Water control parameter optimization method, device and terminal equipment for horizontal well in water drive reservoir

Abstract

The application provides a water control parameter optimization method and device for a horizontal well of an oil reservoir and terminal equipment, and relates to the technical field of oil and natural gas. The method comprises the following steps: determining a target oil-increasing influence factor of an oil well and an oil-increasing contribution rate of each target oil-increasing influence factor; determining the weight of each target oil-increasing influence factor based on the oil-increasing contribution rate of each target oil-increasing influence factor, and performing weighted summation on the parameter values of each target oil-increasing influence factor based on the weight of each target oil-increasing influence factor to obtain an oil-increasing potential score corresponding to the oil well and determine a target oil well; constructing a target function with the maximum oil-increasing rate as the target, and performing optimization on the target function through a pre-constructed numerical simulation model of the oil reservoir and a first optimization algorithm to determine the optimal annular packer placement position of the target oil well, and performing optimization on the target function through the numerical simulation model of the oil reservoir and a second optimization algorithm to determine the optimal water control tool parameter of the target oil well. The application effectively improves the water control and oil-increasing effect of the oil well.

Description

Technical Field

[0001] This application relates to the field of oil and gas technology, specifically to a method for optimizing water control parameters in a horizontal well of a water-drive reservoir, a device for optimizing water control parameters in a horizontal well of a water-drive reservoir, a computer-readable storage medium, and a terminal device. Background Technology

[0002] Currently, most offshore oilfields have a recovery rate exceeding 40% in their main formations, with ultra-high water-cut horizontal wells accounting for over 70%. Effectively managing excessive water production in horizontal wells is a pressing challenge for achieving "water control and oil enhancement" and improving reservoir recovery. While mechanical water control tools have demonstrated good water control effectiveness, the oil enhancement effect varies greatly between different water-controlled wells due to differences in geological factors and the quality of water control schemes. Conducting adaptability research on water control tools and developing the optimal water control scheme for oil enhancement is a key challenge and bottleneck for this technology. Currently, both domestic and international efforts are actively exploring the microscopic water production patterns in bottom-water reservoirs, providing guidance for selecting reasonable and feasible water control and shut-off schemes for these reservoirs. Among them, Giger proposed a two-dimensional mathematical model to describe the water ridge and analyzed the impact of bottom water ridge entry on oil production in horizontal wells; Cheng Linsong et al. used the principle of mirror reaction and potential superposition to solve the seepage process of horizontal wells in bottom water drive reservoirs and derived relevant production capacity and water breakthrough time formulas; Racham et al. used numerical simulation methods to establish a three-dimensional Cartesian coordinate horizontal well model, analyzed various reservoir factors affecting the water ridge, and obtained the relationship between water breakthrough time and critical production; Jiang Hanqiao et al. focused on analyzing the influence of geological factors on the water flooding mode and described the distribution area of ​​remaining oil; Permadi et al. constructed a horizontal well model based on physical simulation methods and compared the influence of different water avoidance heights, crude oil viscosity, horizontal section lengths and other factors on horizontal well water flooding; Yu Huajie established a three-dimensional visualization physical simulation experimental device to analyze the water flooding law of horizontal wells in homogeneous bottom water reservoirs and clarified the characteristics of the water flooding mode as uniform uplift - water breakthrough in the middle - expansion on both sides - full well water flooding - uplift on both wings. The study of water production patterns in horizontal wells has yielded significant results in mathematical theory, reservoir numerical simulation, and physical simulation experiments. However, there is currently a lack of systematic analysis of the influence of geological and engineering factors on water production in horizontal wells, making it impossible to screen target wells for water control based on oil production potential evaluation.

[0003] On the other hand, the common mechanical tools currently used for water control in offshore oilfields mainly include ICD (Inflow Control Device), AICD (Autonomous Inflow Control Device), and C-AICD (Composite Autonomous Inflow Control Device). Among them, ICDs are mainly nozzle-type ICDs developed by companies such as Schlumberger and Tendeka; AICDs are mainly floating disc-type AICDs developed by Statoil and flow channel-type AICDs developed by Halliburton; and C-AICDs are mainly domestically developed composite water control screen structures. In terms of oil control effectiveness, field application results vary widely. A small number of water-controlled wells have increased oil production by more than 40,000 cubic meters, while some wells have only increased by 3,000 cubic meters, showing a significant difference in oil production increase among different water-controlled wells. The reasons for the large differences in effectiveness are twofold. First, the reservoirs where horizontal wells are located vary. For example, when point flooding occurs, water control measures can effectively suppress water production in water-producing layers and promote oil production in non-water-producing layers. However, when linear flooding occurs, the potential for increased oil production is very low. Second, the quality of the water control design scheme also plays a role. The placement of packers, the selection of water control tools, and the setting of water control parameters all affect the effectiveness of the water control scheme. Designing the optimal water control parameters is very difficult. Summary of the Invention

[0004] The purpose of this application is to provide a method for optimizing water control parameters in horizontal wells of water-drive reservoirs, a device for optimizing water control parameters in horizontal wells of water-drive reservoirs, a computer-readable storage medium, and a terminal device to solve the above-mentioned problems.

[0005] To achieve the above objectives, the first aspect of this application provides a method for optimizing water control parameters in horizontal wells of water-drive reservoirs, comprising:

[0006] An oil reservoir model including multiple oil wells is established and observation data is obtained. Production is simulated through the oil reservoir model to obtain production data. Based on the observation data and production data, an automatic history fitting result is obtained through a preset automatic history fitting algorithm. An oil reservoir numerical simulation model is established based on the automatic history fitting result.

[0007] Determine the target oil-increasing factors for oil wells and the oil-increasing contribution rate of each target oil-increasing factor;

[0008] Obtain the parameter values ​​of each target oil-increasing influencing factor for different oil wells, determine the weight of each target oil-increasing influencing factor based on the oil-increasing contribution rate of each target oil-increasing influencing factor, and perform a weighted summation of the parameter values ​​of each target oil-increasing influencing factor with the weight of each target oil-increasing influencing factor to obtain the oil-increasing potential score of the corresponding oil well, and determine the oil wells with potential scores higher than the potential score threshold as target oil wells;

[0009] An objective function is constructed with the goal of maximizing the oil production rate. The objective function is then optimized using a pre-constructed reservoir numerical simulation model and a first optimization algorithm to determine the optimal annular packer placement position for the target oil well. Additionally, the objective function is optimized using the reservoir numerical simulation model and a second optimization algorithm to determine the optimal water control tool parameters for the target oil well.

[0010] Optionally, before simulating production using the reservoir model, the method further includes:

[0011] The region in the reservoir model where the horizontal well trajectory is non-linear is identified as the region to be adjusted, and the rotation angle of each grid cell in the region to be adjusted is determined.

[0012] Based on the rotation angle of each grid cell in the area to be adjusted, a rotation operation is performed on each grid cell to convert the corresponding horizontal well trajectory in the area to be adjusted into a straight line;

[0013] The neighboring grid cells of each grid cell in the region to be adjusted are determined, the attribute value of each neighboring grid cell is obtained, and the predicted attribute value of each grid cell in the region to be adjusted is determined by bilinear interpolation based on the attribute values ​​of the neighboring grid cells of each grid cell.

[0014] Optionally, the automatic history fitting algorithm is an ensemble Kalman filter algorithm;

[0015] Based on the observation data and production data, an automatic historical fitting result is obtained through a preset automatic historical fitting algorithm, including:

[0016] Determine the set state vector of the set Kalman filter algorithm, wherein the set state vector includes the static parameters, dynamic parameters and production data of the reservoir model;

[0017] Based on the aforementioned ensemble Kalman filtering algorithm, the error covariance matrix of the ensemble state vector and the error covariance matrix of the ensemble production data are obtained, wherein the ensemble production data is a set of production data obtained by continuously updating the model set during the inversion process;

[0018] The Kalman gain of the Kalman filter algorithm for the set is determined based on the error covariance matrix of the set state vector, the error covariance matrix of the set production data, and the observation data; based on the Kalman gain and the static and dynamic parameters of the reservoir model, the static and dynamic parameters of the reservoir model are updated to obtain the automatic historical fitting results.

[0019] Optionally, the target oil production enhancement factors for oil wells are determined, including:

[0020] Construct a single-well model of the oil well and determine multiple geological and engineering parameters of the single-well model;

[0021] Under specified water control tool parameters, reservoir numerical simulation is performed on the single-well model based on different geological and engineering parameters to obtain the oil production rate of the single-well model under different geological and engineering parameters;

[0022] The obtained oil enhancement rates are sorted in descending order, and the geological or engineering parameters corresponding to the top n oil enhancement rates are determined as the target oil enhancement influencing factors.

[0023] Optionally, the contribution rate of each target oil-increasing factor to oil production can be determined, including:

[0024] Based on at least one target oil-increasing influencing factor, at least one target mechanism model is constructed using orthogonal design method;

[0025] Reservoir numerical simulations were performed for each target mechanism model under different water control tool parameters to obtain the oil enhancement rate of each target mechanism model under different water control tool parameters. Based on the oil enhancement rate of each target mechanism model under different water control tool parameters, the oil enhancement contribution rate of each target oil enhancement influencing factor was determined.

