Perforation water control adaptive adjustment method and management system

Abstract

The invention belongs to the technical field of oil and gas field development, and particularly relates to an adaptive adjustment method and management system for perforation water control, and the method comprises the steps: obtaining oil layer geological parameters, fluid properties and water invasion condition data; constructing a dynamic model according to the oil reservoir data, performing analysis according to the dynamic model and the water invasion prediction model, and determining a water invasion risk level according to an analysis result in combination with a dynamic weight distribution algorithm; according to the water invasion risk level and the mining target, perforation parameters are optimized in combination with a genetic algorithm, optimized perforation parameters are obtained, and according to the optimized perforation parameters, a water invasion path is simulated in combination with environmental parameters and an ant colony foraging algorithm, and a corresponding water prevention and control strategy is formulated; and according to the water control strategy, monitoring the dynamic change of the oil layer and the water control effect, adjusting parameters and strategies in time, feeding back data, and updating the water control strategy knowledge base. Therefore, the problems of lagging risk assessment, extensive perforation parameters, single prevention and control strategy, lack of closed-loop feedback and the like in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas field development, and particularly relates to a method for adaptively adjusting perforation for water control and a management system therefor. Background Art

[0002] With the development of intelligent mining technology, in oil and gas field development, perforation operation, as a key link connecting the oil reservoir and the wellbore, its parameter design directly affects the water invasion risk and the mining efficiency. Currently, the geological conditions of oil reservoirs are complex, and the fluid properties and water invasion dynamics show strong non-linear characteristics, which pose extremely high requirements for the adaptability of perforation parameters and the accuracy of water invasion prevention and control strategies. There is an urgent need for an intelligent adjustment method that can integrate oil reservoir geological parameters, fluid properties, water invasion historical data, and real-time dynamic monitoring information. Through dynamic modeling driven by multi-source data, intelligent algorithm optimization, and a closed-loop feedback mechanism, the dynamic optimization of perforation parameters and the active prevention and control of water invasion risk are realized to meet the dual requirements of efficient development and safe operation under complex working conditions.

[0003] However, traditional technologies mainly rely on static geological models and empirical parameter design, suffering from problems such as lagging risk assessment, rough perforation parameters, single prevention and control strategies, and lack of closed-loop feedback. As a result, water invasion accidents occur frequently, the treatment cost surges, resource development is inefficient, and safety risks accumulate. In complex scenarios, the water invasion accident rate is more than 40% higher than that of intelligent methods, and the treatment cost increases by 20%-30%, making it difficult to meet the requirements of efficient and safe mining. Summary of the Invention

[0004] This application provides a method for adaptively adjusting perforation for water control and a management system therefor to solve the problems of lagging risk assessment, rough perforation parameters, single prevention and control strategies, and lack of closed-loop feedback in the prior art.

[0005] In a first aspect embodiment of this application, a method for adaptively adjusting perforation for water control is provided, including the following steps: obtaining geological parameters, fluid properties, and water invasion situation data of an oil reservoir; constructing a dynamic mirror model of the oil reservoir according to the geological parameters, fluid properties, and water invasion situation data of the oil reservoir, analyzing according to the dynamic mirror model of the oil reservoir and a water invasion prediction model, and determining the water invasion risk level of the oil reservoir according to the result of the analysis in combination with a dynamic weight allocation algorithm; dynamically optimizing perforation parameters according to the water invasion risk level and mining objectives in combination with a genetic algorithm to obtain optimized perforation parameters, and simulating a water invasion path according to the optimized perforation parameters in combination with environmental parameters and an ant colony foraging algorithm to formulate corresponding water control strategies; according to the water control strategies, monitoring the dynamic changes of the oil reservoir and the water control effect in real time, timely adjusting the parameters and the water control strategies, and at the same time, performing data feedback to update the water control strategy knowledge base.

[0006] Preferably, the analysis based on the dynamic mirror model and water invasion prediction model of the oil reservoir includes: obtaining formation structure data, fracture and fault data, and water layer distribution data; extracting key features according to the formation structure data, the fracture and fault data, and the water layer distribution data, and constructing a heterogeneous feature matrix; and mining fracture associations, analyzing data uncertainty, and accurately judging the water invasion risk in complex scenarios based on the heterogeneous feature matrix.

[0007] Preferably, the formula of the dynamic model of the oil reservoir is:

[0008] where is the oil well production; K is the formation permeability; h is the thickness of the oil reservoir; Δp is the pressure difference; μ is the fluid viscosity; re is the supply boundary radius; rw is the oil well radius; is the natural logarithm.

[0009] Preferably, the formula of the water invasion prediction model is:

[0010] where is the water invasion volume; is the cumulative gas production; is the coefficient of formation volume factor of natural gas; is the water invasion coefficient; is the coefficient of formation volume factor of formation water.

[0011] Preferably, according to the results of the above analysis, combined with the dynamic weight allocation algorithm, determine the water invasion risk level of the oil reservoir, including: obtaining geological factor data, engineering factor data, and production factor data; performing dynamic weight allocation of the data according to the geological factor data, the engineering factor data, and the production factor data to obtain risk level evaluation data; and determining the water invasion risk level of the oil reservoir according to the risk level evaluation data, and generating a differential plugging strategy.

