Method for constructing three-dimensional injection-production well pattern based on multi-objective algorithm
By constructing a three-dimensional injection-production well network based on a multi-objective algorithm, combined with geological models and numerical simulation streamline method, well location deployment is optimized, solving the accuracy problem of well connectivity analysis in fault-controlled reservoirs, realizing three-dimensional collaborative development, and improving recovery rate and economic benefits.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA UNIV OF PETROLEUM (EAST CHINA)
- Filing Date
- 2025-09-28
- Publication Date
- 2026-06-26
Smart Images
Figure CN121162233B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of oil and gas reservoir energy development technology, and in particular relates to a method for constructing a three-dimensional injection-production well network based on a multi-objective algorithm. Background Technology
[0002] Fault-controlled reservoirs are an important category of complex bedrock reservoirs, such as those in carbonate or metamorphic rocks. Their reservoir space and seepage channels are primarily controlled by large faults and associated fracture systems. Under the influence of multiple tectonic movements, they often form a "core-zone structure" model, with a fault as the core, comprising a core (breccia and densely fractured cavities), an outer core (fractured zone), and distal fracture zones. These reservoirs exhibit strong heterogeneity and anisotropy, characterized by diverse reservoir types, large scale differences, and complex spatial structures. Their seepage mechanisms differ significantly from those of conventional porous reservoirs.
[0003] Currently, the development of this type of reservoir largely follows the technical approach used for conventional reservoirs, which faces numerous challenges in practical application, resulting in generally unsatisfactory development outcomes. At present, well connectivity analysis in fault-controlled reservoir development relies heavily on simple comparisons between static geological data and production dynamics, lacking systematic and dynamic quantitative methods. This leads to inaccurate judgments of connectivity quality and dominant flow direction, easily causing unreasonable injection-production configurations, inefficient injection circulation, and poor displacement effects. Limited by model characterization capabilities, numerical simulations struggle to accurately depict the actual flow patterns of fluids in complex fractured-vuggy systems, resulting in a still relatively vague understanding of remaining oil distribution, mostly remaining at a macroscopic scale. This makes it impossible to accurately identify enrichment patterns such as "top loft oil" and "inter-well stagnant oil," leading to insufficient targeting and effectiveness of adjustment measures. Existing potential assessments typically rely solely on single indicators such as remaining oil saturation or static reserve abundance, failing to comprehensively consider key dynamic factors such as driving energy, flow capacity, and well network control. This results in designated potential areas potentially being difficult to effectively utilize due to insufficient energy or poor connectivity, reducing the scientific rigor of decision-making. Meanwhile, traditional two-dimensional uniform well networks are difficult to adapt to the three-dimensional distribution differences of the core zone structure. The development methods are singular and cannot effectively cope with the rapid depletion or bottom water coning problem caused by insufficient natural energy. They generally suffer from problems such as low control level, injection-production mismatch, poor sweep efficiency and low recovery rate.
[0004] Therefore, there is an urgent need to establish a targeted technical system that integrates high-precision geological characterization, quantitative evaluation of dynamic connectivity, comprehensive classification of development potential, and optimized deployment of three-dimensional well networks. Summary of the Invention
[0005] The purpose of this invention is to provide a method for constructing a three-dimensional injection-production well network based on a multi-objective algorithm, which effectively solves the problem that existing potential evaluations rely solely on single indicators such as remaining oil saturation or static reserve abundance, making it difficult to effectively utilize the designated potential areas.
[0006] To solve the above technical problems, the technical solution adopted by the present invention is: a three-dimensional injection-production well network construction method based on multi-objective algorithm, including the following steps: S1, based on geological model and seepage parameters, four core zone structure modes are proposed: fault core, fault outer core, densely developed fracture zone and secondary fracture zone.
[0007] S2. Using reservoir engineering methods and numerical simulation streamline method, well connectivity analysis is performed on reservoir units divided based on core zone structure model.
[0008] S3. Based on the embedded discrete fracture model and combined with the results of well connectivity analysis, the distribution pattern of remaining oil under the control of the core zone structure is analyzed.
[0009] S4. Combining remaining oil utilization potential, displacement power, flow capacity, connectivity, and reserve abundance, a multi-parameter comprehensive evaluation system suitable for the structural characteristics of the core zone of fault-controlled reservoirs is constructed.
[0010] S5. For both bottom-water depletion and bottom-water depletion development of fault-controlled reservoirs, a three-dimensional injection-production well network construction technology based on the comprehensive development potential index is formed by combining injection-production relationships and production methods.
[0011] S6. Based on a multi-objective optimization algorithm, with the three-dimensional synergistic optimization of recovery rate, well-controlled reserves and net present value as the core, an efficient well location deployment technology for different fault zones is constructed.
[0012] Furthermore, in step S1, the fault nucleus is located in the main core of the core belt structure, with a porosity between 0 and 0.1626%, an average porosity of 0.0243%, and a permeability between 47.6618 and 6141.3403 mD, with an average permeability of 734.4932 mD.
[0013] The outer core of the fault is located in a secondary part of the core zone structure, with a porosity between 0 and 0.05%, an average porosity of 0.0187%, and a permeability between 20.0967 and 2220.6714 mD, with an average permeability of 235.1783 mD.
[0014] The densely developed fracture zone is located around the core of the core-band structure and is the main fracture zone. The porosity is between 0 and 0.0035%, with an average porosity of 0.0013%. The permeability is between 6.1096 and 417.9504 mD, with an average permeability of 13.1166 mD.