[0026] Optionally, the geological parameters include:

[0027] Permeability, crude oil viscosity, water-sheltered height, permeability gradient, rhythmicity, and location of interlayers;

[0028] The engineering parameters include:

[0029] Horizontal well length, well trajectory morphology, water cut, and production rate;

[0030] The water control tool includes:

[0031] Passive water control tool ICD, autonomous water control tool AICD, and composite autonomous water control tool C-AICD;

[0032] The parameters of the water control tool include:

[0033] The type of water control tool, its location, and its opening degree.

[0034] Optionally, reservoir numerical simulations are performed on the single-well model based on different geological and engineering parameters to obtain the oil production rate of the single-well model under different geological and engineering parameters, including:

[0035] For each geological parameter and engineering parameter, other geological parameters and engineering parameters besides the current geological parameter or engineering parameter are fixed, and reservoir numerical simulation is performed on the single well model under different values ​​of the current geological parameter or engineering parameter to obtain the different oil enhancement rates of each type of water control tool under different values ​​of the current geological parameter or engineering parameter.

[0036] Determine the maximum and minimum oil enhancement rates for each category of water control tools, and then determine the oil enhancement rate for the current geological or engineering parameters based on the maximum and minimum oil enhancement rates for each category of water control tools.

[0037] Optionally, the oil enhancement rate of the current geological or engineering parameters is determined based on the maximum and minimum oil enhancement rates of each type of water control tool, including:

[0038] The oil enhancement rate of the current geological or engineering parameters is determined using the following formula:

[0039]

[0040] Where n represents the total number of categories of water control tools, r i This represents the oil enhancement rate of a certain water control tool; r avg This represents the average value of the difference in oil yield, %; (r i ) max Represents the maximum oil increase rate of the i-th type of water control tool, (r i ) min This represents the minimum oil enhancement rate of the i-th type of water control tool; This represents the average value of the difference in oil production rate between water control tools, expressed as... Oil enhancement rate, as a current geological or engineering parameter.

[0041] Optionally, based on at least one target oil-increasing influencing factor, at least one target mechanism model is constructed using orthogonal design method, including:

[0042] The different factor levels of each target oil-increasing factor are determined based on the different parameter value ranges of each target oil-increasing factor.

[0043] For each target oil increase factor, the different factor levels of the current target oil increase factor are combined with the different factor levels of other target oil increase factors through orthogonal design to obtain at least one factor level combination corresponding to the current target oil increase factor.

[0044] For each target oil-increasing influencing factor, the factor level combination corresponding to it is deduplicated so that the factor level combination corresponding to each target oil-increasing influencing factor is different. Based on each deduplicated factor level combination, at least one target mechanism model corresponding to each target oil-increasing influencing factor is established.

[0045] Optionally, reservoir numerical simulations are performed on each target mechanism model under different water control tool parameters to obtain the oil enhancement rate of each target mechanism model under different water control tool parameters, including:

[0046] Without water control tools, reservoir numerical simulations were performed on each target mechanism model to obtain the cumulative oil production of each target mechanism model under the condition of water control tools; and

[0047] In the presence of water control tools, for each target mechanism model, reservoir numerical simulation is performed under each type of water control tool to obtain the cumulative oil production of each target mechanism model under each type of water control tool.

[0048] Based on the cumulative oil production of each target mechanism model under each type of water control tool and the cumulative oil production of each target mechanism model without water control tool, the oil enhancement rate of each target mechanism model under each type of water control tool is determined. Based on the oil enhancement rate of each target mechanism model under each type of water control tool, the maximum and minimum oil enhancement rates corresponding to each target oil enhancement influencing factor under each type of water control tool are determined.

[0049] Optionally, determine the maximum and minimum oil increase rates corresponding to each target oil increase factor under each category of water control tools, including:

[0050] Under each category of water control tools:

[0051] Fix the factor levels of other target oil-increasing factors besides the current target oil-increasing factor to obtain at least one fixed factor level combination of other target oil-increasing factors besides the current target oil-increasing factor;

[0052] For each fixed factor level combination, calculate the difference in oil enhancement rate of the current target oil enhancement factor under different factor levels;

[0053] The maximum value among all obtained fuel efficiency differences is determined as the maximum fuel efficiency of the current target fuel efficiency influencing factor, and the minimum value among all obtained fuel efficiency differences is determined as the minimum fuel efficiency of the current target fuel efficiency influencing factor.

[0054] Optionally, based on the oil enhancement rate of each target oil enhancement factor under different water control tool parameters, the oil enhancement contribution rate of each target mechanism model is determined, including:

[0055] The contribution rate of each target oil-increasing factor to oil production under different categories of water control tools is determined using the following formula:

[0056]

[0057] Among them, C i S represents the contribution rate of the oil-increasing factor for the i-th objective, dimensionless; i Let S be the oil increase rate corresponding to the i-th target oil increase influencing factor, %; (S i ) max Let S be the maximum oil increase rate of the i-th target oil increase factor. i ) min Let be the minimum oil increase rate of the i-th target oil increase factor, and n be the total number of target oil increase factors.

[0058] Optionally, the parameter values ​​of each target oil-increasing influencing factor for different oil wells are obtained. The weight of each target oil-increasing influencing factor is determined based on its contribution rate. The parameter values ​​of each target oil-increasing influencing factor are then weighted and summed to obtain the corresponding oil well's oil-increasing potential score, including:

[0059] Obtain the parameter values ​​of each target oil-increasing influencing factor for different oil wells, and determine the score of each target oil-increasing influencing factor for different oil wells through a preset target oil-increasing influencing factor evaluation table. The target oil-increasing influencing factor evaluation table includes at least the scores corresponding to the parameter values ​​of different target oil-increasing influencing factors.

[0060] The weights of different types of water control tools are determined. The oil production contribution rate of each target oil production factor under different types of water control tools is weighted and summed to obtain the weight of each target oil production factor. The scores of each target oil production factor are weighted and summed to obtain the oil production potential score of the corresponding oil well.

[0061] Optionally, the first optimization algorithm is a particle swarm optimization algorithm based on simulated annealing and elite back learning. It optimizes the objective function using a pre-built reservoir numerical simulation model and the first optimization algorithm to determine the optimal annular packer placement position for the target oil well, including:

[0062] S1. Initialize particle swarm parameters, which include the number of particles in the particle swarm and the initial position and initial velocity of each particle, and map the different annular packer placement positions of the target oil well to different dimensions of each particle.

[0063] S2. Determine the fitness value of each particle in the particle swarm through the reservoir numerical simulation model, and determine the individual extreme value and the population extreme value based on the objective function value and fitness value of each particle.

[0064] S3. Determine the simulation temperature of the simulated annealing algorithm based on the population optimal solution;

[0065] S4. Calculate the annealing fitness of each particle at the simulated temperature, and replace the population extremum based on the preset strategy according to the annealing fitness of each particle.

[0066] S5. Update the velocity and position of each particle, determine the fitness value of each particle based on the updated objective function value, and update the individual extreme value and the population extreme value according to the fitness value of each particle.

[0067] S6. Generate a random real number in the range [0,1]. If the random real number is less than a preset threshold, proceed to step S7; otherwise, proceed to step S9.

[0068] S7. Sort the fitness values ​​of each particle from high to low, and perform reverse learning on the top n particles to obtain n particles after reverse learning.

[0069] S8. Determine the fitness value of each inversely learned particle based on the objective function, compare the fitness value of each inversely learned particle with the current fitness value of each particle, and update the particles in the current particle swarm according to the comparison result.

[0070] S9. Determine whether the maximum number of iterations has been reached. If the maximum number of iterations has not been reached, reduce the simulation temperature and return to step S4. If the maximum number of iterations has been reached, determine the optimal annular packer placement position for the target oil well based on the population extremum in the current particle swarm.

[0071] Optionally, the fitness values ​​of each back-learned particle are compared with the current fitness values ​​of each particle, and the particles in the current particle swarm are updated based on the comparison results, including:

[0072] Each inversely learned particle is compared with the fitness values ​​of the last n particles in descending order of fitness value. If the fitness value of the current inversely learned particle is better than the fitness value of any of the last n particles, the inversely learned particle replaces the corresponding particle in the last n particles.

[0073] Optionally, update the velocity and position of each particle, including:

[0074] The velocity of each particle is updated using the following formula:

[0075] v id (t+1)=wv id (t)+c1r1((0.5+r3 / 2)p id -x id (t))+c2r2((0.5+r4 / 2)p gd -x id (t))

[0076] The position of each particle is updated using the following formula:

[0077] x id (t+1)=x id (t)+v id (t+1)

[0078] Among them, v id (t+1) represents the velocity of the i-th particle in dimension d at iteration t+1, v id (t) represents the velocity of the i-th particle in dimension d at iteration t, w is the inertia weight, c1 and c2 are learning factors, r1, r2, r3, and r4 are random numbers varying in [0,1], and p id Let p be the individual extreme value of the i-th particle in dimension d. gd For the population extremum, x id (t) represents the position of the i-th particle in dimension d, x id (t+1) represents the velocity of the i-th particle in dimension d at iteration t+1.