[0012] Preferably, according to the water invasion risk level and the production target, dynamically optimize the perforation parameters by combining the genetic algorithm to obtain the optimized perforation parameters, including: obtaining the genetic algorithm coding rule; generating a constrained initial chromosome coding according to the water invasion risk level and the production target, in combination with the genetic algorithm coding rule; inputting the constrained initial chromosome coding into a dynamic perforation responder; and optimizing the parameters according to the genetic algorithm in the dynamic perforation responder to generate the optimized perforation parameters.

[0013] Preferably, according to the optimized perforation parameters, combined with environmental parameters and the ant colony foraging algorithm, simulate the water invasion path and formulate corresponding water control strategies, including: obtaining perforation historical data; identifying the laws and characteristics of water invasion according to the perforation historical data; constructing a water invasion simulation environment according to the laws and characteristics of water invasion, combined with the optimized perforation parameters and the ant colony foraging algorithm; formulating the water control plan according to the water invasion simulation environment and intelligently regulating the perforation strategy.

[0014] The second aspect of the embodiments of the present application provides a perforation water control adaptive adjustment management system, including: an acquisition module, configured to acquire physical parameter data and dynamic circuit simulation data of a new energy vehicle high-voltage electrical system; a construction module, configured to construct a high-precision 3D model according to the physical parameter data of the new energy vehicle high-voltage electrical system, input the dynamic circuit simulation data of the new energy vehicle into the high-precision 3D model, and generate an interactive principle annotation and a particle special effect animation; an interaction module, configured to obtain the circuit state change in the particle special effect animation based on a gesture recognition model and a new energy vehicle VR interaction technology, and display the principle and signal flow of the new energy vehicle high-voltage system according to the circuit state change; a recording module, configured to construct a full-process safety practical simulation training scenario according to the principle and signal flow data of the new energy vehicle high-voltage system, and record the operation data of the trainees according to the full-process safety practical simulation training scenario; an evaluation module, configured to train an AI evaluation model according to the operation data of the trainees to obtain an evaluation report, adjust the interactive feedback parameters of the training scenario according to the evaluation report, and generate a personalized learning report for the trainees at the same time.

[0015] Therefore, the present application includes the following beneficial effects: The embodiments of the present application construct an oil reservoir dynamic mirror model through multi-dimensional data acquisition, accurately evaluate the water invasion risk level in combination with the dynamic weight algorithm, intelligently optimize the perforation parameters by using the genetic algorithm, simulate the water invasion path with the help of the ant colony algorithm to formulate targeted prevention and control strategies, and realize the dynamic adjustment of the prevention and control strategies and the iteration of the knowledge base relying on real-time monitoring and data feedback. This technical path integrating data-driven modeling, intelligent algorithm optimization and closed-loop feedback mechanism improves the accuracy of water invasion risk identification and the scientificity of production parameter optimization. Through the accurate simulation of the water invasion path, targeted prevention and control strategies can be formulated, and real-time dynamic regulation can be carried out to form a self-optimizing prevention and control system, providing full-process intelligent risk control for efficient oil reservoir exploitation and enhancing the accuracy and adaptive ability of water invasion prevention and control. Thus, the problems of lagging risk assessment, rough perforation parameters, single prevention and control strategy and lack of closed-loop feedback in the prior art are solved.

[0016] The additional aspects and advantages of the present application will be partly given in the following description, partly will become obvious from the following description, or will be understood through the practice of the present application. Description of the Drawings

[0017] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of embodiments in conjunction with the accompanying drawings, where: Figure 1 FIG. is a flowchart of a perforation water control adaptation adjustment method provided according to an embodiment of the present application; Figure 2 FIG. is an example diagram of a BaraLogix DRU system provided according to an embodiment of the present application; Figure 3 FIG. is an example diagram of a governance method for extra-high water cut reservoirs in the Changyuan old area provided according to an embodiment of the present application; Figure 4 FIG. is a flowchart of a perforation water control adaptation adjustment method provided according to an embodiment of the present application; Figure 5 FIG. is a structural schematic diagram of a perforation water control adaptation adjustment method provided according to an embodiment of the present application; Figure 6 FIG. is a schematic diagram of a silicon-based intelligent eye inversion module provided according to an embodiment of the present application; Figure 7 FIG. is a schematic diagram of a pressure domain stability control central module provided according to an embodiment of the present application; Figure 8 FIG. is a schematic diagram of a water vein prophet deduction module provided according to an embodiment of the present application; Figure 9 FIG. is a schematic diagram of a well domain breakthrough disposal module provided according to an embodiment of the present application; Figure 10 FIG. is a schematic diagram of a reservoir think tank process engine provided according to an embodiment of the present application; Figure 11 FIG. is a schematic diagram of a full life cycle data platform provided according to an embodiment of the present application. Detailed Embodiments

[0018] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application.

