

[0241] (2.1) Program optimization process

[0242] 1) Import the Case file and Grid file of the PUNQ-S3 model into the program, such as Figure 12 As shown;

[0243] 2) Evaluate the potential of the PUNQ-S3 model. Specifically, the corresponding potential evaluation screenshot is as follows Figure 13 As shown;

[0244] 3) Using particle swarm optimization to iteratively update the well layout parameters of the PUNQ-S3 model, the present invention provides the following Figure 14 The screenshot of the program iterative optimization process is shown;

[0245] 4) Optimization results are displayed. The specific optimization results are shown as follows: Figure 15 shown.

[0246] (2.2) Optimization effect evaluation

[0247] The rapid well location optimization design method of the present invention was used to optimize the well location design of the PUNQ-S3 reservoir and compared with the original well layout plan. The optimization calculation used 50 particles and 50 iterations for evaluation, and the optimization calculation took 2.8 hours. Figure 16 The recovery levels of the optimization results with potential constraints, the optimization results without potential constraints, and the original well layout plan were compared.

[0248] Depend on Figure 16 It can be seen that the recovery degree of the optimization scheme with potential constraint and the optimization scheme without potential constraint is higher than that of the original scheme, which shows that the method proposed in the present invention is effective.

[0249] Furthermore, Figure 17 A line chart comparing the changes in Net Present Value (NPV) during the optimization process for an optimization method with and without potential constraints is presented. The chart shows that the initial NPV of the well placement solution with potential constraints is higher than that of the solution without potential constraints. The final NPV is also higher for the former, demonstrating the excellent optimization effectiveness of the potential constraint method of this invention.

[0250] Figure 18 and Figure 19 A comparison of the well location deployment and remaining oil distribution of the original scheme and the optimized scheme obtained by potential constraint in the present invention shows that compared with the original scheme, the method proposed in the present invention has a greater degree of control over the remaining oil in the reservoir, further proving the effectiveness of the present invention.

[0251] Example 2

[0252] The method is described in detail in the embodiments disclosed in the above invention. The method of the invention can be implemented using various devices or systems. Therefore, the invention also discloses a well location optimization system based on displacement balance analysis, which is described in detail in specific embodiments below.

[0253] In one embodiment, a well location optimization system based on displacement balance analysis provided in an embodiment of the present invention includes:

[0254] Simulation model building blocks, which are configured as follows:

[0255] A three-dimensional digital reservoir geological model is established based on the set reservoir geological data, and a reservoir flow simulation model of the target reservoir area is constructed by integrating it with the set dynamic production data;

[0256] Well area division module, its configuration is as follows:

[0257] Based on the established three-dimensional digital reservoir geological model, the production potential data and water injection potential data of each grid in the target reservoir area are calculated using a set strategy according to the grid parameters, and the initial well layout area is selected based on the data;

[0258] The initial solution generation module is configured as follows:

[0259] Initial well placement parameters and potential requirements are set based on the set reservoir data and development demand data, and the well placement parameters and potential requirements are fitted and adjusted based on the set plan in the initial well placement area to form an initial well placement plan corresponding to each well; wherein the well placement parameters include wellhead coordinates, wellbore length, wellbore azimuth, completion interval, and well operating system data;

[0260] Parallel simulation calculation module, its configuration is:

[0261] The reservoir streamline simulation model is run in parallel to perform streamline simulation calculations on each initial well layout plan, and fitness data representing the degree of balance of the reservoir area is obtained to select the optimal initial well layout plan corresponding to each well;

[0262] Parameter optimization module, its configuration is:

[0263] The well layout parameters of each optimal initial well layout plan are optimized using the set improved particle swarm algorithm. A series of optimized well layout plans are generated through iterative execution, and the optimal initial well layout plan is updated based on the optimized well layout plan. Each optimized well layout plan is evaluated based on fitness data that characterizes the degree of equilibrium in the reservoir area until the evaluation results meet the set final well layout conditions. The optimal well layout plan obtained is then used as the final target well layout plan.

[0264] In one embodiment, the system further includes a simulation model verification module configured to:

[0265] After establishing the reservoir flowline simulation model, the reservoir fluid parameters and rock physical parameters are obtained through experiments to determine the initial reservoir conditions and the corresponding oil test data and production test data;

[0266] The corresponding experimental reservoir flow simulation model is constructed based on the reservoir geological data and dynamic production data corresponding to the experiment, and the flow simulation calculation of oil, gas and water in the experimental reservoir is carried out based on the streamline simulation model;

[0267] The simulation results are compared with the experimentally measured historical oil and gas production data to ensure that the operating status of the constructed experimental reservoir flow line simulation model meets the set conditions.

[0268] In one embodiment, the well area division step is further configured as follows:

[0269] The calculated production potential data and water injection potential data are stored in two three-dimensional arrays to realize the digitization of reservoir potential. The three-dimensional potential arrays are vertically summed and normalized to form a two-dimensional potential map corresponding to each three-dimensional potential data.

