Three-dimensional well pattern interference evaluation method based on dynamic oil drainage volume

Abstract

The invention belongs to the technical field of shale oil horizontal well three-dimensional development, and particularly discloses a three-dimensional well pattern interference evaluation method based on dynamic oil drainage volume. The method is used for solving the problem of how to establish an accurate inter-well interference evaluation method under the conditions of high reservoir heterogeneity and complex inter-well mutual influence. Comprising the following steps: (1) constructing a multi-field coupling dynamic oil drainage volume characterization model based on reservoir pressure field evolution, fluid seepage characteristics and fracture network response, and realizing quantitative and dynamic evaluation of shale oil reservoir inter-well interference; and (2) establishing an inter-well interference degree quantitative evaluation system based on the multi-field coupling dynamic oil drainage volume characterization model, and measuring the space overlapping degree and the pressure field difference of the oil drainage volume of each well by using the inter-well interference degree quantitative evaluation system so as to provide a quantitative basis for well pattern layout and interference control. According to the method, the dynamic seepage characteristics of the shale oil reservoir and the inter-well competition relationship are tightly coupled, and the interference identification precision is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of three-dimensional development of horizontal wells in shale oil, and particularly relates to a method for evaluating interference of a three-dimensional well pattern based on dynamic oil drainage volume. Background Technique

[0002] With the increasing urgency of increasing reserves and production of unconventional reservoirs under deep and complex formation conditions, the relationship among formation pressure, fracture network, and well interference degree becomes more and more complex. If the characteristics of well interference cannot be accurately evaluated and the dynamic seepage law cannot be grasped, it is easy to lead to insufficient reservoir utilization or too high density of ineffective wells, resulting in problems such as aggravated production decline and poor economic benefits.

[0003] At present, the optimization of well pattern layout usually relies on numerical simulation methods. By establishing a geological model and simulating and predicting the pressure field and seepage field, although it has certain effects in conventional oilfields, there are still deficiencies in dealing with the coupled analysis of microseismic fracture monitoring data, heterogeneous reservoirs, and real-time production data in shale oil reservoirs. Some methods for manually evaluating the degree of well interference (such as pressure field reconstruction or fluid saturation tracking, etc.) may be time-consuming and laborious, and it is difficult to meet the requirements for rapid and large-scale well pattern combination optimization. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for evaluating interference of a three-dimensional well pattern based on dynamic oil drainage volume, effectively solving the key technical problems in the development of a three-dimensional well pattern in shale oil reservoirs: how to establish an accurate method for evaluating well interference under the conditions of strong reservoir heterogeneity and complex well-to-well interaction.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A method for evaluating interference of a three-dimensional well pattern based on dynamic oil drainage volume, comprising the following steps: S1. Construct a multi-field coupled dynamic oil drainage volume characterization model based on the evolution of the reservoir pressure field, fluid seepage characteristics, and fracture network response, so as to realize the quantitative and dynamic evaluation of well interference in shale oil reservoirs.

[0007] S2. Based on the multi-field coupled dynamic oil drainage volume characterization model, establish a quantitative evaluation system for well interference degree, and use the quantitative evaluation system for well interference degree to measure the spatial overlap degree of the oil drainage volume of each well and the pressure field difference, providing a quantitative basis for well pattern layout and interference control.

[0008] Further, in step S1, the multi-field coupled dynamic oil drainage volume characterization model integrates the following dynamic characteristics: the dynamic evolution characteristics of pressure propagation: revealing the pressure diffusion law of the reservoir under development dynamics; the seepage law of oil-water two-phase fluid: quantifying the spatial distribution of fluid saturation change; the dynamic response of the fracture network: the dynamic evolution characteristics of fracture morphology under the coupling action of the stress field and seepage field.

[0009] Further, in step S1, based on the multiphase seepage theory, a dynamic oil drainage volume calculation equation is established:

[0010] V leak ′(t) = φ(p)·[S o,l,m,n (t + 1) - S o,l,m,n (t)]·V eff (p)·△p eff (t);

[0011] Wherein, V leak ′(t) represents the dynamic oil drainage volume at time t, φ(p) represents the pressure-sensitive function, p represents the formation pressure, S o,l,m,n is the oil-phase saturation of the corresponding grid block under the (l, m, n) spatial coordinates, V eff (p) represents the effective stimulation volume varying with pressure, △p eff (t) is the production pressure difference at time t.