[0019] The following describes the perforation water control adaptive adjustment method and management system according to the embodiments of the present application with reference to the accompanying drawings. In view of the problem of lagging risk assessment mentioned in the above background art, the present application provides a perforation water control adaptive adjustment method. In this method, a dynamic mirror model of the oil reservoir is constructed through multi-dimensional data acquisition, the water invasion risk level is accurately evaluated by combining a dynamic weight algorithm, the perforation parameters are intelligently optimized by using a genetic algorithm, and the water invasion path is simulated by using an ant colony algorithm to formulate targeted prevention and control strategies. At the same time, the dynamic adjustment of the prevention and control strategies and the iteration of the knowledge base are realized relying on real-time monitoring and data feedback. This technical path integrating data-driven modeling, intelligent algorithm optimization and closed-loop feedback mechanism improves the accuracy of water invasion risk identification and the scientific nature of production parameter optimization. Through the accurate simulation of the water invasion path, targeted formulation of prevention and control strategies can be achieved, and a self-optimizing prevention and control system can be dynamically regulated in real time, providing full-process intelligent risk control for efficient oil reservoir production and enhancing the accuracy and adaptive ability of water invasion prevention and control. Thus, the problems of lagging risk assessment, rough perforation parameters, single prevention and control strategy and lack of closed-loop feedback in the prior art are solved.

[0020] Specifically, Figure 1 FIG. is a schematic flow chart of the perforation water control adaptive adjustment method provided by the embodiments of the present application.

[0021] As Figure 1 shown, the perforation water control adaptive adjustment method includes the following steps: In step S101, geological parameters, fluid properties and water invasion situation data of the oil reservoir are obtained.

[0022] Among them, the fluid properties refer to the physical and chemical characteristics of the fluid such as density, viscosity, compressibility, surface tension, etc.

[0023] It can be understood that the embodiments of the present application provide key basic data for accurately evaluating the water invasion risk of the oil reservoir, optimizing production parameters and formulating targeted prevention and control strategies by obtaining fluid properties, improving the accuracy of analysis and decision-making, and conforming to the actual flow characteristics of the fluid.

[0024] For example, as Figure 2 shown, in the drilling engineering, fluid property data is collected in real time through the BaraLogix DRU system. The density of the drilling fluid is detected once per minute, and the rheological properties (such as viscosity, shear stress) are analyzed once every 15 minutes. The real-time data is dynamically associated with the downhole working conditions through the InSite data platform. If a sudden drop in the density of the drilling fluid or abnormal viscosity is detected, the system will immediately give an alarm and trigger parameter adjustment to avoid risks such as wellbore collapse or blowout caused by fluid imbalance. By continuously monitoring the change trend of fluid properties, the operator can optimize the drilling fluid formula and injection parameters, reducing the non-operation time by more than 20% and reducing the accident rate under complex well conditions.

[0025] In step S102, a dynamic mirror model of the oil reservoir is constructed based on the geological parameters, fluid properties, and water invasion data of the oil reservoir. Analyses are performed according to the dynamic mirror model of the oil reservoir and the water invasion prediction model. Based on the analysis results and combined with the dynamic weight allocation algorithm, the water invasion risk level of the oil reservoir is determined.

[0026] Among them, the dynamic weight allocation algorithm is an algorithm that dynamically adjusts the weights of various factors according to real-time data, task requirements, or environmental changes to optimize decision-making, resource allocation, or model performance.

[0027] It can be understood that in the embodiments of the present application, by capturing the dynamic change characteristics of multi-dimensional data such as geological parameters, fluid properties, and water invasion conditions in real time, the weights of various factors are automatically adjusted, making the water invasion risk assessment more in line with the current state of the oil reservoir and improving the timeliness and accuracy of risk assessment.

[0028] In the embodiments of the present application, the analysis according to the dynamic mirror model of the oil reservoir and the water invasion prediction model includes: obtaining formation structure data, fracture and fault data, and water layer distribution data; extracting key features according to the formation structure data, fracture and fault data, and water layer distribution data to construct a heterogeneous feature matrix; and based on the heterogeneous feature matrix, mining fracture associations, analyzing data uncertainty, and accurately judging the water invasion risk in complex scenarios.

[0029] Among them, the heterogeneous feature matrix is a matrix used to characterize the non-uniformity of the feature parameters of an object in spatial distribution or structure, and its elements usually reflect the differential values of features in different positions or dimensions.

[0030] It can be understood that in the embodiments of the present application, by integrating multi-source data such as formation structure, fractures and faults, and water layer distribution, the spatial heterogeneity of the oil reservoir is transformed into quantifiable matrix elements, accurately capturing the differences in feature parameters at different positions, and providing a structured analysis basis for mining the connectivity of the fracture network, identifying preferential water invasion channels, and quantifying data uncertainty.

[0031] For example, in the application of offshore oilfields, through the heterogeneous feature matrix, micro-fracture groups and water layer connection paths that are not recognized by traditional models are mined, guiding the adjustment of the injection-production well pattern layout, increasing the water flooding controlled reserves by 18% and slowing down the rising rate of water cut by 25%, enhancing the development efficiency and risk controllability of complex heterogeneous oil reservoirs.

[0032] In the embodiments of the present application, the formula of the dynamic model of the oil reservoir is:

[0033] Among them, is the oil well production; K is the formation permeability; h is the oil reservoir thickness; Δp is the pressure difference; μ is the fluid viscosity; re is the supply boundary radius; rw is the oil well radius; is the natural logarithm.

[0034] It can be understood that in the embodiments of the present application, by integrating multi-dimensional data such as geological parameters, fluid properties, and water invasion conditions, a digital mirror that real-time maps the true state of the oil reservoir is constructed, which can accurately capture the dynamic evolution laws of the formation structure, fluid migration, and water invasion characteristics, providing a dynamic analysis basis for water invasion risk assessment.