[0015] Secondary fracture zones are located outside the main fracture zone of the core structure. They are external edge fracture zones or branch fracture zones with porosity between 0 and 0.0210%, with an average porosity of 0.0089% and permeability between 12.2195 and 144.7885 mD, with an average permeability of 31.8083 mD.
[0016] Furthermore, in step S2, the reservoir engineering method includes the interference well test method, the oil pressure response method, and the injection-production effect method. The analysis results of the interference well test method, the oil pressure response method, the injection-production effect method, and the numerical simulation streamline method are superimposed and integrated. If the analysis results of the four methods are consistent, it indicates that there is connectivity between wells within the unit.
[0017] The principle of the interference well test method is to use the change of the oil well operating system as an excitation signal and observe the response of the pressure and production parameters of the adjacent wells. If there is a connection between the wells, the parameter change trend of the responding well is correlated and consistent with the operation of the excitation well in time.
[0018] The principle of the oil pressure response method is as follows: when production is in progress, the fluid in the unit is gradually extracted, the formation energy is continuously reduced, and the range of the pressure drop funnel is continuously expanded. If there is connectivity between wells, the pressure drop funnel formed during the production process will cause the formation pressure of all wells in the same connected unit to show a synchronous downward trend.
[0019] The principle of the injection-production-response method is to use the water injection behavior of the injection well as the excitation signal to observe whether the production volume, water cut, and oil pressure parameters of the surrounding production wells show a response. The existence and quality of connectivity are judged by the time, intensity, and direction of the response.
[0020] The principle of the numerical simulation streamline method is as follows: establish a numerical simulation model of the target unit, use production dynamic data for historical fitting, and combine streamline numerical simulation to analyze the inter-well connectivity of the target unit to determine the overall streamline density and overall connectivity of the target unit.
[0021] Furthermore, in step S3, the remaining oil distribution pattern under the control of the core zone structure is: "core enrichment, partial activation of the outer core, rapid activation of the main fractures, and retention in secondary fractures"; the remaining oil distribution pattern of the water injection development unit is: "attic oil at the top of the core, partial activation of the outer core, and retention in the water-flooded channels of the fracture zone"; the remaining oil distribution pattern of the depletion development unit is: "core enrichment, no activation between wells, and unconnected fracture zones".
[0022] Furthermore, in step S4, the post-processing function of Petrel RE is used to process each grid in the geological model. The remaining oil utilization potential, displacement power, flow capacity, connectivity, and reserve abundance are extracted and substituted into the comprehensive development potential index formula for calculation. The values are normalized. The higher the index, the greater the development potential of the region.
[0023] Comprehensive Development Potential Index :
[0024] ;
[0025] in, Indicates the potential for utilization of remaining oil, referring to time Oil saturation of the grid With residual oil saturation The difference.
[0026] Indicates driving force, refers to time Formation pressure of the grid With minimum flow pressure threshold The difference.
[0027] Indicates liquidity, refers to Absolute permeability of the grid Relative permeability of oil phase at current oil saturation The logarithm of the product of .
[0028] It indicates connectivity and represents the degree of connectivity between individual wells.
[0029] express The abundance of the grid is related to the effective thickness. Crack density Crack opening Porosity Compared to clean hair The product of.
[0030] Furthermore, in step S4, the remaining oil utilization potential data comes from the remaining oil saturation field obtained by numerical simulation of the embedded discrete fracture model in step S3; the displacement force comes from the pressure field obtained by numerical simulation of the embedded discrete fracture model in step S3; the absolute permeability comes from the geological model, and the relative permeability of the oil phase comes from the relative permeability curve; the connectivity relationship is quantified and assigned a value by the well connectivity analysis results in step S2, with the value of strongly connected regions approaching 1 and the value of weakly connected or unconnected regions approaching 0; the reserve abundance comes from the static geological model.
[0031] Furthermore, in step S5, for the development of fracture-controlled reservoirs with bottomless water depletion, a three-dimensional well network of "shallow expansion and deep deepening well production" is adopted, which takes into account both shallow and deep reservoirs and injection and production. A development method combining nitrogen gas miscible displacement and gravity assistance is adopted, and a graded development strategy of "core priority, outer core progression, and fracture zone assistance" is adopted.
[0032] For reservoirs with bottom water depletion and fault control, a three-dimensional well network of "shallow gas injection, deep water injection, and mid-section oil production" is constructed to simultaneously control bottom water and drive residual oil. A development method combining nitrogen-gas miscible flooding and gravity-assisted synergy is adopted, and an injection-production method of "gas injection to form a gas cap, suppressing bottom water coning, and expanding the swept volume" is used. A development strategy of "bottom water suppression and regulation, and balanced gas injection utilization" is adopted.
[0033] Furthermore, in step S6, based on the NSGA-III multi-objective optimization algorithm, well spacing and depth are used as key optimization parameters, and recovery rate, net present value and well-controlled reserves are comprehensively considered to achieve three-dimensional optimization of well network layout in both horizontal and vertical dimensions.
[0034] Furthermore, in step S6, the design and solution process of the NSGA-III multi-objective optimization algorithm is as follows: S61, Population initialization: The Latin hypercube sampling method is used to randomly generate initial schemes for well spacing and depth that satisfy the constraints, covering the design space.