[0012] Further, the numerical simulation process of the multi-field coupling dynamic oil drainage volume characterization model is as follows:

[0013] First, collect rock physical parameters, including but not limited to porosity, permeability, and elastic modulus, and build a three-dimensional geomechanics model in combination with the stress field distribution.

[0014] Secondly, correct the fracture network using microseismic monitoring data and fracturing pumping data to generate a fracture geometry close to the actual field situation.

[0015] Then, obtain the dynamic distributions of pressure and saturation and the fracture stimulation volume of different grids through numerical simulation.

[0016] Finally, substitute the porosity, permeability, formation pressure, dynamic distributions of saturation, and fracture stimulation volume of different grids into the dynamic oil drainage volume calculation equation to calculate the spatio-temporal distribution characteristics and evolution laws of the dynamic oil drainage volume at each time point.

[0017] Further, in step S2, the well interference degree quantitative evaluation system includes the oil drainage volume overlap degree, the pressure field difference coefficient, and the time weighting function.

[0018] The calculation formula for the oil drainage volume overlap degree is:

[0019] V overlap (t) = V A (t) ∩ V B (t);

[0020]

[0021] Wherein, V overlapRepresents the overlapping area of the dynamic oil drainage volume, V A (t) and V B (t) represent the dynamic oil drainage volumes of Well A and Well B at time t respectively, and R overlap represents the proportion of the overlapping degree of the oil drainage volume.

[0022] The calculation formula for the pressure field difference coefficient is:

[0023]

[0024] Among them, D pressure represents the pressure field difference coefficient, N is the total number of pressure field grid cells, and p A (k,t) and p B (k,t) are the pressure values of the k-th grid cell of Well A and Well B at time t respectively.

[0025] The calculation formula for the time-weighting function is:

[0026] w(t) = e -αt ;

[0027] Among them, w represents the time-weighting function, and α is the attenuation coefficient.

[0028] The comprehensive evaluation index I total for the interference degree between wells is calculated as:

[0029]

[0030] Among them, T is the total time step, and both β and γ are weighting coefficients.

[0031] Furthermore, when 0 ≤ I total < 0.3, the interference level between wells is weak interference; when 0.3 ≤ I total < 0.6, the interference level between wells is medium interference; when 0.6 ≤ I total ≤ 1, the interference level between wells is strong interference.

[0032] Compared with the prior art, the beneficial technical effects of the present invention are:

[0033] (1) The present invention integrates the dynamic oil drainage volume theory and the quantification of the interference degree between wells: Traditional well pattern designs are difficult to comprehensively measure the degree of reservoir utilization and the pressure interference between wells. The present invention tightly couples the dynamic seepage characteristics of shale oil reservoirs with the competitive relationship between wells through a multi-field coupled dynamic oil drainage volume characterization model and an interference degree comprehensive evaluation system, greatly improving the accuracy of interference identification and avoiding the economic losses caused by the layout of ineffective well density.

[0034] (2) The present invention introduces a CNN-LSTM deep learning prediction model to improve prediction efficiency and accuracy: converting three-dimensional geological attributes, microseismic data, and historical production data into trainable samples, integrating the spatial feature extraction of the CNN module and the temporal feature learning of the LSTM module, significantly shortening the evaluation time for multi-well layout; having better consistency and generalization ability compared to pure numerical simulation.

[0035] (3) The multi-objective optimization framework of the present invention is perfect, taking into account recovery rate, NPV, and well interference degree: the present invention uses the NSGA-III multi-objective optimization algorithm to perform collaborative optimization on the three major objectives, not only paying attention to the final oil reservoir production (recovery rate) and economic benefits (net present value), but also effectively controlling the well interference degree, ensuring the balance between the engineering feasibility and economic benefits of the development plan. Brief Description of the Drawings

[0036] Figure 1 It is a comparison diagram of the hydraulic fracture morphology simulation and microseismic detection results in Example 1.

[0037] Figure 2 It is a verification result diagram of the multi-field coupling dynamic oil drainage volume characterization model in terms of production dynamic data in Example 1.

[0038] Figure 3 It is a schematic diagram of the simulation results of the dynamic oil drainage volume of the block in the initial production stage and currently in Example 1.

[0039] Figure 4 It is a comparison effect diagram of the predicted values and true values of the CNN-LSTM deep learning prediction model on different targets in Example 2.

[0040] Figure 5 It is a schematic diagram of the Pareto front of the optimization results in Example 2.