[0079] Optionally, the method further includes:

[0080] The inertia weights are updated using the following formula:

[0081] w=w max -p(iter max -iter)

[0082] p=(w max -w min ) / sum(1:iter)

[0083] Among them, w max For the maximum inertia weight, w min For the minimum inertia weight, iter max The maximum number of iterations is given by 'iter', and the current iteration number is given by 'sum(1:iter)'. The sum of 'sum' from 1 to 'iter' is the cumulative sum.

[0084] Optionally, the second optimization algorithm is a multi-objective particle swarm optimization algorithm with elite back-learning capabilities.

[0085] A second aspect of this application provides a device for optimizing water control parameters in a horizontal well of a water-drive reservoir, comprising:

[0086] The model building module is configured to build a reservoir model including multiple oil wells and acquire observation data, simulate production through the reservoir model to obtain production data, obtain automatic history fitting results through a preset automatic history fitting algorithm based on the observation data and production data, and build a reservoir numerical simulation model based on the automatic history fitting results.

[0087] The oil production contribution rate calculation module is configured to determine the target oil production impact factors of the oil well and the oil production contribution rate of each target oil production impact factor;

[0088] The oil well scoring module is configured to obtain the parameter values ​​of each target oil-increasing influencing factor for different oil wells, and to perform a weighted summation of the parameter values ​​of each target oil-increasing influencing factor based on the oil-increasing contribution rate of each target oil-increasing influencing factor to obtain the oil-increasing potential score of the corresponding oil well, and to determine the oil wells with potential scores higher than the potential score threshold as target oil wells.

[0089] The water control parameter optimization module is configured to construct an objective function with the goal of maximizing the oil production rate. The objective function is optimized using a pre-constructed reservoir numerical simulation model and a first optimization algorithm to determine the optimal annular packer placement position for the target oil well. The objective function is also optimized using the reservoir numerical simulation model and a second optimization algorithm to determine the optimal water control tool parameters for the target oil well.

[0090] In a third aspect, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to execute the water control parameter optimization method for a horizontal well in a water-drive reservoir as described above.

[0091] In a fourth aspect, this application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described method for optimizing water control parameters in a horizontal well of a water-drive reservoir.

[0092] The embodiments provided in this application have the following beneficial effects:

[0093] This application evaluates the oil well's oil-increasing potential by determining the target oil-increasing influencing factors and the oil-increasing contribution rate of each target oil-increasing influencing factor. The oil well with the highest oil-increasing potential is selected as the target oil well, and an objective function is constructed with the maximum oil-increasing rate as the objective. The objective function is optimized through an improved optimization algorithm and a reservoir numerical simulation model, thereby obtaining the optimal annular packer placement position and optimal water control tool parameters for the target oil well, effectively improving the water control and oil-increasing effect of the oil well.

[0094] Other features and advantages of the embodiments or implementations of this application will be described in detail in the following detailed description section. Attached Figure Description

[0095] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0096] Figure 1This illustration schematically shows a flowchart of a method for optimizing water control parameters in a horizontal well of a water-drive reservoir according to an embodiment of this application.

[0097] Figure 2 This illustration shows a schematic diagram of the automatic history fitting results of an embodiment of this application;

[0098] Figure 3 This illustration schematically shows a simulation diagram of a single-well model water control tool according to an embodiment of this application;

[0099] Figure 4 The schematic diagram illustrates the cumulative oil production curves under three water control tools and without water control tools in the initial stage of production according to the embodiments of this application;

[0100] Figure 5 The schematic diagram illustrates the cumulative oil production curves under three water control tools and without water control tools during the ultra-high water cut period according to the embodiments of this application.

[0101] Figure 6 The schematic diagram illustrates the moisture content curves under three water control tools and without water control tools during the ultra-high moisture content period according to the embodiments of this application;

[0102] Figure 7 A schematic block diagram of a water-drive reservoir horizontal well water control parameter optimization device according to an embodiment of this application is shown.

[0103] Figure 8 The schematic diagram illustrates a terminal device structure according to an embodiment of this application.

[0104] Explanation of reference numerals in the attached figures

[0105] 10 - Terminal device, 100 - Processor, 101 - Memory, 102 - Computer program. Detailed Implementation

[0106] The specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the embodiments of this application.

[0107] To solve the above problems, such as Figure 1 As shown, the first aspect of this application provides a method for optimizing water control parameters in a horizontal well of a water-drive reservoir, comprising:

[0108] S100. Establish a reservoir model including multiple oil wells and obtain observation data. Simulate production through the reservoir model to obtain production data. Based on the observation data and production data, obtain automatic history fitting results through a preset automatic history fitting algorithm. Establish a reservoir numerical simulation model based on the automatic history fitting results.

[0109] S200, Determine the target oil-increasing influencing factors of the oil well and the oil-increasing contribution rate of each target oil-increasing influencing factor;

[0110] S300. Obtain the parameter values ​​of each target oil-increasing influencing factor for different oil wells. Determine the weight of each target oil-increasing influencing factor based on its contribution rate. Sum the parameter values ​​of each target oil-increasing influencing factor with their respective weights to obtain the corresponding oil-increasing potential score. Determine the oil wells with potential scores higher than the potential score threshold as target oil wells.

[0111] S400. Construct an objective function with the goal of maximizing oil production rate. Optimize the objective function using a pre-constructed reservoir numerical simulation model and a first optimization algorithm to determine the optimal annular packer placement position for the target oil well. Optimize the objective function using a reservoir numerical simulation model and a second optimization algorithm to determine the optimal water control tool parameters for the target oil well.

[0112] Thus, this application evaluates the oil well's oil-increasing potential by determining the target oil-increasing influencing factors and the oil-increasing contribution rate of each target oil-increasing influencing factor. The oil well with the highest oil-increasing potential is selected as the target oil well, and an objective function is constructed with the maximum oil-increasing rate as the objective. The objective function is optimized through an improved optimization algorithm and a reservoir numerical simulation model, thereby obtaining the optimal annular packer placement position and optimal water control tool parameters for the target oil well, effectively improving the water control and oil-increasing effect of the oil well.

[0113] In step S100, the reservoir model can be constructed using existing reservoir numerical simulation software. Since the well trajectory of a horizontal well may be a complex polygonal line, in order to reduce the consumption of computing resources and improve computing efficiency, this application first simplifies the reservoir model. Therefore, before simulating production using the reservoir model, the method further includes: determining the region in the reservoir model where the horizontal well trajectory is non-linear as the region to be adjusted; determining the rotation angle of each grid cell in the region to be adjusted; performing a rotation operation on each grid cell based on the rotation angle of each grid cell in the region to be adjusted to convert the corresponding horizontal well trajectory in the region to be adjusted into a straight line; determining the adjacent grid cells of each grid cell in the region to be adjusted; obtaining the attribute values ​​of each adjacent grid cell, such as permeability, porosity, saturation, etc.; and determining the predicted attribute values ​​of each grid cell in the region to be adjusted using bilinear interpolation based on the attribute values ​​of the adjacent grid cells of each grid cell. Since the new position of a mesh cell after rotation may not completely correspond to the original position, this application uses bilinear interpolation to estimate the attribute values ​​of the rotated mesh cells at their new positions. For example, assuming the rotated mesh cell consists of four adjacent mesh cells, the attribute value f(x, y) of the rotated mesh cell at its new position can be calculated using the following formula:

[0114]

[0115] Where (x, y) represents the position of the rotated mesh cell, and (x0, y0), (x1, y0), (x0, y1), and (x1, y1) represent the positions of the adjacent mesh cells of the rotated mesh cell. , f 00 f 10 f 01 f 11 These represent the attribute values ​​of adjacent grid cells.

[0116] In this application, the automatic history fitting algorithm is an ensemble Kalman filter algorithm. In step S100, based on the observation data and production data, the automatic history fitting result is obtained through a preset automatic history fitting algorithm, including:

[0117] S110. Determine the ensemble state vector for the ensemble Kalman filter algorithm. The ensemble state vector includes the static parameters, dynamic parameters, and production data of the reservoir model. Static parameters are those that do not change over time during the history fitting process, including fluid interface location, discretized reservoir porosity field, and permeability field. Dynamic parameters include pressure and saturation. Production data includes fluid production. For example, the ensemble state vector can be represented as... Where Ne represents the number of members in the set, y n，j Let m represent the state vector of set member j at time n, and m represent the state vector of set N. m The static parameter of dimension p n N represents p The dynamic parameter of dimension d n N represents d Dimensional production data.