[0035] The constraints are: well locations should not be deployed in areas with extremely low porosity or inaccessible areas; well locations and formations should avoid low-permeability or unstable areas; and the total investment cost should not exceed the budget limit.
[0036] S62. Fitness Assessment and Reference Point Generation: The CNN-LSTM model is called to predict the recovery rate, net present value and well-controlled reserves for each well pattern layout scheme; the fitness of each individual is calculated based on the objective function value, and the individuals are ranked in combination with the effects of well spacing and depth adjustment.
[0037] S63. Solution set evolution and reference point distribution: Generate reference points in the target space of "recovery rate - net present value - well-controlled reserves" to guide the distribution of Pareto front solutions and ensure a reasonable trade-off between well spacing and depth.
[0038] S64. Crossover Mutation and Iterative Update: Using simulated binary crossover and multi-point mutation operations, well spacing and depth are iteratively adjusted to optimize the target values.
[0039] S65. Convergence Judgment: Set the maximum number of iterations or the convergence condition of the Pareto front solution, and output a set of optimal well network layouts that balance the trade-offs in recovery rate, net present value and well-controlled reserves.
[0040] Compared with the prior art, the beneficial technical effects of the present invention are: the present invention overcomes many limitations of traditional methods and realizes a leap from "experience-driven, two-dimensional plane, rough and uniform" development to "data-driven, three-dimensional, precise and targeted" development.
[0041] (1) The inter-well connectivity analysis has been transformed from "static speculation" to "dynamic verification," significantly improving reliability. This invention comprehensively utilizes various dynamic analysis methods, such as interference testing, oil pressure response, and injection-production effectiveness, and uses the numerical simulation streamline method as a verification means, forming a comprehensive analysis process with mutual verification of multiple evidence bodies. This greatly improves the accuracy and reliability of inter-well connectivity judgment.
[0042] (2) The well network deployment strategy has changed from "two-dimensional plane" to "three-dimensional collaboration", fundamentally improving the development effect. This invention proposes the principle of "potential-based well network determination, reserve-scale-based well number control, and injection-production control through charging channels", and forms three-dimensional injection-production well networks of "shallow expansion and deep production" and "gas above water and water below water production" for two scenarios with and without bottom water. This realizes the three-dimensional, differentiated, and collaborative development of the reservoir.
[0043] (3) Innovatively introduce multi-objective optimization algorithm (NSGA-III), with three-dimensional collaborative optimization of recovery rate, well-controlled reserves and net present value as the core, to build efficient well location deployment technology for different fault zones, which solves the problem of insufficient reserve utilization and economic imbalance in traditional well location design based solely on the single objective of cumulative oil production. Attached Figure Description
[0044] Figure 1 This is a flowchart of the method of the present invention.
[0045] Figure 2 This is a diagram showing the relationship between the excitation and interference of well testing in Example 1.
[0046] Figure 3 This is a graph showing the response curves of oil pressure changes in wells M4, M5, and M6 in Example 1.
[0047] Figure 4 This is a schematic diagram of the Pareto front solution set output in Example 1.
[0048] Figure 5 This is a schematic diagram of the adjustment of the high recovery optimization scheme in the Pareto optimal solution set output by Example 1.
[0049] Figure 6 This is a schematic diagram showing the adjustment of the high-well-controlled reserves optimization scheme in the Pareto optimal solution set output by Example 1.
[0050] Figure 7 This is the net present value evolution diagram based on artificial intelligence for 500 well location optimization iterations in the Pareto optimal solution set output in Example 1. Detailed Implementation
[0051] Example 1: A specific example is a block in the TLM oilfield. This block is a typical fault-controlled core-zone structure reservoir with the characteristics of "low porosity, low permeability, and strong heterogeneity". The average porosity is 0.023%, the average permeability is 50 Md, and the reservoir depth is 6000-8000 m. Faults, fractures, and core-zone structures are relatively well-developed in the block. The block adopts a three-dimensional development model. By constructing a three-dimensional injection-production well network and optimizing well locations based on multi-objective optimization algorithms, efficient utilization can be achieved. The actual well layout includes 4 horizontal production wells. The well trajectory is deployed along the direction of dominant stress, and the production wells adopt a constant pressure exhaustion production mode.
[0052] The method for constructing a three-dimensional injection-production well network based on a multi-objective algorithm provided in this embodiment includes the following steps, such as... Figure 1 As shown: S1. Based on the geological understanding and detailed description of fault-controlled oil and gas reservoirs, and utilizing geological data and dynamic and static development data, the reservoir characteristics of the core zone structure are understood.
[0053] Using porosity and permeability parameters, four core-zone structural models are proposed: fault-core, fault-outer core, densely fractured zone, and secondary fractured zone. The fault-core, located in the main core of the core-zone structure, has a porosity between 0 and 0.1626%, with an average porosity of 0.0243%, and a permeability between 47.6618 and 6141.3403 mD, with an average permeability of 734.4932 mD. The fault-outer core, located in the secondary part of the core-zone structure, has a porosity between 0 and 0.05%, with an average porosity of 0.0187%, and a permeability between 20.0967 and 2220.6714 mD, with an average permeability of 235.1783 mD. The densely fractured zone is located in the core... The core of the core-belt structure is surrounded by the main fracture zone, with a porosity between 0 and 0.0035%, an average porosity of 0.0013%, and a permeability between 6.1096 and 417.9504 mD, with an average permeability of 13.1166 mD. The secondary fracture zone is located outside the main fracture zone of the core-belt structure, and consists of outer edge fracture zones or branch fracture zones. Its porosity is between 0 and 0.0210%, with an average porosity of 0.0089%, and its permeability is between 12.2195 and 144.7885 mD, with an average permeability of 31.8083 mD.