[0041] Figure 6 It is a comparison effect diagram of the recovery rates of the basic plan and the optimized plan in Example 2.

[0042] Figure 7 It is a comparison effect diagram of the net present values of the basic plan and the optimized plan in Example 2.

[0043] Figure 8 It is a comparison effect diagram of the well interference degrees of the basic plan and the optimized plan in Example 2. Detailed Embodiments

[0044] Example 1: Select a certain block of an oilfield as the research object of this example. This block is a typical shale oil reservoir with the characteristics of "low porosity, low permeability, and rich organic matter": the average porosity is about 6.9%, and the matrix permeability is 0.433×10 -3 μm2 , the original formation pressure is 58.2 MPa, the reservoir burial depth is 3500 - 3750 m, and natural fractures are relatively developed in the area. The block adopts a three-dimensional development mode and realizes efficient production through multi-layer systematic well placement; the actual well placement includes 5 horizontal production wells, the wellbore trajectory is deployed along the dominant stress direction, the horizontal section length is 2000 - 3000 m, and the production wells adopt a constant-pressure depletion production method.

[0045] A three-dimensional well pattern interference evaluation method based on dynamic drainage volume includes the following steps: S1. Construct a multi-field coupling dynamic drainage volume characterization model based on the evolution of the reservoir pressure field, fluid seepage characteristics, and fracture network response to realize quantitative and dynamic evaluation of well interference in shale oil reservoirs.

[0046] (1) Definition and physical meaning of dynamic drainage volume (DDV)

[0047] During the development of shale oil reservoirs, affected by geological conditions and development dynamics, the formation pressure and fluid distribution inside the reservoir show complex evolution. DDV aims to quantify the effective drainage area at different production stages of the reservoir. DDV is accurately characterized by a multi-field coupling dynamic drainage volume characterization model of the reservoir pressure field, seepage characteristics, and fracture network response, and dynamically reflects the spatio-temporal variation characteristics of the effective drainage area in the reservoir. The multi-field coupling dynamic drainage volume characterization model integrates the following core dynamic characteristics: ① Dynamic evolution characteristics of pressure propagation: revealing the pressure diffusion law of the reservoir under development dynamics; ② Seepage law of oil-water two-phase fluid: quantifying the spatial distribution of fluid saturation changes; ③ Dynamic response of the fracture network: dynamically capturing the fracture geometry and its interaction with the pressure field and seepage field.

[0048] By constructing a multi-field coupling dynamic drainage volume characterization model, the spatial distribution characteristics of reservoir development benefits can be intuitively and quantitatively presented, providing a scientific basis for optimizing well parameters and development strategies.

[0049] (2) Achieving high-precision calculation of dynamic drainage volume through a multi-field coupling model: ① Three-dimensional geological model coupling: Construct a three-dimensional reservoir model based on actual geological data, including geological parameters such as porosity, permeability, and natural fracture distribution; ② Geological mechanics and seepage coupling simulation: Use numerical simulation software to simulate the stress field and fracture network after hydraulic fracturing transformation of the reservoir, and calculate the pressure field and seepage field distributions at different time steps.

[0050] Based on the multi-phase seepage theory, establish a dynamic drainage volume calculation equation:

[0051] V leak ′(t) = φ(p)·[S o,l,m,n (t + 1) - S o,l,m,n (t)]·V eff (p)·△peff (t);

[0052] Among them, V leak ′(t) represents the dynamic drainage volume at time t, φ(p) represents the pressure-sensitive function, p represents the formation pressure, and S o,l,m,n is the oil-phase saturation of the corresponding grid block under the (l, m, n) spatial coordinates, and V eff (p) represents the effective stimulation volume varying with pressure, and △p eff (t) is the production pressure difference at time t.

[0053] The calculation equation of the actual dynamic drainage volume can be corrected according to specific numerical simulation scenarios in combination with factors such as porosity, oil saturation, and fracture extension scale.

[0054] The numerical simulation process of the multi-field coupling dynamic drainage volume characterization model is as follows: First, collect the petrophysical parameters of the block (porosity, permeability, elastic modulus, Poisson's ratio, fracture distribution, etc.), and combine with the stress field distribution to build a three-dimensional geomechanics model; Second, use microseismic monitoring data and fracturing pumping data to correct the fracture network and generate a fracture geometry closer to the actual field; Then, use numerical simulation to obtain the dynamic distributions of pressure and saturation and the fracture stimulation volume of different grids; Finally, substitute the porosity, permeability, formation pressure, saturation dynamic distribution, and fracture stimulation volume of different grids into the dynamic drainage volume calculation equation to calculate the spatio-temporal distribution characteristics and evolution laws of the dynamic drainage volume at each time point.