[0035] For example, by integrating distributed fiber optic sensing technology and AI algorithms, the temperature, pressure, and fluid migration data of the oil reservoir are monitored in real time, the change laws of the oil-water interface are accurately identified, and the injection-production plan is optimized. As a result, the water cut of the oil wells in the test area has decreased by 0.07 percentage points, and the cumulative oil production has exceeded 2.8×10 8 cubic meters. At the same time, the water absorption thickness ratio has been increased by 1 percentage point through intelligent separate injection technology, improving the development efficiency of old oilfields.

[0036] In the embodiments of the present application, the formula of the water invasion prediction model is:

[0037] where, is the water invasion volume; is the cumulative gas production; is the natural gas volume coefficient; is the water invasion coefficient; is the formation water volume coefficient.

[0038] It can be understood that in the embodiments of the present application, by constructing a dynamic prediction system, the evolution laws such as water invasion paths and rates are accurately captured, the water invasion risks at different stages are quantified, and the trends of high-risk areas are early warned, providing a basis for perforation optimization, injection-production adjustment, etc.

[0039] For example, by real-time monitoring the production data of gas wells and combining multi-attribute fusion geological body carving technology, the water invasion hidden dangers in small reef bodies are accurately identified, and differential prevention and control strategies are formulated. The annual water production is controlled below 250,000 cubic meters, the annual gas production of the gas field is stably maintained above 34 billion cubic meters, and the cumulative gas production increase exceeds 130 billion cubic meters, successfully extending the stable production period to 2028.

[0040] In the embodiments of the present application, according to the analysis results, combined with the dynamic weight distribution algorithm, the water invasion risk level of the oil reservoir is determined, including: obtaining geological factor data, engineering factor data, and production factor data; performing dynamic weight distribution of the data according to the geological factor data, engineering factor data, and production factor data to obtain risk level evaluation data; determining the water invasion risk level of the oil reservoir according to the risk level evaluation data, and generating a differential plugging strategy.

[0041] Among them, the differential plugging strategy is to formulate a personalized plan for the selection of plugging materials, injection processes, and construction parameters according to the geological characteristics, fluid properties, and water invasion degrees of different water invasion scenarios or regions in the oil reservoir.

[0042] It can be understood that in the embodiments of this application, by dynamically analyzing geological, engineering and other data, personalized plans are matched according to the characteristics of different water invasion regions, solving the adaptability problem of unified plugging, improving the physical and chemical matching degree between the plugging material and the target region, and reducing the consumption of ineffective plugging materials.

[0043] For example, in the development of the Tahe fracture-vug reservoir, by dynamically analyzing the reservoir heterogeneity, the water invasion regions are divided into three types: high-permeability channeling type, medium-permeability bypassing type, and low-permeability slow-invasion type; for the high-permeability channeling region, a composite plugging agent of "high-strength fiber + fast-setting resin" is used in combination with a large-displacement pulse injection process to quickly plug the main water flow channel; in the medium-permeability bypassing region, a plugging agent of "nano-gel + deformable particles" is used, and pore-throat level plugging is achieved by controlling the injection rate; in the low-permeability slow-invasion region, a plugging agent of "biopolymer + surfactant" is used, and the injection with low shear is used to improve the seepage resistance, increasing the single-well water shutoff efficiency to 88%, reducing the operation cost by 25%, decreasing the water cut in the test area by 12 percentage points, and increasing the cumulative oil production by more than 500,000 tons.

[0044] In step S103, according to the water invasion risk level and the production target, the perforation parameters are dynamically optimized by combining the genetic algorithm to obtain the optimized perforation parameters. According to the optimized perforation parameters, combined with the environmental parameters and the ant colony foraging algorithm, the water invasion path is simulated, and the corresponding water control strategy is formulated.

[0045] Among them, the genetic algorithm is an adaptive heuristic algorithm that efficiently searches and optimizes the solution space of the problem by simulating mechanisms such as natural selection, crossover, and mutation in biological evolution.

[0046] It can be understood that in the embodiments of this application, by simulating the biological evolution mechanism, efficient solutions for multiple objectives and multiple constraints are realized in the optimization of perforation parameters, quickly traversing parameter combinations such as perforation density and aperture, breaking through the limitations of traditional empirical design, ensuring that the parameters meet the differential requirements such as high-permeability fracture control and low-permeability production increase, and avoiding water invasion risks.

[0047] In the embodiments of this application, according to the water invasion risk level and the production target, the perforation parameters are dynamically optimized by combining the genetic algorithm to obtain the optimized perforation parameters, including: obtaining the genetic algorithm coding rule; generating a constrained initial chromosome coding according to the water invasion risk level and the production target, in combination with the genetic algorithm coding rule; inputting the constrained initial chromosome coding into the dynamic perforation responder; and optimizing the parameters according to the genetic algorithm in the dynamic perforation responder to generate the optimized perforation parameters.

[0048] Among them, the genetic algorithm coding rule is a mapping method that converts the problem solution into a chromosome structure that can be processed by the genetic algorithm, providing basic data for genetic operations.