[0054] S2. By comprehensively utilizing dynamic production data and employing various reservoir engineering methods (interference well testing, oil pressure response, and injection-production effectiveness) and numerical simulation streamline method, the inter-well connectivity of reservoir units initially divided based on the core-zone structure model is verified and refined.
[0055] This embodiment employs the following four methods for cross-validation to improve the reliability of the results.
[0056] (1) The interference well test method uses changes in the working conditions of oil wells (such as new well commissioning, well opening and closing, and adjustment of nozzles) as natural "excitation signals" to observe the response of parameters such as pressure and production of surrounding wells (i.e., "response signals"). If there is connectivity between wells, the parameter change trend of the responding well should be highly correlated and consistent with the operation of the excitation well in time.
[0057] The implementation method involves using the slope variation of a double logarithmic curve to determine the connectivity of suspected interference signals based on their amplitude. For example... Figure 2 As shown, only slight abnormal signals can be seen from the measured curves of wells A1 and A2. However, there is a slight rise in the derivative curve in the double logarithmic curve. Judging from the amplitude of the interference signal, A1 and A2 are connected, but the connection is weak.
[0058] (2) The oil pressure response method utilizes the principle that formation pressure is a unified whole within the same connected unit. During production, fluid is gradually extracted from the unit, formation energy continuously decreases, and the pressure drop funnel expands. If there is connectivity between wells, the pressure drop funnel formed during production will cause the formation pressure of all wells within the same connected unit to show a synchronous downward trend. Even in a stable production phase without changes in the operating regime, the fluctuations in oil pressure should exhibit a high degree of similarity. For example... Figure 3 As shown, a continuous production period was selected where the operating procedures for all wells remained unchanged. Daily wellhead oil pressure data for wells suspected to be within the same unit (e.g., wells M4, M5, and M6) were extracted during this production period. After data normalization, the data were plotted in the same chart for trend comparison. Figure 3 It can be seen that the oil pressure change curves of wells M4, M5, and M6 highly overlap, with the inflection points of the rise and fall almost perfectly synchronized. This indicates that they all responded to the same small fluctuations in formation pressure, strongly suggesting that the three belong to the same connected reservoir unit.
[0059] (3) The injection-production effect method uses the water injection behavior of the injection well as the excitation signal to observe whether the parameters such as the production volume, water cut, and oil pressure of the surrounding production wells show an effective response (such as an increase in production volume, recovery of oil pressure, and change in water cut). The existence and quality of connectivity are judged by the time, intensity and direction of the effective response.
[0060] The implementation method involves selecting the injection cycle of the injection well, for example, starting water injection and churn operations at well Y4. Production volume and oil pressure data of surrounding candidate wells (wells Y5 and Y6) are monitored and extracted during the injection period and the production phase after injection cessation. The lag of changes in response well parameters relative to the start time of water injection is analyzed.
[0061] (4) The principle of the numerical simulation streamline method is: establish a numerical simulation model of the target block, use the previously collected production dynamic data for historical fitting, start the streamline simulation function, combine streamline numerical simulation to analyze the inter-well connectivity of the target block, and determine the overall streamline density and overall connectivity of the target block.
[0062] The implementation method involves using pre-processed production dynamic data (production rate, pressure, etc.) as input and running a numerical simulator. By repeatedly adjusting parameters such as permeability, porosity, and connectivity, the simulated pressure, production, and other data achieve a high degree of agreement (fit rate > 85%) with the actual historical data of the oilfield. Based on the well-fitted numerical simulation model, streamline distribution diagrams for a specific time period are calculated and plotted. The density of streamlines indicates the flow rate, and the direction of streamlines indicates the main flow path of the fluid.
[0063] The analysis results of the interference well test method, oil pressure response method, injection and production effect method and numerical simulation streamline method are superimposed and integrated. If the analysis results of the four methods are consistent, it indicates that there is connectivity between wells in the unit.
[0064] S3. Based on the embedded discrete fracture model (EDFM) and combined with the results of inter-well connectivity analysis, the distribution pattern of remaining oil under the control of the core zone structure is analyzed.
[0065] This step aims to accurately characterize the remaining oil distribution in complex fracture network systems of fault-controlled reservoirs, providing a direct basis for efficient well placement. Its core lies in utilizing an advanced embedded discrete fracture model (EDFM), combined with previous inter-well connectivity analysis results, to quantitatively and visually study the remaining oil enrichment pattern controlled by the core zone structure. The specific implementation process is as follows.
[0066] In fault-controlled reservoirs, the fracture and fracture systems within the core zone are highly irregular and densely developed, making it difficult for traditional structured meshes to accurately characterize their geometry and flow effects. The EDFM method allows the main fractures and fractures of different scales to be embedded as discretized entities (such as thin plates) into the background matrix mesh, accurately describing the orientation, conductivity, and fluid exchange between fractures and the matrix without the need for complex local mesh refinement.