[0055] The verification of the multi-field coupling dynamic drainage volume characterization model includes the following steps: Use microseismic monitoring data to verify the initial fracture geometry and stimulation volume; Achieve accurate characterization of the well interference characteristics based on the history matching of production dynamic data. The verification results are as Figure 1 and Figure 2 shown. The results show that the dynamic drainage volume characterization model has a high description ability for the dynamic response characteristics of shale oil reservoir, and its fitting accuracy reaches more than 85%, providing a reliable basis for well pattern optimization.

[0056] The specific steps are as follows: First, input the rock mechanics parameters (elastic modulus, Poisson's ratio), porosity, permeability, fracture distribution, etc. of the block into the geomechanics and seepage coupling model to obtain the pressure field distribution and fracture geometry at each time step. Second, use microseismic monitoring data and fracturing pumping data to correct the fracture network and generate a fracture geometry closer to the actual field. Finally, after completing the coupling iteration calculation at each time step, according to the dynamic drainage volume calculation equation, output the effective production volume corresponding to each well in the block, as Figure 3As shown in the figure. By comparing the microseismic monitoring results with the production history data, it is confirmed that DDV has a high simulation accuracy (error controlled within 10% - 15%) in the early stage of fracturing and the initial stage after production.

[0057] S2. Based on the multi-field coupling dynamic drainage volume characterization model, establish a quantitative evaluation system for well interference degree. Use the quantitative evaluation system for well interference degree to measure the spatial overlap degree of the drainage volume of each well and the pressure field difference, providing a quantitative basis for well pattern layout and interference control.

[0058] In the three-dimensional development process of shale reservoirs, a reasonable evaluation of the well interference degree is an important prerequisite for optimizing the well pattern layout. For this reason, this embodiment proposes a quantitative evaluation system for well interference degree based on the multi-field coupling dynamic drainage volume characterization model to scientifically measure the interference degree among multiple wells.

[0059] (1) The spatial overlap degree of the drainage volume is an important indicator to measure the well interference intensity. The embodiment uses the drainage volume overlap ratio to quantify the dynamic interference between wells.

[0060] The calculation formula for the drainage volume overlap ratio is:

[0061] V overlap (t) = V A (t) ∩ V B (t);

[0062]

[0063] Among them, V overlap represents the overlapping area of the dynamic drainage volume, and V A (t) and V B (t) respectively represent the dynamic drainage volumes of Well A and Well B at time t, and R overlap represents the drainage volume overlap ratio. The numerical range of the drainage volume overlap ratio is from 0 to 1, and the larger the value, the more significant the well interference.

[0064] (2) To further reflect the degree of dynamic interference between wells, this embodiment introduces a pressure field difference coefficient. The larger the pressure field difference coefficient, the more significant the difference in the pressure field between wells, indicating a greater intensity of well interference. The calculation formula for the pressure field difference coefficient is:

[0065]

[0066] Among them, D pressure represents the pressure field difference coefficient, N is the total number of pressure field grid cells, and p A (k, t) and p B (k, t) are the pressure values of the k-th grid cell of Well A and Well B at time t respectively.

[0067] (3) Time-weighted overlap degree calculation: To comprehensively reflect the inter-well interference characteristics in different production stages, this embodiment designs a time-weighted function w(t) to dynamically adjust the weight of interference evaluation. Its calculation formula is:

[0068] w(t) = e -αt ;

[0069] where w represents the time-weighted function and α is the attenuation coefficient.

[0070] Combining the oil drainage volume overlap degree and the pressure field difference coefficient, a comprehensive evaluation index I of the inter-well interference degree is defined total , and its calculation formula is:

[0071]

[0072] where T is the total time step; β and γ are weight coefficients used to balance the contributions of the oil drainage volume overlap degree and the pressure field difference coefficient to the interference degree.

[0073] (4) Inter-well interference degree evaluation index system: Based on the above calculations, this embodiment establishes an inter-well interference degree evaluation system including the oil drainage volume overlap degree, the pressure field difference coefficient, and the time-weighted function. By comprehensively considering these three indicators, the inter-well interference degree can be divided into three levels, as shown in Table 1.