[0270] Furthermore, the initial solution generation module is configured to perform the following operations:

[0271] Set the initial values ​​of well placement parameters and potential requirements based on the geological characteristics, reserve size, fluid properties, expected recovery factor, and expected oil production rate of the target reservoir area;

[0272] Using the normalized two-dimensional production potential map, based on the well spacing constraint, grids are randomly selected in the initial well layout area to generate a corresponding number of reservoir wellhead coordinates in sequence. The potential requirements are adjusted according to the matching of the number of generated wellhead coordinates with the corresponding well layout parameter values ​​in the initial well layout plan until the matching of the two meets the set conditions. The initial data values ​​of each well layout parameter that meets the conditions in the target reservoir area are taken as the initial well layout plan.

[0273] In one embodiment, the parallel simulation calculation module is configured as follows:

[0274] The grid cumulative flow capacity data and grid cumulative storage capacity data of each well layout plan are calculated based on the flight time in the streamline simulation result file. The Lorentz coefficient of each oil well layout plan is then obtained as the corresponding fitness value. The well layout plan corresponding to the minimum Lorentz coefficient is taken as the optimal initial well layout plan.

[0275] Preferably, in one embodiment, the parallel simulation calculation module is further configured to:

[0276] The well layout parameters in the optimal initial well layout plan are stored to form a global optimal array for subsequent retrieval for comparative analysis and update. When updating, the entire set of well layout parameters is updated by direct replacement so that the historically optimal well layout parameter values ​​are always retained in the array.

[0277] In one embodiment, the parameter optimization module is configured as follows:

[0278] Based on the principles of adjusting the inertia weight, increasing information exchange between particles and expanding the search space, the velocity update formula and position update formula of the improved particle swarm algorithm are set, and the well layout parameters of the optimized well layout scheme are calculated by combining the two.

[0279] Furthermore, in one embodiment, the parameter optimization module is configured to evaluate each optimized well layout plan based on the fitness data by performing the following operations:

[0280] The constructed reservoir streamline simulation model is used to calculate the fitness values ​​corresponding to each optimized well layout scheme. The calculated results are compared and analyzed with the fitness values ​​of the well layout parameters in the known global optimal array to obtain the evaluation results of the current optimized well layout scheme.

[0281] In combination with practical applications, in one embodiment, the well layout parameters further include well tail coordinates, and the initial plan generation module is further configured to:

[0282] A random function is used to select well layout trajectory parameters that meet the set trajectory conditions in the corresponding initial well layout area, and based on the parameters, the matching effective well tail coordinates are determined in combination with the well head coordinates, which together with the well head coordinates represent the well type data of the initial well layout plan.

[0283] In one embodiment, the well layout parameters further include perforation layer data, and the initial plan generation module is further configured to:

[0284] Based on the selected well trajectory parameters and combined with the production potential data of each well layout plan, the inverse distance weighted algorithm is used to determine the perforation layer data of each initial well layout plan.

[0285] In one embodiment, the well layout parameters also include production system parameters, and the initial plan generation module is further configured to:

[0286] The total production and injection volumes of the target reservoir area are calculated based on the reservoir geological reserves, expected oil production rate, and expected injection-production ratio. Furthermore, the corresponding production system parameters are determined based on the production potential data corresponding to each initial well layout plan and the calculated total injection-production volume.

[0287] In the well location optimization system based on displacement balance analysis provided by the embodiment of the present invention, each module or unit structure can operate independently or in combination according to the actual well location optimization requirements of the project to achieve corresponding technical effects.

[0288] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should extend to equivalent substitutions of these features understood by those skilled in the relevant art. It should also be understood that the terminology used herein is for the purpose of describing specific embodiments only and is not intended to be limiting.

[0289] The phrase "one embodiment" mentioned in the specification means that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present invention. Therefore, the phrase "one embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0290] Although the embodiments disclosed herein are as described above, the contents described herein are merely embodiments for facilitating understanding of the present invention and are not intended to limit the present invention. Any person skilled in the art may make any modifications and variations in the form and details of the embodiments without departing from the spirit and scope of the present invention. However, the scope of patent protection of the present invention shall remain subject to the scope defined by the appended claims.