[0067] The implementation method involves importing the EDFM geological model into a numerical simulator and loading historical dynamic data (production, pressure, water cut, etc.) from all producing wells. By adjusting relevant parameters of the matrix and fractures (such as fracture conductivity and matrix supply capacity to fractures), the production history is fitted to ensure that the model can accurately reproduce the actual production history of the oilfield. A well-fitted EDFM model is a prerequisite for reliably predicting the distribution of remaining oil. The model is then run to the current development stage or to predict future conditions, extracting the remaining oil saturation field, pressure field, and streamline distribution from the EDFM model.
[0068] By combining streamline simulation and saturation distribution maps, the enrichment location and scale of residual oil in complex fracture-vuggy networks are visually revealed. This embodiment proposes: ① Residual oil distribution patterns under core-zone structure control: "Core enrichment, partial utilization of the outer core, rapid utilization of main fractures, and retention in secondary fractures." ② Residual oil distribution patterns in water injection development units: "Attic oil at the top of the core, partial utilization of the outer core, and retention between water-flooded channels in the fracture zone." That is, water flow preferentially advances along high-permeability main fractures, forming "water-dominant channels," causing crude oil in the surrounding matrix and secondary fractures to be bypassed, thus forming "attic oil at the top" (located in the high-position matrix) and "retained oil between water-flooded channels in the fracture zone." ③ Residual oil distribution patterns in depleted development units: "Core enrichment, no utilization between wells, and unconnected fracture zones." That is, due to the lack of effective displacement energy, the degree of crude oil utilization is mainly controlled by well location and connectivity. Residual oil is highly enriched in the matrix "within the core zone."
[0069] S4. Combining remaining oil utilization potential, displacement power, flow capacity, connectivity, and reserve abundance, a multi-parameter comprehensive evaluation system suitable for the structural characteristics of the core zone of fault-controlled reservoirs is constructed.
[0070] This step aims to overcome the limitations of traditional development potential evaluation methods (which only consider remaining oil or reserve abundance) and creatively construct a multi-dimensional parameter comprehensive evaluation system suitable for the core zone structure characteristics of fault-controlled reservoirs. This provides core technical means for achieving quantitative, refined, and hierarchical evaluation of development potential. The specific implementation process is as follows.
[0071] (1) Construction of multi-parameter comprehensive evaluation system and parameter determination.
[0072] This embodiment constructs a comprehensive development potential index based on five core dimensions: remaining oil utilization potential, displacement power, flow capacity, connectivity, and reserve abundance. The model is described below. The meanings of each indicator and the data sources are as follows.
[0073] ① Remaining oil utilization potential ( ):refer to time Oil saturation of the grid With residual oil saturation The difference represents the mobility of the remaining oil at that location. This data is directly derived from the remaining oil saturation field obtained through numerical simulation using the EDFM model in step S3.
[0074] ②Driving power ( ):refer to time Formation pressure of the grid With minimum flow pressure threshold The difference (such as abandoned pressure) characterizes the energy driving the fluid to flow into the wellbore. This data comes from the pressure field obtained from the numerical simulation of the EDFM model in step S3.
[0075] ③Flow capacity ( ):refer to Absolute permeability of the grid Relative permeability of oil phase at current oil saturation The logarithm of the product of the two. It comprehensively characterizes the ease of oil flow determined by rock and fluid properties. Absolute permeability is derived from geological models, while relative oil-phase permeability is derived from relative permeability curves.
[0076] ④ Connectivity ( ): This parameter represents the degree of connectivity between individual wells and is a dimensionless coefficient between 0 and 1. It is assigned a value after being quantified from the well connectivity analysis results (such as streamline density and disturbance response intensity) in step S2. The value of strongly connected regions approaches 1, while the value of weakly connected or unconnected regions approaches 0.
[0077] ⑤ Grid abundance ( ): for effective thickness Crack density Crack opening Porosity Compared to clean hair The product of these parameters represents the enrichment level of oil resources within the grid. This parameter is derived from a static geological model.
[0078] (2) Calculation and dimensionless transformation of development potential index.
[0079] Multiplying the parameters of the above five dimensions (remaining oil utilization potential, displacement power, flow capacity, connectivity, and reserve abundance) forms a comprehensive development potential index. Calculation formula:
[0080] .
[0081] This formula comprehensively reflects the five key aspects of "recoverable oil (remaining oil), energy-driven (pressure), energy flow (flow capacity), extractable (connectivity), and sufficient scale (reserves)." Using Petrel RE's post-processing capabilities, each grid in the geological model is processed... The remaining oil potential, displacement force, flow capacity, connectivity, and reserve abundance are extracted and substituted into the comprehensive development potential index formula to generate a three-dimensional model of the entire reservoir. Index data volume. To avoid the influence of dimensions and facilitate regional comparisons, the calculated data will be... The values are normalized so that they fall within the range of 0 to 1. The normalized values are then... The higher the index, the greater the development potential of the region.
[0082] S5. For both bottom-water depletion and bottom-water depletion development of fault-controlled reservoirs, a three-dimensional injection-production well network construction technology based on the comprehensive development potential index is formed by combining injection-production relationships and production methods.
[0083] This step integrates and applies the aforementioned research findings. Its core lies in transforming geological understanding, remaining oil distribution, and quantitative evaluation results of development potential into a set of three-dimensional well network deployment technologies that can guide field practice, thereby achieving efficient development of fault-controlled reservoirs.