[0049] It can be understood that in the embodiments of the present application, by mapping the perforation parameters and constraint conditions into an operable chromosome structure, it is ensured that the initial solution conforms to the engineering reality, reducing invalid parameter combinations, improving the algorithm convergence efficiency, and making the optimized perforation parameters technically feasible.

[0050] For example, in the optimization of perforation parameters in a low-permeability oil reservoir in the Bohai Sea, the real-number coding rule is used to map parameters such as perforation density, aperture, and phase angle into a three-dimensional real-number chromosome vector, and geological constraints and production capacity targets are embedded. By encoding the initial solution space into a chromosome group that conforms to engineering rules, the genetic algorithm focuses on the feasible solution region during the iteration process, and the efficiency of the crossover and mutation operations is increased by 40%. After 200 iterations, the optimized parameters increase the single-well production capacity in the test area by 22% compared with the traditional empirical design, and at the same time reduce the water invasion events caused by excessive perforation in high-risk areas by 35%.

[0051] In the embodiments of the present application, according to the optimized perforation parameters, combined with environmental parameters and the ant colony foraging algorithm, the water invasion path is simulated, and corresponding water control strategies are formulated, including: obtaining perforation historical data; identifying the laws and characteristics of water invasion occurrence according to the perforation historical data; constructing a water invasion simulation environment according to the laws and characteristics of water invasion occurrence, combined with the optimized perforation parameters and the ant colony foraging algorithm; formulating a water control plan according to the water invasion simulation environment, and intelligently regulating the perforation strategy.

[0052] Among them, the ant colony foraging algorithm is a bionic optimization algorithm that simulates ants' communication and cooperation through pheromones to find the shortest path to food, and realizes the efficient solution of complex problems through indirect communication and cooperation among individuals.

[0053] It can be understood that in the embodiments of the present application, by simulating the principle of ants' cooperation in finding paths, parallel search and dynamic adjustment of path weights are carried out in the water invasion path simulation, efficiently identifying the dominant water invasion channels in complex oil layers, quickly adapting to changes in formation parameters, and accurately depicting the dynamic evolution of the water invasion path.

[0054] For example, as Figure 3As shown in the figure, in the treatment of extra-high water cut reservoirs in the Changyuan old area, the ant colony foraging algorithm is introduced to simulate the water invasion path. By simulating the pheromone secretion and path selection behavior of ants, parameters such as permeability, pressure field, and fluid viscosity are converted into the intensity of "virtual pheromones", and the water invasion paths of more than 3,000 injection-production units are searched in parallel. For the complex scenario where high-permeability strips and low-permeability matrices are intertwined, the algorithm automatically identifies the main water invasion channels accounting for 15%, and predicts the channel evolution trend based on the dynamic update mechanism of pheromones. Based on the simulation results, the oilfield implements precise plugging of the main channels with "high-strength granular plugging agent + pulsed injection", and adopts "flexible gel + steady-state displacement" for differential treatment of secondary channels, reducing the water cut rising rate in the test area by 0.8 percentage points, increasing the effective rate of water plugging operations from 70% to 89%, and reducing the average operation cost per well by 25%.

[0055] In step S104, according to the water control strategy, the dynamic changes of the oil reservoir and the water control effect are monitored in real time, the parameters and the water control strategy are adjusted in time, and at the same time, data feedback is carried out to update the water control strategy knowledge base.

[0056] Among them, the water control strategy is a systematic plan to prevent and control water source threats such as groundwater and surface water through scientific planning and technical means for water damage problems that may be faced in various engineering constructions, so as to ensure project safety and environmental stability.

[0057] It can be understood that in the embodiment of the present application, by monitoring the oil reservoir dynamics and water control effect in real time, the dynamic adjustment of parameters and strategies and data feedback update are realized, accurately coping with the water damage threats in engineering construction, timely resolving water source risks such as groundwater and surface water, ensuring project safety and environmental stability, and at the same time continuously optimizing the strategy knowledge base to improve the scientificity and long-term effectiveness of water damage prevention and control.

[0058] According to the perforation water control adaptive adjustment method proposed in the embodiment of the present application, a dynamic mirror model of the oil reservoir is constructed through multi-dimensional data acquisition, the water invasion risk level is accurately evaluated by combining the dynamic weight algorithm, the perforation parameters are intelligently optimized by using the genetic algorithm, and the ant colony algorithm is used to simulate the water invasion path to formulate targeted control strategies. At the same time, relying on real-time monitoring and data feedback, the dynamic adjustment of the control strategy and the iteration of the knowledge base are realized. This technical path that integrates data-driven modeling, intelligent algorithm optimization and closed-loop feedback mechanism improves the accuracy of water invasion risk identification and the scientificity of production parameter optimization. Through the accurate simulation of the water invasion path, the targeted formulation of the control strategy can be realized, and a self-optimizing control system can be dynamically regulated in real time, providing full-process intelligent risk control for the efficient exploitation of the oil reservoir and enhancing the accuracy and adaptive ability of water invasion prevention and control. Thus, the problems of lagging risk assessment, rough perforation parameters, single control strategy and lack of closed-loop feedback in the prior art are solved.

[0059] The following will elaborate on the perforation water control and adaptation adjustment method through a specific embodiment, as Figure 4 shown, including: Step 1: Data acquisition and information integration.