[0074] Table 1 Classification criteria for the inter-well interference degree

[0075]

[0076] In this embodiment, 5 horizontal wells in this block are selected as the target well group. The DDV distributions of each well at the same moment are superimposed and the pressure field difference coefficient is calculated respectively to obtain the comprehensive evaluation index I of the inter-well interference degree total . In the first 6 months of the simulation period, due to the influence of fracture interference and fracturing transformation, the I value of some well groups total reaches 0.55 - 0.60, showing a medium to strong interference level; with the adjustment of the production system and the pressure decay, after 1 year of production, the interference degree gradually decreases to about 0.35, indicating that the interference will be alleviated in time sequence, which is in line with the on-site observation phenomenon.

[0077] Example 2: On the basis of Example 1, this embodiment further provides a multi-objective optimization method for the well positions of a shale oil three-dimensional well pattern.

[0078] It includes the following steps: S3. After establishing the quantitative evaluation system of the inter-well interference degree described in Example 1 based on the multi-field coupling dynamic oil drainage volume characterization model in Example 1, combined with the CNN-LSTM deep learning prediction model, multi-source data fusion and high-precision prediction of the dynamic oil drainage volume, recovery rate, net present value (NPV), and inter-well interference degree are realized.

[0079] The CNN-LSTM deep learning prediction model is based on the convolutional neural network (CNN) and the long short-term memory network (LSTM). By integrating multi-dimensional geological parameters and production dynamic data, it can quickly improve the collaborative prediction ability of dynamic oil drainage volume, recovery rate, net present value, and well interference degree through efficient multi-dimensional feature extraction and sequence learning, effectively solving the problem of low calculation efficiency in optimizing the well pattern layout under complex geological conditions.

[0080] (1) Architecture design of the CNN-LSTM deep learning prediction model.

[0081] I. CNN module: Spatial feature extraction.

[0082] Use the convolutional neural network to process geological parameters (such as porosity, permeability, fracture distribution, etc.). Through multi-layer convolution and pooling operations, extract the spatial distribution characteristics of the parameter field.

[0083] By converting geological parameter fields (such as permeability field, oil saturation field, etc.) into RGB three-channel images, capture the dynamic changes of reservoir heterogeneity and fracture networks, and improve the accuracy of reservoir modeling.

[0084] II. LSTM module: Temporal feature learning.

[0085] For time series data such as historical production and pressure changes, use LSTM to capture the time-dependent relationships of production dynamics. Through a double-layer LSTM structure, predict the evolution trends of dynamic oil drainage volume and well interference degree over time.

[0086] III. Multi-source data fusion: Deeply fuse the spatial features extracted by the CNN module, the temporal features captured by the LSTM module, and tabular data (such as well positions, horizontal section lengths, etc.). Make full use of multi-source information to achieve collaborative prediction, and finally predict the dynamic oil drainage volume, recovery rate, net present value, and well interference degree. The design of multi-source data fusion enables the model to adapt to different geological conditions and engineering parameters and generate targeted prediction results.

[0087] (2) Model training: A total of 400 sets of reservoir numerical simulation schemes are designed and generated in this embodiment. Among them, 320 sets are used to train the basic framework of the CNN-LSTM deep learning prediction model, 40 sets are used to determine the hyperparameters of the hybrid neural network, and the remaining 40 sets are used to test the generalization ability of the CNN-LSTM deep learning prediction model. The production time period of each simulation scheme is 3 years, and the dynamic adjustment period of the single-well working system is set to 2 months, with a total of 18 time steps to fully reflect the dynamic response characteristics of the reservoir.

[0088] The training data includes the following main inputs: ① Geological and fracture information: permeability, porosity, fracture network distribution; ② Production performance data: production rate, water cut, bottom-hole flowing pressure; ③ DDV time series: the dynamic oil infiltration volume output at each time step.

[0089] Among them, the CNN module is responsible for extracting spatial features (such as formations, fracture grids, etc.), the LSTM module is responsible for capturing time series dependencies, and finally outputs the comprehensive predicted values of the target parameters (dynamic oil drainage volume, net present value, recovery factor, well interference degree).

[0090] The training set is established using the Python programming language by calling the INTERSECT numerical simulator of Schlumberger. In the specific implementation process, first use the os.system() function to execute the preset cmd instruction sequence to achieve batch processing of the automatically generated.afi files. On this basis, sequential Gaussian simulation (SGSIM) is used to generate random geological samples to ensure data coverage and rationality.