[0084] For bottomless, depleted, and fracture-controlled reservoirs, a multi-dimensional well network is needed for development. These reservoirs have insufficient natural energy and low primary recovery rates. The approach needs to shift from a single "surface well network" to a multi-dimensional network that considers both shallow and deep reservoirs, as well as injection and production, employing a "shallow-area sweep-deep well production" strategy. A development approach combining nitrogen miscible displacement with gravity-assisted extraction is adopted. Nitrogen is used to create miscibility or near-miscibility with crude oil, significantly reducing crude oil viscosity and interfacial tension. Simultaneously, gravity differentiation drives the gas to rise and propel the remaining oil at the top. A staged development strategy of "core priority, outer core progression, and fracture zone assistance" is employed. Ultimately, the shallow reservoirs will be dominated by a gas injection well network to expand sweep, while the deep reservoirs will be dominated by a production well network for efficient extraction.
[0085] For the construction of a three-dimensional well network for fault-controlled reservoirs with bottom water depletion, these reservoirs face the risk of bottom water propulsion, and conventional development is prone to water flooding. A three-dimensional well network of "shallow gas injection, deep water injection, and mid-section oil production" is required to simultaneously control bottom water and drive residual oil. A development approach combining nitrogen-gas miscible flooding with gravity-assisted synergy is preferred. The injection-production method employs "gas injection to form a gas cap, suppress bottom water coning, and expand the swept volume." A development strategy of "bottom water suppression and regulation, and balanced gas utilization" is adopted. Ultimately, through the regulation of deep water injection wells, the oil-water interface is uniformly and slowly raised to avoid localized water channeling; through shallow gas injection, the residual oil at the top of the structure is effectively utilized, and the swept volume is expanded through gravity differentiation, achieving balanced displacement.
[0086] S6. Based on a multi-objective optimization algorithm, and with the three-dimensional synergistic optimization of recovery rate, well-controlled reserves, and net present value as the core, an efficient well location deployment technology for different fault zones is constructed. This embodiment is based on the NSGA-III multi-objective optimization algorithm, with well spacing and depth as key optimization parameters, comprehensively considering recovery rate, net present value, and well-controlled reserves to achieve three-dimensional optimization of well network layout in both horizontal and vertical dimensions.
[0087] (I) Construction of a multi-objective optimization mathematical model.
[0088] (1) Objective function.
[0089] Objective 1: Maximize oil recovery (RF). Improve reservoir utilization and optimize development results.
[0090] Objective 2: Maximize Net Present Value (NPV). Optimize economic returns by comprehensively considering production volume, oil prices, investment costs, and discount rates.
[0091] Objective 3: Well-controlled reserves (QN). Improve the level of well-controlled reserves and enhance the rationality of well network deployment.
[0092] (2) Decision variables.
[0093] Well location coordinates and depth: Well location coordinates affect the degree of reserve control and the scope of reservoir utilization, while well depth reflects the development level and economic rationality of wells at different depths.
[0094] Optimize the set of variables:
[0095] ;
[0096] in, For the first Well location coordinates, ; Number of wells; For the first The depth of the well.
[0097] (3) Constraints. Well locations should not be deployed in areas with extremely low porosity or inaccessible areas, and should avoid crossing areas with unfavorable fault and fracture networks; well locations and formations should avoid low-permeability or unstable areas; economic constraints: total investment costs should not exceed the budget limit.
[0098] (II) Design and solution of NSGA-III multi-objective optimization algorithm.
[0099] (1) Population initialization: The Latin hypercube sampling method is used to randomly generate initial schemes for well spacing and depth that meet the constraints, covering the design space.
[0100] (2) Fitness assessment and reference point generation: The CNN-LSTM model is used to predict the recovery rate, net present value and well-controlled reserves for each well pattern layout scheme. The fitness of each individual is calculated based on the objective function value, and the individuals are ranked in combination with the effects of well spacing and depth adjustment.
[0101] (3) Solution set evolution and reference point distribution: Reference points are generated in the target space of "recovery rate - net present value - well controlled reserves" to guide the distribution of Pareto front solutions and ensure a reasonable trade-off between well spacing and depth.
[0102] (4) Cross-mutation and iterative update: Using simulated binary cross-mutation (SBX) and multi-point mutation operations, the well spacing and depth are iteratively adjusted to optimize the target value.
[0103] (5) Convergence determination: Set the maximum number of iterations or the convergence condition of the Pareto front solution, and output a set of optimal well network layouts that balance the recovery rate, net present value and well-controlled reserves.
[0104] (III) Analysis and application of well network parameter optimization results.
[0105] After completing the NSGA-III multi-objective solution, a set of Pareto front solutions is obtained, each corresponding to a set of well network deployment and engineering parameter combinations. In this embodiment, a crossover variation coefficient of 0.39, a variation coefficient of 0.02, a population size of 200, and 300 iterations are selected to output the Pareto front solution. Figure 4 As shown.