[0118] S120. Based on the ensemble Kalman filter algorithm, obtain the error covariance matrix of the ensemble state vector and the error covariance matrix of the ensemble production data. The ensemble production data is the set of production data obtained by continuously updating the model set during the inversion process. It can be understood that the purpose of the ensemble Kalman filter algorithm is to obtain a series of reservoir models that can fit the historical production data of the oilfield and determine the optimal reservoir model.

[0119] S130. Determine the Kalman gain of the ensemble Kalman filter algorithm based on the error covariance matrix of the ensemble state vector, the error covariance matrix of the ensemble production data, and the observation data. The Kalman gain can be expressed as:

[0120]

[0121] Where, k n Indicates Kalman gain, , Let C be the error covariance matrix of the predicted set of state vectors. Dn The error covariance matrix of the production data set. H is the mean of the state vector, and H is a matrix operator that represents the correlation between the state vector and the observation data, which can be obtained from the actual data of the reservoir.

[0122] For example, H can be represented as:

[0123]

[0124] Where O is N d ×(N m +N p A zero matrix of dimension N, where I is N d ×N d An identity matrix of dimension 1, where v(n) is the observation error, and v(n) ~ N(0, C). Dn ).

[0125] The predicted state vector can be updated using the following formula:

[0126]

[0127] in, Let j be the predicted state vector of member j of the set at time n. For a sample of observational data with perturbation, the perturbed observational data can be obtained for any set member j in the following way:

[0128]

[0129] in, , Decomposing C using the square root method Dn The square root matrix L can be obtained. D Z D For N d A 1×1-dimensional polynomial random variable with a mean of 0 and a standard deviation of 1.

[0130] S140. Based on Kalman gain and the static and dynamic parameters of the reservoir model, the static and dynamic parameters of the reservoir model are updated to obtain the automatic history fitting result. By continuously updating the ensemble state vector, the updated static and dynamic parameters can be obtained. This process is repeated until the optimal static and dynamic parameters are output, which are then used as the automatic history fitting result. The automatic history fitting results of the production dynamics and production profile of this application are as follows: Figure 2 As shown.

[0131] In step S200, the target oil production enhancement factors for the oil well are determined, including:

[0132] S210. Construct a single-well model of the oil well and determine multiple geological and engineering parameters for the single-well model. Geological parameters include, but are not limited to: permeability, crude oil viscosity, water-avoidance height, permeability gradient, rhythmicity, and interlayer location; engineering parameters include, but are not limited to: horizontal well length, well trajectory morphology, water cut, and production rate; the single-well model adopts a multi-section well model (MSW), such as... Figure 3 As shown, parallel well sections are added between the wellbore node and the annulus node as water control tool nodes, and the placement of packers in horizontal wells is designed in combination to simulate three different water control tools. The construction of single-well models is an existing technology and is not limited here.

[0133] S220. Under specified water control tool parameters, reservoir numerical simulation is performed on a single-well model based on different geological and engineering parameters to obtain the oil production rate of the single-well model under different geological and engineering parameters. The water control tools include: passive water control tool ICD, autonomous water control tool AICD, and composite autonomous water control tool C-AICD; the water control tool parameters include: the type of water control tool, the location of the water control tool, and the opening degree of the water control tool. The type of water control tool specifies that the water control tool is ICD, AICD, or C-AICD. This application sets two high-permeability strips in the single-well model for water control tools to be installed at these locations in the wellbore. Initially, each type of water control tool has an opening parameter of 2 per 10 meters at the wellbore location corresponding to the high-permeability strip. No water control tools are installed at other locations in the wellbore. This application, by fixing the parameters of the water control tool and employing the controlled variable method, analyzes the influence of six geological factors (permeability, crude oil viscosity, water-avoidance height, permeability gradient, rhythmicity, and interlayer location) and four engineering factors (horizontal well length, well trajectory morphology, water cut, and production rate) on the oil enhancement effect. Specifically, it conducts reservoir numerical simulations on a single-well model based on different geological and engineering parameters to obtain the oil enhancement rate of the single-well model under different geological and engineering parameters, including:

[0134] S221. For each geological and engineering parameter, fix all other geological and engineering parameters except the current geological or engineering parameter. Perform reservoir numerical simulation on the single-well model under different values ​​of the current geological or engineering parameter to obtain the different oil enhancement rates of each type of water control tool under different values ​​of the current geological or engineering parameter. Specifically, the oil enhancement rate of each factor can be simulated using existing reservoir numerical simulation software such as Eclipse, GPTSim, and CMG. This is not limited here. For example, by calling the reservoir numerical simulation software interface and fixing all factors except permeability, simulate the oil enhancement rate of different permeabilities under ICD, AICD, and C-AICD, and so on, to obtain the oil enhancement rate of each factor at different values ​​under ICD, AICD, and C-AICD.

[0135] S222. Determine the maximum and minimum oil enhancement rates for each category of water control tools. Based on the maximum and minimum oil enhancement rates for each category of water control tools, determine the oil enhancement rate for the current geological or engineering parameters, including: determining the oil enhancement rate for the current geological or engineering parameters using the following formula:

[0136]

[0137] Where n represents the total number of categories of water control tools, r i This represents the oil enhancement rate of a certain water control tool; r avg This represents the average value of the difference in oil yield, %; (r i ) max Represents the maximum oil increase rate of the i-th type of water control tool, (r i ) min This represents the minimum oil enhancement rate of the i-th type of water control tool; This represents the average value of the difference in oil production rate between water control tools, expressed as... Oil enhancement rate, as a current geological or engineering parameter.

[0138] For example, taking permeability as the current factor, numerical simulations were used to obtain the maximum oil increase rate (r1) for different permeability levels under the water control tool ICD. max Minimum fuel efficiency (r1) min The maximum oil enhancement rate for different permeability levels under ICD water control is (r2). max Minimum fuel efficiency (r2) min The maximum oil enhancement rate for different permeability levels under ICD water control is (r3). max Minimum fuel efficiency (r3) min For each water control tool, calculate the difference in oil yield: (r1) max -(r1) min=△r1、(r2) max -(r2) min =△r2 and (r3) max -(r3) min =△r3, then the oil increase rate corresponding to the penetration rate is △r ave =1 / 3(△r1+△r2+△r3), and so on, we can obtain the oil increase rate corresponding to all factors.

[0139] S230. Sort the obtained oil enhancement rates in descending order, and determine the geological or engineering parameters corresponding to the top n oil enhancement rates as target oil enhancement influencing factors. In this application, permeability gradient, crude oil viscosity, horizontal well length, and daily fluid production are determined as target oil enhancement influencing factors, i.e., main controlling factors.

[0140] In step S200, the contribution rate of each target oil-increasing influencing factor is determined, including:

[0141] S240. Based on at least one target oil-increasing influencing factor, construct at least one target mechanism model using orthogonal design method, including:

[0142] S2401. Determine different factor levels for each target oil production influencing factor based on the different parameter value ranges of each target oil production influencing factor. For example, based on different values ​​of permeability difference, crude oil viscosity, horizontal well length, and daily fluid production, each main controlling factor is divided into different levels. For instance, the factor level for permeability with a permeability difference below the threshold is determined as small permeability range, and the factor level for permeability with a permeability difference below the threshold is determined as large permeability range. Similarly, the factor level for crude oil viscosity is divided into high viscosity and low viscosity; the factor level for horizontal well length is divided into long length and short length; and the factor level for daily fluid production is divided into high fluid production and low fluid production.

[0143] S2402. For each target oil-increasing influencing factor, using orthogonal design, the different factor levels of the current target oil-increasing influencing factor are combined with the different factor levels of other target oil-increasing influencing factors to obtain at least one factor level combination corresponding to the current target oil-increasing influencing factor. An orthogonal design table is established by combining the different factor levels of different controlling factors to construct a mechanistic model.

[0144] S2403. For each target oil-increasing influencing factor, deduplicate the factor level combinations to ensure that each target oil-increasing influencing factor has a different factor level combination. Based on each deduplicated factor level combination, establish at least one target mechanism model corresponding to each target oil-increasing influencing factor. For example, define a small permeability range as 11, a large permeability range as 12, low crude oil viscosity as 21, high crude oil viscosity as 22, short horizontal well length as 31, long horizontal well length as 32, low daily fluid production as 41, and high daily fluid production as 42. After deduplication, the orthogonal design table includes: {(11, 21, 31, 41), (11, 21, 32, 42), (11, 22, 31, 42), (11, 22, 32, 41), (12, 21, 31, 42), (12, 21, 32, 41), (12, 22, 31, 41), (12, 22, 32, 42), (11, 21, 32, 41), (11, 22, 31, 41), (12, 21, 31, 41), (12, 22, 32, 41), (11, 22, 32, 42), (12, 21, 32, 42), (12, 22, 31, 42), (11, 21, 31, 42)}, totaling 16 combinations. Establish target mechanism models for oil wells under each combination.