[0091] (3) Model verification: To evaluate the accuracy of different deep learning models in predicting the dynamic oil infiltration volume of oil reservoirs, this embodiment selects three indicators, the coefficient of determination (R 2 ), the mean absolute error (MAE), and the root mean square error (RMSE), for performance comparison tests of the CNN model, the LSTM model, and the CNN-LSTM deep learning prediction model. The results are shown in Table 2.

[0092] Table 2 Performance evaluation of different oil reservoir prediction models based on deep learning

[0093] <![CDATA[R 2 > MAE RMSE CNN model 0.881 0.0391 0.0455 LSTM model 0.853 0.0458 0.0491 CNN-LSTM deep learning prediction model 0.913 0.0248 0.0297

[0094] The test results show that the CNN-LSTM deep learning prediction model is significantly superior to the individual CNN model or LSTM model in terms of R 2 , MAE, and RMSE, indicating that the CNN-LSTM deep learning prediction model of this embodiment can more accurately capture the spatio-temporal evolution law of the dynamic oil infiltration volume of the oil reservoir and achieve high-precision prediction.

[0095] (4) Test set results: The last 40 sets of oil reservoir numerical simulation schemes are used for testing. The results show that the average relative error of production rate prediction is less than 10%; the R 2 of well interference degree prediction can reach 0.92; the prediction of recovery factor and net present value also has high accuracy, as shown in Figure 4 .

[0096] S4. Implement multi-objective optimization based on the NSGA-III multi-objective optimization algorithm, comprehensively balance the recovery factor, net present value, and well interference degree on the premise of meeting engineering and economic constraints, and finally obtain the optimal well spacing and layer spacing layout that takes into account the recovery factor, net present value, and well interference degree through the organic combination of the CNN-LSTM deep learning prediction model and multi-objective optimization.

[0097] With the support of the CNN-LSTM deep learning prediction model, this embodiment proposes a three-dimensional well pattern optimization model based on the NSGA-III multi-objective optimization algorithm. This three-dimensional well pattern optimization model takes well spacing and layer spacing as key optimization parameters, comprehensively considers the recovery factor, net present value, and well interference degree, and realizes the three-dimensional optimization of the well pattern layout in the horizontal and vertical dimensions.

[0098] (1) Construct a three-dimensional well pattern optimization model based on the NSGA-III multi-objective optimization algorithm. The construction method is as follows:

[0099] I. Determine the objective function.

[0100] Objective 1: Maximize the recovery factor max f1(x): Improve the utilization degree of the reservoir and optimize the development effect; Objective 2: Maximize the net present value max f2(x): Comprehensively consider production, oil price, investment cost, and discount rate to optimize economic benefits; Objective 3: Minimize the well interference degree min f3(x): Reduce the interference between horizontal wells and vertical interval wells and improve the rationality of well pattern deployment.

[0101] II. Determine the decision variables.

[0102] Take the horizontal well spacing (affecting the well interference degree and reservoir utilization range) and vertical well spacing (reflecting the development degree and economic rationality of wells in different intervals) as decision variables, and optimize the variable set x:

[0103] x = {(X1, Y1), (X2, Y2), …, (X n , Y n ), d h , Δh};

[0104] Among them, (X i , Y i ) is the well location coordinate of the i-th well, i = 1, 2, …, n; d h is the horizontal spacing between adjacent wells; Δh is the layer spacing between adjacent wells in the vertical direction.

[0105] S43. Determine the constraint conditions:

[0106] Reservoir and geological constraints. Wells are not deployed in areas with porosity lower than 4% or inaccessible areas:

[0107]

[0108] Among them, is the minimum allowable horizontal well spacing, is the maximum allowable horizontal well spacing, is the minimum allowable horizontal well layer spacing, is the maximum allowable horizontal well layer spacing.

[0109] The well location and interval should avoid interbeds and fracture zones:

[0110] d h,min ≤ d h ≤ d h,max , Δh min ≤ Δh ≤ Δh max ;

[0111] Among them, d h,min is the minimum allowable horizontal well spacing, d h,max is the maximum allowable horizontal well spacing, Δh min is the minimum allowable horizontal well layer spacing, Δh max is the maximum allowable horizontal well layer spacing.