[0060] Through seismic exploration, logging, core experiments, and real-time monitoring, comprehensively collect reservoir geological parameters, fluid properties, and water invasion dynamic data (such as fracture density, oil-water density difference, water production rate, etc.). After removing outliers and dimensionality reduction by principal component analysis, extract the key features of water invasion to provide a data basis for subsequent modeling and analysis.

[0061] Step 2: Build a reservoir dynamic model and risk assessment.

[0062] Use reservoir numerical simulation software to build a three-dimensional geological model, combine machine learning algorithms to train a water invasion prediction model, dynamically allocate the weights of multi-source data through methods such as entropy weight method, quantitatively evaluate the water invasion risk level (low / medium / high risk), and clarify the prevention and control priorities and technical strategies under different risk levels.

[0063] Step 3: Dynamically optimize perforation parameters and formulate strategies.

[0064] Based on the genetic algorithm, optimize parameters such as perforation density, aperture, interval, and phase angle to minimize the water invasion rate and meet the production constraints; use the ant colony foraging algorithm to simulate the water invasion path, identify high-risk water-conducting channels, and specifically design perforation schemes (such as avoiding fractures perforation) and engineering measures (such as injecting gel plugging agents) to form a comprehensive strategy of "parameter optimization + path prevention and control".

[0065] Step 4: Real-time monitoring and strategy iteration.

[0066] Through downhole fiber optic sensors and ground data acquisition, real-time track the dynamic changes of reservoir pressure, water cut, etc., set a water invasion warning threshold (such as the daily increase in water cut > 2%), trigger automatic parameter adjustment and optimization of water plugging measures; synchronously input the water invasion characteristics and the optimal plan into the knowledge base, and continuously update the prediction model through reinforcement learning.

[0067] Step 5: Effect evaluation and continuous optimization.

[0068] Regularly evaluate key indicators such as water production rate, water invasion rate, and production volume to verify the effectiveness of the water control strategy (such as the number of workovers reduced by ≥ 30%); combine new well data to review the effectiveness of the strategy, dynamically adjust the model weights and algorithm parameters, gradually improve the adaptability to complex reservoir water invasion problems, and achieve continuous iterative optimization of the technical solution.

[0069] In summary, the present invention accurately identifies high-risk water intrusion areas through the deep integration of geological, fluid, and water intrusion data. It uses a genetic algorithm and an ant colony foraging algorithm to collaboratively optimize perforation parameters and water intrusion path simulation, ensuring stable production (≥90% of the design value) while controlling the water production rate (fluctuation ≤5%). Combining real-time monitoring with knowledge base iteration reduces the water intrusion rate, the number of well repairs, and the threat of water damage and operating costs. This provides a replicable water intrusion prevention and control template for complex oil reservoirs, thereby improving the safety and economic efficiency of reservoir development.

[0070] Next, the perforation prevention and control water adaptive regulation management system proposed according to the embodiment of the present application will be described with reference to the accompanying drawings.

[0071] Figure 5 It is a block diagram of the perforation prevention and control water adaptation adjustment method according to an embodiment of the present application.

[0072] like Figure 5 As shown, the perforation water prevention and control adaptive regulation management system 10 includes: a silicon-based smart eye inversion module 100, a pressure domain stabilization center module 200, a water vein prophet deduction module 300, a well area breakthrough disposal module 400 and a reservoir think tank process engine 500.

[0073] Among them, the Silicon-based Smart Eye inversion module 100 is used to integrate distributed fiber optic sensing and microseismic monitoring technology to invert wellbore stress and formation fracture parameters in real time; the pressure domain stabilization center module 200 is used to predict, control and dynamically adjust bottomhole pressure, and link the blowout preventer to achieve collaborative management of well control safety; the Water Pulse Prophet deduction module 300 is used to warn of water intrusion risks, and dynamically deduce the wellbore-reservoir fracture expansion and multiphase flow migration process in combination with digital twin technology; the well field breach disposal module 400 is used to integrate technology and dynamically control risk positioning, well control operations and accident response processes; the reservoir think tank process engine 500 is used for digital management and control to generate intelligent collaborative decision-making for the entire life cycle of oil and gas field development.

[0074] It can be understood that in the embodiment of the present application, the silicon-based smart eye inversion module is integrated with the sensing technology to obtain the wellbore stress and formation fracture parameters in real time, providing support for the wellbore stability assessment; the pressure domain stabilization center predicts and adjusts the bottom hole pressure, and links the blowout preventer to ensure well control safety; the water vein prophet deduction module combines with the digital twin to warn of water intrusion risks and deduce multiphase flow migration; the well field breakthrough disposal module integrates technology to dynamically control risk positioning and emergency processes; the reservoir think tank process engine generates full life cycle intelligent decision-making, which solves the problems of risk assessment lag, rough perforation parameters, single prevention and control strategy and lack of closed-loop feedback in the existing technology.

[0075] In the embodiment of the present application, the Si-based Insight inversion module 100 includes: Figure 6 As shown, distributed optical fiber sensing components and microseismic monitoring system.

[0076] Among them, the distributed optical fiber sensing component is used to collect stress, strain and temperature data around the wellbore in real time, and construct a continuous wellbore-formation physical field monitoring network; the microseismic monitoring system is used to capture microseismic signals generated by formation fractures, locate the development position and propagation direction of fractures, and quantify fracture dynamic parameters.