[0145] S250. Perform reservoir numerical simulations for each target mechanism model under different water control tool parameters to obtain the oil production rate of each target mechanism model under different water control tool parameters. Based on the oil production rate of each target mechanism model under different water control tool parameters, determine the oil production contribution rate of each target oil production influencing factor. Specifically, performing reservoir numerical simulations for each target mechanism model under different water control tool parameters to obtain the oil production rate of each target mechanism model under different water control tool parameters includes: performing reservoir numerical simulations for each target mechanism model without water control tools to obtain the cumulative oil production of each target mechanism model without water control tools; and simulating the cumulative oil production of each target mechanism model without water control tools using reservoir numerical simulation software. In the presence of water control tools, reservoir numerical simulations are performed for each target mechanism model under each type of water control tool to obtain the cumulative oil production of each target mechanism model under each type of water control tool. The cumulative oil production of each target mechanism model under ICD, AICD, and C-AICD water control tools is simulated using reservoir numerical simulation software. Based on the cumulative oil production of each target mechanism model under each type of water control tool and the cumulative oil production of each target mechanism model without water control tools, the oil production increase rate of each target mechanism model under each type of water control tool is determined. It can be understood that the oil production increase rate of each target mechanism model under each type of water control tool can be obtained by comparing the cumulative oil production of each target mechanism model under ICD, AICD, and C-AICD water control tools with the cumulative oil production of each target mechanism model without water control tools. Based on the oil enhancement rate of each target mechanism model under each category of water control tools, determine the maximum and minimum oil enhancement rates corresponding to each target oil enhancement influencing factor under each category of water control tools.

[0146] The process involves determining the maximum and minimum oil-increasing rates for each target oil-increasing influencing factor under each category of water control tools. This includes: under each category of water control tools: fixing the factor levels of other target oil-increasing influencing factors besides the current target oil-increasing influencing factor, resulting in at least one fixed factor level combination for other target oil-increasing influencing factors besides the current target oil-increasing influencing factor; for each fixed factor level combination, calculating the oil-increasing rate difference of the current target oil-increasing influencing factor under different factor levels; determining the maximum value among all obtained oil-increasing rate differences as the maximum oil-increasing rate of the current target oil-increasing influencing factor, and determining the minimum value among all obtained oil-increasing rate differences as the minimum oil-increasing rate of the current target oil-increasing influencing factor. For example, during calculation, based on the orthogonal design table, any controlling factor is determined as the target controlling factor. The oil-increasing rates of the target mechanism model corresponding to different factor levels of the target controlling factor under different categories of water control tools are obtained when the factor levels of other controlling factors are fixed. The differences between these oil-increasing rates are calculated, and the maximum difference is determined as the maximum oil-increasing rate of the current target controlling factor, and the minimum difference is determined as the minimum oil-increasing rate of the current target controlling factor. For example, taking permeability range as the primary controlling factor, the factor levels of crude oil viscosity, horizontal well length, and daily fluid production are fixed. The oil production rate of the corresponding target mechanism model under different types of water control tools is obtained for different permeability ranges. For instance, in the orthogonal design table, the oil production rates of the target mechanism models corresponding to (11, 21, 31, 41) and (12, 21, 31, 41) under different types of water control tools are calculated. The difference in oil production rates between the two target mechanism models is calculated for each type of water control tool. Then, (11, 21, 32, 42) and (12, 21, 32, 42) are obtained. The corresponding target mechanism model is used to calculate the oil enhancement rate under different categories of water control tools. The difference in oil enhancement rate between the two target mechanism models is calculated for each category of water control tool. Similarly, the difference in oil enhancement rate of permeability range under different categories of water control tools is calculated with all other target oil enhancement influencing factors having fixed factor level combinations. Under each category of water control tool, the maximum oil enhancement rate difference is taken as the maximum oil enhancement rate of permeability range under the current category of water control tool, and the minimum oil enhancement rate difference is taken as the minimum oil enhancement rate of permeability range under the current category of water control tool.

[0147] In step S100, the oil increase contribution rate of each target oil increase influencing factor is determined based on the oil increase rate of each target mechanism model under different water control tool parameters. This includes determining the oil increase contribution rate of each target oil increase influencing factor under different categories of water control tools using the following formula:

[0148]

[0149] Among them, C i S represents the contribution rate of the oil-increasing factor for the i-th objective, dimensionless;i Let S be the oil increase rate corresponding to the i-th target oil increase influencing factor, %; (S i ) max Let S be the maximum oil increase rate of the i-th target oil increase factor. i ) min Let be the minimum oil increase rate of the i-th target oil increase factor, and n be the total number of target oil increase factors.

[0150] For example, when the water control tool is an ICD, the maximum and minimum oil enhancement rates for the permeability range are respectively (S1). max and (S1) min The maximum and minimum oil enrichment rates for crude oil viscosity are (S2) respectively. max and (S2) min The maximum and minimum oil production rates for horizontal well lengths are (S3). max and (S3) min The maximum and minimum oil production increases per day are (S4). max and (S4) min The contribution rate of the penetration range is calculated as follows: C1 = ((S1)) max -(S1) min ) / (((S1) max -(S1) min )+ ( (S2) max -(S2) min )+ ( (S3) max -(S3) min )+ ( (S4) max -(S4) min )).

[0151] In step S300, the parameter values ​​of each target oil-increasing influencing factor for different oil wells are obtained. Based on the oil-increasing contribution rate of each target oil-increasing influencing factor, the weight of each target oil-increasing influencing factor is determined. The parameter values ​​of each target oil-increasing influencing factor are then weighted and summed to obtain the corresponding oil-increasing potential score for the oil well, including:

[0152] S310. Obtain the parameter values ​​of each target oil-increasing influencing factor for different oil wells, and determine the score of each target oil-increasing influencing factor for different oil wells through the preset target oil-increasing influencing factor evaluation table. The target oil-increasing influencing factor evaluation table shall at least include the scores corresponding to the parameter values ​​of different target oil-increasing influencing factors. For example, the scores of some main control factors are shown in Table 1, which are not limited here.

[0153] Table 1

[0154]

[0155] S320. Determine the weights of different categories of water control tools. Using these weights, sum the oil production contribution rates of each target oil production factor under different categories of water control tools to obtain the weight of each target oil production factor. Then, sum the scores of each target oil production factor using these weights to obtain the corresponding oil production potential score for the oil well. The weights of different categories of water control tools can be predetermined and are not limited here. In step S300, the target oil well can be determined according to a preset oil production potential evaluation table. For example, different potential scores can be divided into multiple levels, as shown in Table 2.

[0156] Table 2

[0157]

[0158] Among them, the potential square threshold can be set to 2.0, that is, when the potential evaluation of an oil well is determined to be high or above, the current oil well is determined to be the target oil well.

[0159] Understandably, in this application, the reservoir numerical simulation model can also be trained based on deep learning algorithms. For example, by using historical oil production rate data under different annular packer placement positions and different water control tool parameters for different oil wells, deep learning algorithms such as SVM, CNN convolutional neural networks, and LSTM neural networks can be trained to obtain a reservoir numerical simulation model with annular packer placement position and / or water control tool parameters such as water control tool position and opening degree as inputs and oil production rate as output. Understandably, the reservoir numerical simulation model can be a single model with annular packer placement position and water control tool parameters as inputs; the reservoir numerical simulation model can also consist of two sub-models, with the annular packer placement position and water control tool parameters as inputs, respectively. This is not limited here. In this application, firstly, automatic history fitting is performed based on the reservoir model adjustment rotation and automatic history fitting algorithm, namely the ensemble Kalman filter algorithm, to obtain a reservoir numerical simulation model with good fitting effect. Then, the optimal mechanical water control parameter scheme is selected using the first optimization algorithm and the second optimization algorithm. Finally, the reservoir numerical simulation model is called through the algorithm interface for calculation to obtain the oil enhancement effect of the optimal mechanical water control parameter scheme under different water control tools. In this application, the first optimization algorithm is a particle swarm optimization algorithm based on simulated annealing and elite back-learning, namely EO-SAPSO. The second optimization algorithm can adopt the existing multi-objective particle swarm elite back-learning algorithm, namely EO-MOPSO. It is understood that the second optimization algorithm can also adopt the existing particle swarm algorithm or be implemented using the first optimization algorithm; this is not limited here.

[0160] In step S400, the first optimization algorithm is a particle swarm optimization algorithm based on simulated annealing and elite back-learning. It optimizes the objective function using a pre-built reservoir numerical simulation model and the first optimization algorithm to determine the optimal annular packer placement position for the target oil well, including:

[0161] S1. Initialize the particle swarm parameters, which include the number of particles in the swarm and the initial position and initial velocity of each particle. Map the different annular packer placement positions of the target oil well to different dimensions of each particle; for example, if the number of annular packers is d, then the dimension of each particle is d.

[0162] S2. Determine the fitness value of each particle in the particle swarm through the reservoir numerical simulation model, and determine the individual extreme value and the population extreme value based on the objective function value and fitness value of each particle; for example, the oil increase rate output by the reservoir numerical simulation model is used as the fitness value of each particle.