[0112] Economic constraint: The total investment cost does not exceed the budget ceiling:

[0113]

[0114] Among them, Cost(X i , Y i ) is the total cost of deploying wells at the coordinate position (X i , Y i ), and Budget max is the maximum investment budget ceiling allowed for the project.

[0115] In this embodiment, during multi-well network three-dimensional development, three objectives are concerned: recovery factor, net present value, and well interference degree; the decision variables include well location coordinates, horizontal section length, and injection-production mode.

[0116] (2) The design and solution method of the NSGA-III multi-objective optimization algorithm include:

[0117] I. Population initialization: Using the Latin hypercube sampling method, randomly generate an initial scheme of well spacing and layer spacing that meets the constraint conditions, covering the design space.

[0118] II. Fitness evaluation and reference point generation: Call the CNN-LSTM deep learning prediction model to predict the recovery factor, net present value, and well interference degree of each well network layout scheme, calculate the fitness of each individual according to the objective function value, and sort the individuals in combination with the effects of well spacing and layer spacing adjustment.

[0119] III. Solution Set Evolution and Reference Point Distribution: Generate reference points in the objective space of "recovery factor - net present value - inter-well interference degree" to guide the distribution of Pareto front solutions and ensure a reasonable trade-off between well spacing and layer spacing.

[0120] IV. Crossover Mutation and Iterative Update: Use simulated binary crossover and multi-point mutation operations to iteratively adjust well spacing and layer spacing and optimize the objective values.

[0121] V. Convergence Criterion: Set the maximum number of iterations or the convergence condition of Pareto front solutions, and output a set of optimal well patterns that are balanced and compromised in terms of recovery factor, net present value, and inter-well interference degree.

[0122] In this embodiment, the crossover mutation coefficient is finally selected as 0.45, the mutation coefficient is 0.05, the population size is 200, and the Pareto front solutions are output after 300 generations of iteration, as Figure 5 shown.

[0123] (3) After solving the NSGA-III multi-objective optimization algorithm, a set of Pareto front solutions is obtained, and each solution corresponds to a set of well pattern deployment and engineering parameter combinations.

[0124] The methods for in-depth analysis and comparative evaluation of the optimization results include: I. The optimization results are presented in the form of a Pareto front in the three-dimensional objective space composed of recovery factor, net present value, and inter-well interference degree. Each solution in the Pareto front represents a well pattern optimization scheme, and the spatial distribution of these schemes reflects the trade-off relationship between different objectives in multi-objective optimization.

[0125] II. Select several representative solutions from the Pareto optimal solution set for analysis to understand the characteristics of different well pattern layout schemes. Then, the decision maker can select the most suitable scheme from the complete Pareto optimal solution set based on actual needs and preferences.

[0126] III. Sensitivity and Uncertainty Analysis: For the finally selected most suitable scheme, observe the influence degree on recovery factor, net present value, and inter-well interference degree by changing well spacing, layer spacing, number of wells, and horizontal section length.

[0127] IV. Field Tests and Model Iteration: According to the comparison between the monitoring results and the predicted values, continuously correct the CNN-LSTM deep learning prediction model and the evaluation parameters of inter-well interference degree to form a closed loop of "numerical simulation - deep learning - multi-objective optimization - field feedback".

[0128] In the Pareto optimal solution set, three types of typical optimization schemes are selected for analysis: one is the high-production scheme, where the recovery factor is increased from 22% of the base scheme to 28%, an increase of 6%; the second is the high-benefit scheme, where the net present value is increased from 15×10 7from yuan to 20×10 7 yuan, an increase of 5×10 7 yuan; thirdly, for the low inter-well interference scheme, the inter-well interference degree is reduced from 0.5 to 0.2, a decrease of 0.3. As Figure 6 , Figure 7 and Figure 8 shown, by optimizing the injection-production relationship and well pattern parameters, the high-production capacity scheme achieves a better reservoir utilization effect, the high-benefit scheme achieves a significant improvement in economy, and the low inter-well interference scheme ensures the development stability by regulating the injection-production ratio.

[0129] The application of this embodiment shows that this embodiment can effectively solve the problem of three-dimensional well pattern optimization under inter-well interference conditions, and its optimization scheme performs excellently in terms of improving the recovery rate, increasing the net present value, and reducing the inter-well interference degree, fully demonstrating the high credibility and robustness of the three-dimensional well pattern optimization model based on the NSGA-III multi-objective optimization algorithm, and providing reliable technical support for the decision-making and deployment of reservoir engineers under complex development conditions.