[0106] To ensure the results are fully verified and applied, in-depth analysis and comparative evaluation of the optimization results are required, mainly including: (1) such as Figure 4 As shown, the optimization results are presented in the form of Pareto fronts in the three-dimensional target space (recovery rate - net present value - well-controlled reserves). Each solution in the Pareto front represents a well network optimization scheme, and its distribution reflects the trade-off between different objectives in multi-objective optimization. (2) Select several representative solutions: Scheme A: highest recovery rate, but NPV or well-controlled reserves may not be ideal; Scheme B: highest NPV, but recovery rate may be relatively average; Scheme C: high well-controlled reserves, recovery rate may be average; Scheme D: relatively compromise, the solution with the most balanced comprehensive indicators. Different solutions correspond to different well network layout schemes, and development decision-makers can choose the most suitable scheme according to actual needs and preferences. (3) Sensitivity and uncertainty analysis: Change key parameters such as well spacing, depth, and number of wells to observe the degree of influence on recovery rate, NPV and well-controlled reserves. (4) Field test and model iteration: In practical applications, one or more optimal solution schemes can be deployed in the oilfield, and the effect of the optimization scheme can be verified by combining microseismic monitoring, repeated logging and well testing. During the experiment, the optimization model can be further improved through the following steps: a closed loop of "numerical simulation - deep learning - multi-objective optimization - field feedback" to improve the effectiveness and accuracy of well pattern optimization.
[0107] In this embodiment, two typical optimization schemes are selected for analysis from the Pareto front solution set: one is the high-recovery scheme, which fully utilizes injected water / gas by optimizing the injection-production relationship to improve recovery rate and economic benefits, such as... Figure 5As shown, the recovery rate increased from 17.1% in the basic scheme (blue curve) to 23.4% in the optimized scheme (red curve), an increase of 6.3%. Secondly, the high-well-controlled reserve scheme ensures development stability by adjusting the injection-production ratio, such as... Figure 6 As shown, well-controlled reserves increased from 7.58 million tons to 9.97 million tons, an increase of 31.61%. Furthermore, as... Figure 7 As shown, the net present value (NPV) is 80% of the base case (blue curve). 10 7 The original value increased to 170% of the optimized solution (red curve). 10 7 Yuan, an increase of 90 10 7 Yuan.
[0108] This embodiment demonstrates that the method provided by the present invention can effectively solve the problem of three-dimensional well network optimization in target blocks. Its optimization scheme performs well in terms of recovery rate improvement, net present value increase and well-controlled reserves improvement, which fully proves the high reliability and robustness of the well location optimization method based on the NSGA-III algorithm, and provides reliable technical support for reservoir engineers to make decisions and deployments under complex development conditions.
[0109] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.
Claims
1. A method for constructing a three-dimensional injection-production well network based on a multi-objective algorithm, characterized in that, Includes the following steps: S1. Based on geological models and seepage parameters, four core zone structural models are proposed: fault inner core, fault outer core, densely developed fracture zone, and secondary fracture zone. S2. Using reservoir engineering methods and numerical simulation streamline method, the inter-well connectivity of reservoir units divided based on core zone structure model is analyzed. S3. Based on the embedded discrete fracture model and combined with the results of inter-well connectivity analysis, the distribution pattern of remaining oil under the control of the core zone structure is analyzed. S4. Combining remaining oil utilization potential, displacement power, flow capacity, connectivity and reserve abundance, construct a multi-parameter comprehensive evaluation system suitable for the structural characteristics of the core zone of fault-controlled reservoirs. S5. For both bottom-water depletion and bottom-water depletion development of fault-controlled reservoirs, a three-dimensional injection-production well network construction technology based on the comprehensive development potential index is formed by combining injection-production relationships and production methods. S6. Based on a multi-objective optimization algorithm, with the three-dimensional synergistic optimization of recovery rate, well-controlled reserves and net present value as the core, construct an efficient well location deployment technology for different fault zones; In step S2, the reservoir engineering methods include the interference well test method, the oil pressure response method, and the injection-production effect method. The analysis results of the interference well test method, the oil pressure response method, the injection-production effect method, and the numerical simulation streamline method are superimposed and integrated. If the analysis results of the four methods are consistent, it indicates that there is connectivity between wells in the unit. The principle of the interference well test method is: using the change of the oil well working system as an excitation signal, and observing the response of the pressure and production parameters of the adjacent wells. If there is a connection between the wells, the parameter change trend of the responding well is correlated and consistent with the operation of the excitation well in time. The principle of the oil pressure response method is: when production is in progress, the fluid in the unit is gradually extracted, the formation energy is continuously reduced, and the range of the pressure drop funnel is continuously expanded. If there is connectivity between wells, the pressure drop funnel formed during the production process will cause the formation pressure of all wells in the same connected unit to show a synchronous downward trend. The principle of the injection-production-effective method is: using the water injection behavior of the injection well as the excitation signal, observing whether the production volume, water cut, and oil pressure parameters of the surrounding production wells show an effective response, and judging the existence and quality of connectivity by the time, intensity and direction of the effective response. The principle of the numerical simulation streamline method is: to establish a numerical simulation model of the target unit, to perform historical fitting using production dynamic data, and to combine streamline numerical simulation to analyze the inter-well connectivity of the target unit, thereby determining the overall streamline density and overall connectivity of the target unit. In step S3, the residual oil distribution pattern under the control of the core zone structure is: "core enrichment, partial activation of the outer core, rapid activation of the main fractures, and retention of secondary fractures"; The remaining oil distribution pattern in the water injection development unit is: "oil in the top attic of the core, partially utilized in the outer core, and retained in the water-flooded channels of the fracture zone"; The remaining oil distribution pattern of the depleted development unit is: "core enrichment, unused inter-well areas, and unconnected fracture zones"; In step S4, the post-processing function of Petrel RE is used to process each grid in the geological model. The remaining oil utilization potential, displacement power, flow capacity, connectivity, and reserve abundance are extracted and substituted into the comprehensive development potential index formula for calculation. The values are normalized. The higher the index, the greater the development potential of the region; Comprehensive Development Potential Index : ； in, Indicates the potential for utilization of remaining oil, referring to time Oil saturation of the grid With residual oil saturation The difference; Indicates driving force, refers to time Formation pressure of the grid With minimum flow pressure threshold The difference; Indicates liquidity, refers to Absolute permeability of the grid Relative permeability of oil phase at current oil saturation The logarithm of the product; It indicates connectivity and characterizes the degree of connectivity between individual wells; express The abundance of the grid is related to the effective thickness. Crack density Crack opening Porosity Compared to clean hair The product of.