[0077] It can be understood that in the embodiments of the present application, the distributed optical fiber sensing component and the microseismic monitoring system are used to collect wellbore stress, strain, temperature and formation microseismic signals in real time, construct a multi-dimensional monitoring network, accurately locate the fracture position and propagation direction, and quantify dynamic parameters, and perceive the formation physical field and fracture evolution in real time, providing high-density data for wellbore stability evaluation and water invasion early warning, and improving the monitoring accuracy and hidden danger identification ability under complex working conditions.

[0078] In the embodiments of the present application, the pressure domain stability control center module 200 includes: as Figure 7 shown, a dynamic pressure regulation and control module, a bottom hole pressure prediction model, and a blowout preventer linkage management system.

[0079] Among them, the dynamic pressure regulation and control module is used to adjust the wellhead throttle valve, pump injection parameters, etc. in real time to stabilize the bottom hole pressure within a safe range; the bottom hole pressure prediction model is used to predict the change trend of the bottom hole pressure under different working conditions; the blowout preventer linkage management system is used to establish a real-time communication interface between the blowout preventer and the pressure control system, and when the pressure is abnormal, automatically trigger the blowout preventer closing program and synchronously access the well control safety strategy library.

[0080] It can be understood that in the embodiments of the present application, the dynamic pressure regulation and control module, the bottom hole pressure prediction model and the blowout preventer linkage management system are used to collaboratively and intelligently control the bottom hole pressure. The prediction model predicts the pressure trend, the regulation module stabilizes the pressure to a safe range in real time, and the blowout preventer system automatically triggers the closing program and calls the safety strategy in case of abnormality, preventing the risks of blowout and well leakage, improving the automation level and response speed of pressure control, and reducing the manual intervention error.

[0081] In the embodiments of the present application, the water vein prophet deduction module 300 includes: as Figure 8 shown, a water invasion risk early warning engine and a fracture flow and migration coupling simulation module.

[0082] Among them, the water invasion risk early warning engine is used to integrate data such as water cut, pressure gradient, fluid properties, etc., and through the multi-parameter threshold method or machine learning model, real-time early warning of the water invasion risk level; the fracture flow and migration coupling simulation module is used to simulate the fracture propagation process, combined with the finite element method or computational fluid dynamics, to deduce the migration law of multiphase flow in fractures, and predict the water invasion path and breakthrough risk points.

[0083] It can be understood that in the embodiments of the present application, the water invasion risk early warning engine and the fracture flow migration coupling simulation module cooperate to integrate data such as water cut and pressure gradient, and real-time early warning of the water invasion risk level through multi-parameter analysis or machine learning; simulate fracture propagation and deduce the multi-phase flow migration law in combination with fluid mechanics, predict the water invasion path and breakthrough risk points, and reduce the water damage threat and operation cost.

[0084] In the embodiments of the present application, the well domain breakthrough disposal module 400 includes, as Figure 9 shown, a risk positioning and diagnosis system, and an accident response process management system.

[0085] Among them, the risk positioning and diagnosis system is used to accurately locate the specific location of downhole faults and provide an accurate target for emergency disposal; the accident response process management system is used to preset the emergency process and real-time risk assessment, automatically guide the downhole accident disposal operation and optimize the disposal strategy.

[0086] It can be understood that in the embodiments of the present application, the risk positioning and diagnosis system accurately locates the downhole fault location, and the accident response process management system combines the preset process and real-time assessment to automatically guide the disposal and optimize the strategy, shortening the emergency response time, improving the disposal efficiency, and effectively reducing the accident risk and operation loss.

[0087] In the embodiments of the present application, the reservoir think tank process engine 500 includes, as Figure 10 shown, a full-life cycle data center, an intelligent decision-making model library, and a digital control and visualization platform.

[0088] Among them, the full-life cycle data center is used to integrate multi-source data such as geology, engineering, and production, construct an oil and gas field development database through data cleaning and standardization processing, and perform cross-module data sharing; the intelligent decision-making model library is used to generate optimization suggestions for the development plan, continuously iterate the decision-making strategy in combination with reinforcement learning, and improve the full-cycle development efficiency; the digital control and visualization platform is used for real-time monitoring interfaces, multi-department collaborative online collaboration, making the decision-making process transparent and efficient.

[0089] For example, as Figure 11 shown, an oil and gas field integrates data from the entire exploration, development, production, and operation and maintenance chain through the full-life cycle data center, realizing collaborative management of geological modeling, real-time monitoring, and equipment maintenance: in the exploration stage, the data center integrates multi-source data such as geology and geophysical exploration to construct a 3D model and optimize well placement; in the development stage, downhole pressure, flow rate and other parameters are collected in real time to dynamically adjust the production plan; in the production stage, machine learning is used to predict equipment failures, reducing the unplanned downtime by 40%; in the operation and maintenance stage, the asset health status is evaluated based on historical data, extending the equipment service life by 20%.