[0163] S3. Determine the simulation temperature of the simulated annealing algorithm based on the population optimal solution; for example, T0=p gd / log(2), where T0 is the simulated temperature and log(2) is a positive number less than 1. A larger simulated temperature is beneficial for global search.

[0164] S4. Calculate the annealing fitness of each particle at the simulated temperature, and replace the population extremum based on the preset strategy according to the annealing fitness of each particle; wherein, the annealing fitness is calculated by the following formula:

[0165]

[0166] Among them, TF xi Let f(x) be the annealing fitness, e be the natural constant, and f(x) be the annealing fitness. i f(p) represents the fitness value of particle i. id ) represents the fitness value of particle i at the individual extreme value in dimension d, and n is the total number of particles.

[0167] S5. Update the velocity and position of each particle, determine the fitness value of each particle based on the updated objective function value, and update the individual extreme value and the population extreme value according to the fitness value of each particle.

[0168] In this application, the velocity of each particle is updated using the following formula:

[0169] v id (t+1)=wv id (t)+c1r1((0.5+r3 / 2)p id -x id (t))+c2r2((0.5+r4 / 2)p gd -x id (t))

[0170] The position of each particle is updated using the following formula:

[0171] x id (t+1)=x id (t)+v id (t+1)

[0172] Among them, v id (t+1) represents the velocity of the i-th particle in dimension d at iteration t+1, v id (t) represents the velocity of the i-th particle in dimension d at iteration t, w is the inertia weight, c1 and c2 are learning factors, r1, r2, r3, and r4 are random numbers varying in [0,1], and p id Let p be the individual extreme value of the i-th particle in dimension d. gd For the population extremum, x id (t) represents the position of the i-th particle in dimension d, x id (t+1) represents the velocity of the i-th particle in dimension d at iteration t+1.

[0173] Understandably, existing particle swarm optimization algorithms are prone to getting stuck in local optima. Particle populations tend to converge, and when a particle swarm optimization algorithm gets stuck in a local optimum, it becomes difficult to update the speed, making it difficult to find a better position. Therefore, in order to avoid particles getting stuck in local optima, this application introduces extreme value perturbation to expand the particle search range, thereby making it easier for particles to jump out of local optima.

[0174] The inertia weights are updated using the following formula:

[0175] w=w max -p(iter max -iter)

[0176] p=(w max -w min ) / sum(1:iter)

[0177] Among them, w max For the maximum inertia weight, w min For the minimum inertia weight, iter max The maximum number of iterations is given by 'iter', and the current iteration number is given by 'sum(1:iter)'. The sum of 'sum' from 1 to 'iter' is the cumulative sum.

[0178] Compared with existing technologies, the inertia weight in this application has a non-linear decreasing characteristic. The inertia weight decreases faster in the early stage and slower in the later stage, which enables the algorithm to have a stronger search ability in the early stage of iteration and a stronger convergence ability in the later stage of iteration.

[0179] S6. Generate a random real number in [0,1]. If the random real number is less than the preset threshold, i.e., the elite learning probability P0, proceed to step S7; otherwise, proceed to step S9.

[0180] S7. Sort the fitness values ​​of each particle from high to low, and perform reverse learning on the top n particles to obtain n particles after reverse learning. For example, perform reverse learning on the top 1 / 2 particles; the reverse solution of the elite individual can be expressed as:

[0181]

[0182] in, For particle x i，j The inverse solution, x i，j ∈[a j b j ], k∈[0,1] is a generalization coefficient, which can be used to generate multiple different reverse elite individuals, a j b j These are the minimum and maximum values ​​of the current search interval in the j-th dimension, respectively.

[0183] S8. Determine the fitness value of each inversely learned particle based on the objective function, compare the fitness value of each inversely learned particle with the fitness value of the current particles, and update the particles in the current particle swarm according to the comparison result; for example, compare each inversely learned particle with the fitness values ​​of the last n particles in descending order of fitness value. If the fitness value of the current inversely learned particle is better than the fitness value of any of the last n particles, replace the corresponding particle in the last n particles with the inversely learned particle. That is, replace the particles in the original particle swarm with the inversely learned particle whose fitness value is greater than the fitness value of the particles in the original particle swarm.

[0184] S9. Determine if the maximum number of iterations has been reached. If not, lower the simulation temperature, for example, let T... k+1 =CT k Where C is the annealing coefficient, return to step S4, if the maximum number of iterations is reached, determine the optimal annular packer placement position of the target oil well based on the population extremum in the current particle swarm.

[0185] The following specific example illustrates the effectiveness of the method in this application:

[0186] Taking a horizontal well in a bottom-water reservoir at sea in a certain oilfield as an example, the viscosity of the crude oil in this reservoir is 18.7 mPa·s, and the dissolved gas-oil ratio is low, at 1.74 m... 3 / m 3The reservoir pressure is 17.77 MPa, the water volume ratio is approximately 40, and the bottom water energy is relatively abundant. No water control measures were implemented in this well, nor were packers used to isolate the annulus. The well was put into production in May 2008, and by May 2018, the water cut had reached 98.1%, placing it in an ultra-high water-cut stage. The permeability difference along the well exceeds 10, the reservoir crude oil viscosity exceeds 8 mPa·s, and the daily production reaches 2000 m³ / s. 3 / d. According to the water control and oil production potential evaluation table, the well has a potential score of 3 points, which shows that the well has excellent water control and oil production potential.

[0187] Two water control schemes for the target well were optimized for different timings. Based on the mechanical water control parameter optimization scheme that integrates intelligent algorithm and reservoir numerical simulation proposed in this application, the optimal design of the water control scheme for the target well was achieved and the simulation results of the optimized water control scheme for the well were obtained.

[0188] The first approach optimizes the water control parameters during commissioning. The optimal ICD opening parameters are: ICD pore densities of 0, 1, 1, and 4 pores / 10 meters in each annulus section; the optimal AICD opening parameters are: AICD number densities of 0, 1, 1, and 4 AICDs / 10 meters in each annulus section; and the optimal parameters for C-AICD are: ICD pore densities of 0, 1, 1, and 0 pores / 10 meters, and AICD number densities of 0, 1, 1, and 0 AICDs / 10 meters. The cumulative oil production curves for the three water control tools are shown below. Figure 4 As shown, the cumulative oil production using water control tools is significantly higher than that without water control. Among them, C-AICD has the best water control effect, followed by AICD. The cumulative oil production under C-AICD water control conditions is 14% higher than that without water control, and the cumulative oil production can be increased by 52,100 cubic meters. The water control scheme has achieved a very good oil production increase effect.

[0189] The second scheme involves optimizing the water control scheme after reaching the ultra-high water cut stage, with a target well production time of 5 years under water control conditions. The optimized packer placement parameters are consistent with the optimization results of the first water control timing scheme, while the placement and opening parameters of the water control tools are slightly different. Specifically, the parameters of ICD and C-AICD remain unchanged, and the AICD quantity density is adjusted to 0, 1, 1, and 0 per 10 meters. The cumulative oil production curves for the optimal schemes of the three water control tools and the cumulative oil production curves without water control are shown below. Figure 5 As shown, after water control was implemented, the cumulative oil production curves of the three water control tools were significantly higher than those of the non-water control scheme. Similar to the initial water control during production, the C-AICD scheme had the highest cumulative oil production, 7.3% higher than the non-water control scheme, with an increase of 34,400 cubic meters in cumulative oil production.

[0190] Further comparison of the moisture content curves under three water control tools and without water control, such as Figure 6As shown in the figure, it can be seen that after water control, the water content curve decreased significantly, from 98% to 94%. This demonstrates that the water control scheme optimized under the second water control timing can also achieve a good oil-increasing effect.

[0191] In summary, the mechanical water control parameter optimization method proposed in this invention, which integrates intelligent algorithms and reservoir numerical simulation, has achieved excellent oil production enhancement effects in both the initial stage of production and the design of water control schemes during the ultra-high water cut period.

[0192] like Figure 7 As shown, in a second aspect, this application provides a device for optimizing water control parameters in a horizontal well of a water-drive reservoir, comprising:

[0193] The model building module is configured to build a reservoir model including multiple oil wells and acquire observation data, simulate production through the reservoir model to obtain production data, obtain automatic history fitting results through a preset automatic history fitting algorithm based on the observation data and production data, and build a reservoir numerical simulation model based on the automatic history fitting results.

[0194] The oil production contribution rate calculation module is configured to determine the target oil production impact factors of the oil well and the oil production contribution rate of each target oil production impact factor;

[0195] The oil well scoring module is configured to obtain the parameter values ​​of each target oil-increasing influencing factor for different oil wells, determine the weight of each target oil-increasing influencing factor based on the oil-increasing contribution rate of each target oil-increasing influencing factor, and perform a weighted summation of the parameter values ​​of each target oil-increasing influencing factor with the weight of each target oil-increasing influencing factor to obtain the oil-increasing potential score of the corresponding oil well, and determine the oil wells with potential scores higher than the potential score threshold as target oil wells;

[0196] The water control parameter optimization module is configured to construct an objective function with the goal of maximizing the oil production rate. The objective function is optimized using a pre-constructed reservoir numerical simulation model and a first optimization algorithm to determine the optimal annular packer placement position for the target oil well. The objective function is also optimized using a reservoir numerical simulation model and a second optimization algorithm to determine the optimal water control tool parameters for the target oil well.