Claims

1. A well location optimization method based on displacement balance analysis, characterized in that: The method comprises: Simulation model construction steps: building a three-dimensional digital reservoir geological model based on the set reservoir geological data, and integrating it with the set dynamic production data to build a reservoir flow simulation model of the target reservoir area; The well area division step includes calculating the production potential data and water injection potential data of each grid in the target reservoir area based on the established three-dimensional digital reservoir geological model and the set strategy according to the grid parameters, and selecting the initial well area based on the data; An initial plan generation step includes setting initial values ​​of well placement parameters and potential requirements based on the set reservoir data and development demand data, and fitting and adjusting the well placement parameters and potential requirements based on the set plan in the initial well placement area to form an initial well placement plan corresponding to each well; wherein the well placement parameters include wellhead coordinates, wellbore length, wellbore azimuth, completion interval, and well working system data; Parallel simulation calculation steps, parallel operation of the reservoir streamline simulation model to perform streamline simulation calculations on each initial well layout plan, obtain fitness data representing the degree of balance of the reservoir area, and select the optimal initial well layout plan corresponding to each well; a parameter optimization step, optimizing the well layout parameters of each optimal initial well layout plan using a set improved particle swarm algorithm, iteratively generating a series of optimized well layout plans, and updating the optimal initial well layout plan based on the optimized well layout plans; evaluating each optimized well layout plan based on fitness data representing the degree of reservoir displacement balance, and determining a final target well layout plan based on the evaluation results; The method further comprises: after establishing the reservoir streamline simulation model: Obtain reservoir fluid parameters and rock physical parameters through experiments, determine reservoir initial conditions and corresponding oil test data and production test data; The corresponding experimental reservoir flow simulation model is constructed based on the reservoir geological data and dynamic production data corresponding to the experiment, and the flow simulation calculation of oil, gas and water in the experimental reservoir is carried out based on the streamline simulation model; The simulation results are compared with the experimentally measured historical oil and gas production data to ensure that the operating status of the constructed experimental reservoir flow line simulation model meets the set conditions.

2. The method according to claim 1, characterized in that The step of dividing the well area further includes: The calculated production potential data and water injection potential data are stored in two three-dimensional arrays to realize the digitization of reservoir potential. The three-dimensional potential arrays are vertically summed and normalized to form a two-dimensional potential map corresponding to each three-dimensional potential data.

3. The method according to claim 1, characterized in that The initial solution generation step includes: Set the initial values ​​of well placement parameters and potential requirements based on the geological characteristics, reserve size, fluid properties, expected recovery factor, and expected oil production rate of the target reservoir area; Using the normalized two-dimensional production potential map, based on the well spacing constraint, grids are randomly selected in the initial well layout area to generate a corresponding number of reservoir wellhead coordinates in sequence. The potential requirements are adjusted according to the matching of the number of generated wellhead coordinates with the corresponding well layout parameter values ​​in the initial well layout plan until the matching of the two meets the set conditions. The initial data values ​​of each well layout parameter that meets the conditions in the target reservoir area are taken as the initial well layout plan.

4. The method according to claim 1, wherein The parallel simulation calculation step includes: The grid cumulative flow capacity data and grid cumulative storage capacity data of each well layout plan are calculated based on the flight time in the streamline simulation result file. The Lorentz coefficient of each oil well layout plan is then obtained as the corresponding fitness value. The well layout plan corresponding to the minimum Lorentz coefficient is taken as the optimal initial well layout plan.

5. The method according to claim 1, wherein In the parallel simulation calculation step, it also includes: The well layout parameters in the optimal initial well layout plan are stored to form a global optimal array for subsequent retrieval for comparative analysis and update. During the update, the entire set of well layout parameters is updated by replacement so that the historically optimal well layout parameter values ​​are always retained in the array.

6. The method according to claim 1, characterized in that In the parameter optimization step, the velocity update formula and position update formula of the improved particle swarm algorithm are set based on the principles of adjusting the inertia weight, increasing information exchange between particles, and expanding the search space. The well layout parameters of the optimized well layout plan are calculated by combining the two.

7. The method according to claim 1, characterized in that The process of evaluating each optimized well layout plan based on fitness data includes: The constructed reservoir streamline simulation model is used to calculate the fitness values ​​corresponding to each optimized well layout scheme, and the calculated results are compared and analyzed with the fitness values ​​of the well layout parameters in the known global optimal array to obtain the evaluation results of the current optimized well layout scheme. Until the evaluation results meet the set final well layout conditions, the optimal well layout scheme obtained will be used as the final target well layout scheme.

8. The method according to any one of claims 1 to 7, characterized in that The well layout parameters also include well tail coordinates. In the initial plan generation step, a random function is used to select well layout trajectory parameters that meet the set trajectory conditions in the corresponding initial well layout area, and based on them combined with the coordinates of each well head, the matching effective well tail coordinates are determined, which together with the well head coordinates represent the well type data of the initial well layout plan.

9. The method according to claim 8, characterized in that The well layout parameters also include perforation layer data. In the initial plan generation step, based on the selected well layout trajectory parameters and combined with the production potential data of each well layout plan, the perforation layer data of each initial well layout plan is determined using an inverse distance weighted algorithm.

10. The method according to claim 9, characterized in that The well layout parameters also include production system parameters. In the initial plan generation step, the total production and injection volumes of the target reservoir area are calculated based on the geological reserves of the reservoir, the expected oil production rate and the expected injection-production ratio, and then the corresponding production system parameters are determined based on the production potential data corresponding to each initial well layout plan and the calculated total injection-production volume.

11. A well location optimization system based on displacement balance analysis, characterized in that: The system executes the method according to any one of claims 1 to 10.

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