2. The method for constructing a three-dimensional injection-production well network based on a multi-objective algorithm according to claim 1, characterized in that, In step S1, the fault nucleus is located in the main core of the core belt structure, with a porosity between 0 and 0.1626%, an average porosity of 0.0243%, and a permeability between 47.6618 and 6141.3403 mD, with an average permeability of 734.4932 mD. The outer core of the fault is located in the secondary part of the core zone structure, with a porosity between 0 and 0.05%, an average porosity of 0.0187%, and a permeability between 20.0967 and 2220.6714 mD, with an average permeability of 235.1783 mD. The densely developed fracture zone is located around the core of the core-band structure and is the main fracture zone. The porosity is between 0 and 0.0035%, with an average porosity of 0.0013%, and the permeability is between 6.1096 and 417.9504 mD, with an average permeability of 13.1166 mD. Secondary fracture zones are located outside the main fracture zone of the core structure. They are external edge fracture zones or branch fracture zones with porosity between 0 and 0.0210%, with an average porosity of 0.0089% and permeability between 12.2195 and 144.7885 mD, with an average permeability of 31.8083 mD.
3. The method for constructing a three-dimensional injection-production well network based on a multi-objective algorithm according to claim 2, characterized in that, In step S4, the remaining oil utilization potential data comes from the remaining oil saturation field obtained by numerical simulation of the embedded discrete crack model in step S3. The displacement force originates from the pressure field obtained by numerical simulation of the embedded discrete crack model in step S3. The absolute permeability is derived from a geological model, and the relative permeability of the oil phase is derived from the relative permeability curve. The connectivity relationship is quantified and assigned a value by the well connectivity analysis results in step S2. The value of a strongly connected region is close to 1, and the value of a weakly connected or unconnected region is close to 0. The abundance of reserves is derived from a static geological model.
4. The method for constructing a three-dimensional injection-production well network based on a multi-objective algorithm according to claim 3, characterized in that, In step S5, for the development of fracture-controlled reservoirs with no bottom water depletion, a three-dimensional well network of "shallow wave propagation and deep well deepening" is adopted, which takes into account both shallow and deep reservoirs and injection and production. A development method combining nitrogen gas miscible displacement and gravity assistance is adopted, and a graded development strategy of "core priority, outer core progression, and fracture zone assistance" is adopted. For reservoirs with bottom water depletion and fault control, a three-dimensional well network of "shallow gas injection, deep water injection, and mid-section oil production" is constructed to simultaneously control bottom water and drive residual oil. A development method combining nitrogen-gas miscible flooding and gravity-assisted synergy is adopted, and an injection-production method of "gas injection to form a gas cap, suppressing bottom water coning, and expanding the swept volume" is used. A development strategy of "bottom water suppression and regulation, and balanced gas injection utilization" is adopted.
5. The method for constructing a three-dimensional injection-production well network based on a multi-objective algorithm according to claim 4, characterized in that, In step S6, based on the NSGA-III multi-objective optimization algorithm, well spacing and depth are used as key optimization parameters. The recovery rate, net present value and well-controlled reserves are comprehensively considered to achieve three-dimensional optimization of the well network layout in both horizontal and vertical dimensions.
6. The method for constructing a three-dimensional injection-production well network based on a multi-objective algorithm according to claim 5, characterized in that, In step S6, the design and solution process of the NSGA-III multi-objective optimization algorithm is as follows: S61. Population initialization: The Latin hypercube sampling method is used to randomly generate initial schemes for well spacing and depth that meet the constraints, covering the design space; The constraints are: well locations should not be deployed in areas with extremely low porosity or inaccessible areas; well locations and formations should avoid low-permeability or unstable areas; and the total investment cost should not exceed the budget limit. S62. Fitness Assessment and Reference Point Generation: The CNN-LSTM model is used to predict the recovery rate, net present value, and well-controlled reserves for each well pattern layout scheme; the fitness of each individual is calculated based on the objective function value, and the individuals are ranked in combination with the effects of well spacing and depth adjustment; S63. Solution set evolution and reference point distribution: Generate reference points in the target space of "recovery rate - net present value - well-controlled reserves" to guide the distribution of Pareto front solutions and ensure a reasonable trade-off between well spacing and depth. S64. Crossover Mutation and Iterative Update: Using simulated binary crossover and multi-point mutation operations, well spacing and depth are iteratively adjusted to optimize target values; S65. Convergence Judgment: Set the maximum number of iterations or the convergence condition of the Pareto front solution, and output a set of optimal well network layouts that balance the trade-offs in recovery rate, net present value and well-controlled reserves.