[0130] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by those skilled in the art within the essence of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for evaluating the interference of a three-dimensional well pattern based on the dynamic oil drainage volume, characterized in that It includes the following steps: S1. Construct a multi-field coupling dynamic oil drainage volume characterization model based on the evolution of reservoir pressure field, fluid seepage characteristics and fracture network response to realize the quantitative and dynamic evaluation of well interference in shale reservoirs; S2. Based on the multi-field coupling dynamic oil drainage volume characterization model, establish a quantitative evaluation system for well interference degree, and use the quantitative evaluation system for well interference degree to measure the spatial overlap degree of oil drainage volume and pressure field difference of each well, providing a quantitative basis for well pattern layout and interference control.

2. The method for evaluating the interference of a three-dimensional well pattern based on the dynamic oil drainage volume according to claim 1, wherein, In step S1, the multi-field coupling dynamic oil drainage volume characterization model integrates the following dynamic characteristics: Dynamic evolution characteristics of pressure propagation: Reveal the pressure diffusion law of the reservoir under development dynamics; Seepage law of oil-water two-phase fluid: Quantify the spatial distribution of fluid saturation change; Dynamic response of fracture network: Dynamic evolution characteristics of fracture morphology under the coupling action of stress field-seepage field.

3. The method for evaluating the interference of a three-dimensional well pattern based on the dynamic oil drainage volume according to claim 2, wherein In step S1, based on the multi-phase seepage theory, establish a dynamic oil drainage volume calculation equation: V leak ′(t) = φ(p)·[S o,l,m,n (t + 1)-S o,l,m,n (t)]·V eff (p)·△p eff (t); Among them, V leak ′(t) represents the dynamic oil drainage volume at time t, φ(p) represents the pressure sensitivity function, p represents the formation pressure, and S o,l,m,n is the oil phase saturation of the corresponding grid block under the (l, m, n) spatial coordinates, and V eff (p) represents the effective stimulation volume varying with pressure, and △p eff (t) is the production pressure difference at time t.

4. The method for evaluating interference of a three-dimensional well pattern based on dynamic oil drainage volume according to claim 3, characterized in that The numerical simulation process of the multi-field coupling dynamic oil drainage volume characterization model is as follows: First, collect rock physical parameters, including but not limited to porosity, permeability and elastic modulus, and combine with the stress field distribution to build a three-dimensional geomechanics model; Secondly, use microseismic monitoring data and fracturing injection data to correct the fracture network and generate fracture geometric morphology close to the actual situation on site; Then, use numerical simulation to obtain the dynamic distribution of pressure and saturation and fracture transformation volume of different grids; Finally, substitute the porosity, permeability, formation pressure, dynamic distribution of saturation and fracture transformation volume of different grids into the dynamic oil drainage volume calculation equation to calculate the spatial-temporal distribution characteristics and evolution law of the dynamic oil drainage volume at each time point.

5. The method for evaluating the interference of a three-dimensional well pattern based on the dynamic oil drainage volume according to claim 4, wherein In step S2, the quantitative evaluation system for well interference degree includes oil drainage volume overlap degree, pressure field difference coefficient and time weighting function; The calculation formula for the oil drainage volume overlap degree is: V overlap V(t) = V A V(t) ∩ V B V(t); Among them, V overlap represents the overlapping area of the dynamic drainage volume, V A (t) and V B (t) respectively represent the dynamic drainage volumes of Well A and Well B at time t, and R overlap represents the drainage volume overlap ratio; The calculation formula for the pressure field difference coefficient is: Among them, D pressure represents the pressure field difference coefficient, N is the total number of pressure field grid cells, p A (k, t) and p B (k, t) are the pressure values of the k-th grid cell of well A and well B at time t, respectively; The calculation formula for the time weighting function is: w(t) = e -αt ; Among them, w represents the time weighting function, and α is the attenuation coefficient; Comprehensive evaluation index I of interference degree between wells total The calculation formula is as follows: Among them, T is the total time step, and both β and γ are weight coefficients.

6. The method for evaluating the interference of a three-dimensional well pattern based on the dynamic oil drainage volume according to claim 5, wherein When 0 ≤ I total < 0.3, the interference level between wells is weak interference; when 0.3 ≤ I total < 0.6, the interference level between wells is medium interference; when 0.6 ≤ I total ≤ 1, the interference level between wells is strong interference.

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