[0197] It is understood that those skilled in the art will clearly recognize that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0198] In a third aspect, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to execute the water control parameter optimization method for a horizontal well in a water-drive reservoir as described above.

[0199] In a fourth aspect, this application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described method for optimizing water control parameters in a horizontal well of a water-drive reservoir.

[0200] like Figure 8 The diagram shown is a schematic representation of a terminal device provided in an embodiment of this application. Figure 8 As shown, the terminal device 10 of this embodiment includes a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, it implements the steps in the above method embodiments. Alternatively, when the processor 100 executes the computer program 102, it implements the functions of each module / unit in the above device embodiments.

[0201] For example, computer program 102 may be divided into one or more modules / units, one or more of which are stored in memory 101 and executed by processor 100 to complete this application. One or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 102 in terminal device 10.

[0202] Terminal device 10 may be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. Terminal device 10 may include, but is not limited to, a processor 100 and a memory 101. Those skilled in the art will understand that... Figure 8 This is merely an example of terminal device 10 and does not constitute a limitation on terminal device 10. It may include more or fewer components than shown, or combine certain components, or different components. For example, terminal device may also include input / output devices, network access devices, buses, etc.

[0203] The processor 100 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0204] The memory 101 can be an internal storage unit of the terminal device 10, such as a hard disk or RAM of the terminal device 10. The memory 101 can also be an external storage device of the terminal device 10, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or FlashCard equipped on the terminal device 10. Furthermore, the memory 101 can include both internal and external storage units of the terminal device 10. The memory 101 is used to store computer programs and other programs and data required by the terminal device 10. The memory 101 can also be used to temporarily store data that has been output or will be output.

[0205] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0206] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0207] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for optimizing water control parameters in horizontal wells of water-drive oil reservoirs, characterized in that, include: An oil reservoir model including multiple oil wells is established and observation data is obtained. Production is simulated through the oil reservoir model to obtain production data. Based on the observation data and production data, an automatic history fitting result is obtained through a preset automatic history fitting algorithm. An oil reservoir numerical simulation model is established based on the automatic history fitting result. Determine the target oil-increasing factors for oil wells and the oil-increasing contribution rate of each target oil-increasing factor; Obtain the parameter values ​​of each target oil-increasing influencing factor for different oil wells, determine the weight of each target oil-increasing influencing factor based on the oil-increasing contribution rate of each target oil-increasing influencing factor, and perform a weighted summation of the parameter values ​​of each target oil-increasing influencing factor with the weight of each target oil-increasing influencing factor to obtain the oil-increasing potential score of the corresponding oil well, and determine the oil wells with potential scores higher than the potential score threshold as target oil wells; An objective function is constructed with the goal of maximizing the oil production rate. The objective function is then optimized using the reservoir numerical simulation model and a first optimization algorithm to determine the optimal annular packer placement position for the target oil well. Additionally, the objective function is optimized using the reservoir numerical simulation model and a second optimization algorithm to determine the optimal water control tool parameters for the target oil well. Determine the target oil production enhancement factors for oil wells, including: Construct a single-well model of the oil well and determine multiple geological and engineering parameters of the single-well model; Under specified water control tool parameters, reservoir numerical simulation is performed on the single-well model based on different geological and engineering parameters to obtain the oil production rate of the single-well model under different geological and engineering parameters; The obtained oil enhancement rates are sorted in descending order, and the geological or engineering parameters corresponding to the top n oil enhancement rates are determined as the target oil enhancement influencing factors.

2. The method for optimizing water control parameters in horizontal wells of water-drive reservoirs according to claim 1, characterized in that, Before simulating production using the reservoir model, the method further includes: The region in the reservoir model where the horizontal well trajectory is non-linear is identified as the region to be adjusted, and the rotation angle of each grid cell in the region to be adjusted is determined. Based on the rotation angle of each grid cell in the area to be adjusted, a rotation operation is performed on each grid cell to convert the corresponding horizontal well trajectory in the area to be adjusted into a straight line; The neighboring grid cells of each grid cell in the region to be adjusted are determined, the attribute value of each neighboring grid cell is obtained, and the predicted attribute value of each grid cell in the region to be adjusted is determined by bilinear interpolation based on the attribute values ​​of the neighboring grid cells of each grid cell.

3. The method for optimizing water control parameters in horizontal wells of water-drive reservoirs according to claim 1, characterized in that, The automatic history fitting algorithm is an ensemble Kalman filter algorithm; Based on the observation data and production data, an automatic historical fitting result is obtained through a preset automatic historical fitting algorithm, including: Determine the set state vector of the set Kalman filter algorithm, wherein the set state vector includes the static parameters, dynamic parameters and production data of the reservoir model; Based on the aforementioned ensemble Kalman filtering algorithm, the error covariance matrix of the ensemble state vector and the error covariance matrix of the ensemble production data are obtained, wherein the ensemble production data is a set of production data obtained by continuously updating the model set during the inversion process; The Kalman gain of the Kalman filter algorithm for the set is determined based on the error covariance matrix of the set state vector, the error covariance matrix of the set production data, and the observation data. Based on the Kalman gain and the static and dynamic parameters of the reservoir model, the static and dynamic parameters of the reservoir model are updated to obtain automatic historical fitting results.

4. The method for optimizing water control parameters in horizontal wells of water-drive reservoirs according to claim 1, characterized in that, Determine the contribution rate of each target oil-increasing factor to oil production, including: Based on at least one target oil-increasing influencing factor, at least one target mechanism model is constructed using orthogonal design method; Reservoir numerical simulations were performed for each target mechanism model under different water control tool parameters to obtain the oil enhancement rate of each target mechanism model under different water control tool parameters. Based on the oil enhancement rate of each target mechanism model under different water control tool parameters, the oil enhancement contribution rate of each target oil enhancement influencing factor was determined.

5. The method for optimizing water control parameters in horizontal wells of water-drive reservoirs according to claim 4, characterized in that, The geological parameters include: Permeability, crude oil viscosity, water-sheltered height, permeability gradient, rhythmicity, and location of interlayers; The engineering parameters include: Horizontal well length, well trajectory morphology, water cut, and production rate; The water control tool includes: Passive water control tool ICD, autonomous water control tool AICD, and composite autonomous water control tool C-AICD; The parameters of the water control tool include: The type of water control tool, its location, and its opening degree.

6. The method for optimizing water control parameters in horizontal wells of water-drive reservoirs according to claim 5, characterized in that, Oil reservoir numerical simulations were performed on the single-well model based on different geological and engineering parameters to obtain the oil enhancement rate of the single-well model under different geological and engineering parameters, including: For each geological parameter and engineering parameter, other geological parameters and engineering parameters besides the current geological parameter or engineering parameter are fixed, and reservoir numerical simulation is performed on the single well model under different values ​​of the current geological parameter or engineering parameter to obtain the different oil enhancement rates of each type of water control tool under different values ​​of the current geological parameter or engineering parameter. Determine the maximum and minimum oil enhancement rates for each category of water control tools, and then determine the oil enhancement rate for the current geological or engineering parameters based on the maximum and minimum oil enhancement rates for each category of water control tools.

7. The method for optimizing water control parameters in horizontal wells of water-drive reservoirs according to claim 6, characterized in that, The oil enhancement rate of the current geological or engineering parameters is determined based on the maximum and minimum oil enhancement rates of each type of water control tool, including: The oil enhancement rate of the current geological or engineering parameters is determined using the following formula: Where n represents the total number of categories of water control tools, r i This represents the oil enhancement rate of a certain water control tool; r avg This represents the average value of the difference in oil yield, %; (r i ) max Represents the maximum oil increase rate of the i-th type of water control tool, (r i ) min This represents the minimum oil enhancement rate of the i-th type of water control tool; This represents the average value of the difference in oil production rate between water control tools, expressed as... Oil enhancement rate, as a current geological or engineering parameter.

8. The method for optimizing water control parameters in horizontal wells of water-drive reservoirs according to claim 5, characterized in that, Based on at least one target factor influencing oil production, at least one target mechanism model is constructed using orthogonal design, including: The different factor levels of each target oil-increasing factor are determined based on the different parameter value ranges of each target oil-increasing factor. For each target oil increase factor, the different factor levels of the current target oil increase factor are combined with the different factor levels of other target oil increase factors through orthogonal design to obtain at least one factor level combination corresponding to the current target oil increase factor. For each target oil-increasing influencing factor, the factor level combination corresponding to it is deduplicated so that the factor level combination corresponding to each target oil-increasing influencing factor is different. Based on each deduplicated factor level combination, at least one target mechanism model corresponding to each target oil-increasing influencing factor is established.