[0090] The perforation water prevention and control adaptive regulation and management system proposed in the embodiment of the present application obtains wellbore stress and formation fracture parameters in real time through the fusion of silicon-based smart eye inversion module and sensing technology, providing support for wellbore stability assessment; the pressure domain stabilization control center predicts and adjusts the bottom hole pressure, and links the blowout preventer to ensure well control safety; the water vein prophet deduction module combines digital twins to warn of water intrusion risks and deduce multiphase flow migration; the well field breakthrough disposal module integrates technology to dynamically control risk positioning and emergency processes; the reservoir think tank process engine generates full life cycle intelligent decision-making, which solves the problems of lagging risk assessment, extensive perforation parameters, single prevention and control strategy and lack of closed-loop feedback in the existing technology.

[0091] In the description of this specification, reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0092] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0093] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0094] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for adaptively adjusting perforation for water control, characterized in that Including: Obtain geological parameters, fluid properties, and water invasion data of the oil reservoir; Construct a dynamic model of the oil reservoir based on the geological parameters, fluid properties, and water invasion data of the oil reservoir, analyze according to the dynamic model of the oil reservoir and the water invasion prediction model, and determine the water invasion risk level of the oil reservoir according to the results of the analysis and in combination with the dynamic weight allocation algorithm; Dynamically optimize the perforation parameters according to the water invasion risk level and the production target in combination with the genetic algorithm to obtain optimized perforation parameters. According to the optimized perforation parameters, simulate the water invasion path in combination with the environmental parameters and the ant colony foraging algorithm, and formulate corresponding water control strategies; According to the water control strategy, monitor the dynamic changes of the oil reservoir and the water control effect in real time, adjust the parameters and the water control strategy in a timely manner, and at the same time, conduct data feedback to update the water control strategy knowledge base.

2. The perforation water control adaptation adjustment method according to claim 1, characterized in that Analysis according to the dynamic mirror model of the oil reservoir and the water invasion prediction model includes: Obtain formation structure data, fracture and fault data, and water layer distribution data; Extract key features according to the formation structure data, the fracture and fault data, and the water layer distribution data, and construct a heterogeneous feature matrix; According to the heterogeneous feature matrix, excavate fracture associations, analyze data uncertainty, and accurately judge the water invasion risk in complex scenarios.

3. The perforation water control adaptation adjustment method according to claim 2, characterized in that, The formula of the dynamic model of the oil reservoir is: ； Among them, where \(q\) is the oil well production rate; \(K\) is the formation permeability; \(h\) is the oil reservoir thickness; \(\Delta p\) is the pressure difference; \(\mu\) is the fluid viscosity; \(r_e\) is the supply boundary radius; \(r_w\) is the oil well radius; is the natural logarithm.

4. The perforation water control adaptation adjustment method according to claim 2, characterized in that The formula of the water invasion prediction model is: ； wherein, is the water influx volume; is the cumulative gas production; is the natural gas volume factor; is the water influx coefficient; is the formation water volume factor.

5. The perforating water control adaptation adjustment method according to claim 1, wherein Determine the water invasion risk level of the oil reservoir according to the results of the analysis and in combination with the dynamic weight allocation algorithm, including: Obtain geological factor data, engineering factor data, and production factor data; Conduct dynamic weight allocation of data according to the geological factor data, the engineering factor data, and the production factor data to obtain risk level evaluation data; Determine the water invasion risk level of the oil reservoir according to the risk level evaluation data, and generate a differential plugging strategy.

6. The perforation water control adaptation adjustment method according to claim 1, characterized in that Dynamically optimize the perforation parameters according to the water invasion risk level and the production target in combination with the genetic algorithm to obtain optimized perforation parameters, including: Obtain the genetic algorithm coding rule; Generate a constrained initial chromosome coding according to the water invasion risk level and the production target in combination with the genetic algorithm coding rule; Input the constrained initial chromosome coding into the dynamic perforation responder; Optimize the parameters according to the genetic algorithm in the dynamic perforation responder to generate optimized perforation parameters.

7. The perforation water control adaptation adjustment method according to claim 1, characterized in that Simulate the water invasion path according to the optimized perforation parameters in combination with the environmental parameters and the ant colony foraging algorithm, and formulate corresponding water control strategies, including: Obtain perforation historical data; Identify the laws and characteristics of water invasion according to the perforation historical data; Construct a water invasion simulation environment according to the laws and characteristics of water invasion, in combination with the optimized perforation parameters and the ant colony foraging algorithm; Formulate the water control plan according to the water invasion simulation environment, and intelligently regulate the perforation strategy.

8. A perforation water control adaptive adjustment management system, characterized in that, Including: Silicon-based intelligent eye inversion module, pressure domain stability control center module, water vein prophet deduction module, well domain breakthrough disposal module, oil reservoir think tank process engine; among them, The silicon-based smart eye inversion module is used to integrate distributed fiber optic sensing and microseismic monitoring technology to invert wellbore stress and formation fracture parameters in real time; The pressure domain stabilization control central module is used to predict, control and dynamically adjust the bottom hole pressure, and to link the blowout preventer to achieve coordinated management of well control safety; The Water Pulse Prophet deduction module is used to warn of water intrusion risks and dynamically deduce the wellbore-reservoir fracture expansion and multiphase flow migration process using digital twin technology; The well area breach disposal module is used to integrate technologies and dynamically manage risk positioning, well control operations and accident response processes; The reservoir think tank process engine is used for digital management and control, and generates intelligent collaborative decision-making for the entire life cycle of oil and gas field development.

Images