9. The method for optimizing water control parameters in horizontal wells of water-drive reservoirs according to claim 8, characterized in that, Reservoir numerical simulations were performed for each target mechanism model under different water control tool parameters to obtain the oil enhancement rate of each target mechanism model under different water control tool parameters, including: Without water control tools, reservoir numerical simulations were performed on each target mechanism model to obtain the cumulative oil production of each target mechanism model under the condition of water control tools; and In the presence of water control tools, for each target mechanism model, reservoir numerical simulation is performed under each type of water control tool to obtain the cumulative oil production of each target mechanism model under each type of water control tool. Based on the cumulative oil production of each target mechanism model under each type of water control tool and the cumulative oil production of each target mechanism model without water control tool, the oil enhancement rate of each target mechanism model under each type of water control tool is determined. Based on the oil enhancement rate of each target mechanism model under each type of water control tool, the maximum and minimum oil enhancement rates corresponding to each target oil enhancement influencing factor under each type of water control tool are determined.

10. The method for optimizing water control parameters in horizontal wells of water-drive reservoirs according to claim 9, characterized in that, Determine the maximum and minimum oil increase rates for each target oil increase factor under each category of water control tools, including: Under each category of water control tools: Fix the factor levels of other target oil-increasing factors besides the current target oil-increasing factor to obtain at least one fixed factor level combination of other target oil-increasing factors besides the current target oil-increasing factor; For each fixed factor level combination, calculate the difference in oil enhancement rate of the current target oil enhancement factor under different factor levels; The maximum value among all obtained fuel efficiency differences is determined as the maximum fuel efficiency of the current target fuel efficiency influencing factor, and the minimum value among all obtained fuel efficiency differences is determined as the minimum fuel efficiency of the current target fuel efficiency influencing factor.

11. The method for optimizing water control parameters in horizontal wells of water-drive reservoirs according to claim 9, characterized in that, Based on the oil enhancement rate of each target mechanism model under different water control tool parameters, the oil enhancement contribution rate of each target oil enhancement factor is determined, including: The contribution rate of each target oil-increasing factor to oil production under different categories of water control tools is determined using the following formula: Among them, C i S represents the contribution rate of the oil-increasing factor for the i-th objective, dimensionless; i Let S be the oil increase rate corresponding to the i-th target oil increase influencing factor, %; (S i ) max Let S be the maximum oil increase rate of the i-th target oil increase factor. i ) min Let be the minimum oil increase rate of the i-th target oil increase factor, and n be the total number of target oil increase factors.

12. The method for optimizing water control parameters in horizontal wells of water-drive reservoirs according to claim 9, characterized in that, Obtain the parameter values ​​of each target oil-increasing influencing factor for different oil wells. Determine the weight of each target oil-increasing influencing factor based on its oil-increasing contribution rate. Then, sum the parameter values ​​of each target oil-increasing influencing factor using its weight to obtain the corresponding oil-increasing potential score for the oil well, including: Obtain the parameter values ​​of each target oil-increasing influencing factor for different oil wells, and determine the score of each target oil-increasing influencing factor for different oil wells through a preset target oil-increasing influencing factor evaluation table. The target oil-increasing influencing factor evaluation table includes at least the scores corresponding to the parameter values ​​of different target oil-increasing influencing factors. The weights of different types of water control tools are determined. The oil production contribution rate of each target oil production factor under different types of water control tools is weighted and summed to obtain the weight of each target oil production factor. The scores of each target oil production factor are weighted and summed to obtain the oil production potential score of the corresponding oil well.

13. The method for optimizing water control parameters in horizontal wells of water-drive reservoirs according to claim 1, characterized in that, The first optimization algorithm is a particle swarm optimization algorithm based on simulated annealing and elite back learning. It optimizes the objective function using a pre-constructed reservoir numerical simulation model and the first optimization algorithm to determine the optimal annular packer placement position for the target oil well, including: S1. Initialize particle swarm parameters, which include the number of particles in the particle swarm and the initial position and initial velocity of each particle, and map the different annular packer placement positions of the target oil well to different dimensions of each particle. S2. Determine the fitness value of each particle in the particle swarm through the reservoir numerical simulation model, and determine the individual extreme value and the population extreme value based on the objective function value and fitness value of each particle. S3. Determine the simulation temperature of the simulated annealing algorithm based on the population optimal solution; S4. Calculate the annealing fitness of each particle at the simulated temperature, and replace the population extremum based on the preset strategy according to the annealing fitness of each particle. S5. Update the velocity and position of each particle, determine the fitness value of each particle based on the updated objective function value, and update the individual extreme value and the population extreme value according to the fitness value of each particle. S6. Generate a random real number in the range [0,1]. If the random real number is less than a preset threshold, proceed to step S7; otherwise, proceed to step S9. S7. Sort the fitness values ​​of each particle from high to low, and perform reverse learning on the top n particles to obtain n particles after reverse learning. S8. Determine the fitness value of each inversely learned particle based on the objective function, compare the fitness value of each inversely learned particle with the current fitness value of each particle, and update the particles in the current particle swarm according to the comparison result. S9. Determine whether the maximum number of iterations has been reached. If the maximum number of iterations has not been reached, reduce the simulation temperature and return to step S4. If the maximum number of iterations has been reached, determine the optimal annular packer placement position for the target oil well based on the population extremum in the current particle swarm.

14. The method for optimizing water control parameters in horizontal wells of water-drive reservoirs according to claim 13, characterized in that, The fitness values ​​of each particle after reverse learning are compared with the current fitness values ​​of all particles. Based on the comparison results, the particles in the current particle swarm are updated, including: Each inversely learned particle is compared with the fitness values ​​of the last n particles in descending order of fitness value. If the fitness value of the current inversely learned particle is better than the fitness value of any of the last n particles, the inversely learned particle replaces the corresponding particle in the last n particles.

15. The method for optimizing water control parameters in horizontal wells of water-drive reservoirs according to claim 13, characterized in that, Update the velocity and position of each particle, including: The velocity of each particle is updated using the following formula: v id (t+1)=wv id (t)+c1r1((0.5+r3 / 2)p id -x id (t))+c2r2((0.5+r4 / 2)p gd -x id (t)) The position of each particle is updated using the following formula: x id (t+1)=x id (t)+v id (t+1) Among them, v id (t+1) represents the velocity of the i-th particle in dimension d at iteration t+1, v id (t) represents the velocity of the i-th particle in dimension d at iteration t, w is the inertia weight, c1 and c2 are learning factors, r1, r2, r3, and r4 are random numbers varying in [0,1], and p id Let p be the individual extreme value of the i-th particle in dimension d. gd For the population extremum, x id (t) represents the position of the i-th particle in dimension d, x id (t+1) represents the velocity of the i-th particle in dimension d at iteration t+1.

16. The method for optimizing water control parameters in a horizontal well of a water-drive reservoir according to claim 15, characterized in that, The method further includes: The inertia weights are updated using the following formula: w=w max -p(iter max -iter) p=(w max -w min ) / sum(1:iter) Among them, w max For the maximum inertia weight, w min For the minimum inertia weight, iter max The maximum number of iterations is given by 'iter', and the current iteration number is given by 'sum(1:iter)'. The sum of 'sum' from 1 to 'iter' is the cumulative sum.

17. A device for optimizing water control parameters in a horizontal well of a water-drive reservoir, employing the method for optimizing water control parameters in a horizontal well of a water-drive reservoir as described in any one of claims 1-16, characterized in that, include: The model building module is configured to build a reservoir model including multiple oil wells and acquire observation data, simulate production through the reservoir model to obtain production data, obtain automatic history fitting results through a preset automatic history fitting algorithm based on the observation data and production data, and build a reservoir numerical simulation model based on the automatic history fitting results. The oil production contribution rate calculation module is configured to determine the target oil production impact factors of the oil well and the oil production contribution rate of each target oil production impact factor; The oil well scoring module is configured to obtain the parameter values ​​of each target oil-increasing influencing factor for different oil wells, determine the weight of each target oil-increasing influencing factor based on the oil-increasing contribution rate of each target oil-increasing influencing factor, and perform a weighted summation of the parameter values ​​of each target oil-increasing influencing factor with the weight of each target oil-increasing influencing factor to obtain the oil-increasing potential score of the corresponding oil well, and determine the oil wells with potential scores higher than the potential score threshold as target oil wells; The water control parameter optimization module is configured to construct an objective function with the goal of maximizing the oil production rate. The objective function is optimized using a pre-constructed reservoir numerical simulation model and a first optimization algorithm to determine the optimal annular packer placement position for the target oil well. The objective function is also optimized using the reservoir numerical simulation model and a second optimization algorithm to determine the optimal water control tool parameters for the target oil well.

18. A computer-readable storage medium, characterized in that, The computer program stores a method for optimizing water control parameters in a horizontal well of a water-drive reservoir as described in any one of claims 1-16, which, when executed by a processor, causes the processor to perform such method.

19. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes a computer program, it implements the method for optimizing water control parameters in a horizontal well of a water-drive reservoir as described in any one of claims 1-16.

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