Aquifer Distribution Identification Using Genetic Algorithm Optimization
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Solution Overview
Problem
Current methods for characterizing aquifer distribution in complex edge-water oil reservoirs are limited, leading to unclear understanding of aquifer characteristics, which hinders targeted water control measures and affects the ultimate recovery factor of oil reservoirs.
Innovation Solution
An intelligent identification method using a numerical simulation model and genetic algorithm to invert aquifer characteristics from single-well geological and production data, automatically correcting aquifer unit volume and cumulative water influx to determine accurate aquifer distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the numerical simulation method is used to simulate water influx dynamic process by establishing an aquifer based on geological data, then the calculation efficiency and cost are improved, but the accuracy of the established aquifer model cannot be fully explained due to measurement errors or lack of geological data
Solution Approach 1:
The patent implements an iterative feedback mechanism where the numerical simulation model is repeatedly adjusted based on comparison between predicted and actual production data. The genetic algorithm uses the fitness function (difference between predicted and actual water cut) to guide parameter optimization, creating a closed-loop feedback system that continuously improves model accuracy until convergence is achieved.
Solution Approach 2:
The patent replaces the traditional manual history matching process with an automated genetic algorithm optimization system. Instead of manually adjusting aquifer parameters based on expert judgment, the system uses computational intelligence to automatically search the parameter space and optimize model accuracy, substituting human mechanical adjustment with algorithmic optimization.
2Adaptability or versatility
If the numerical simulation method is used to simulate water influx dynamic process, then the application scope is widened, but too many parameters need to be adjusted in the history matching process
Solution Approach 1:
The genetic algorithm performs self-service by automatically adjusting aquifer parameters without human intervention. The system independently evaluates the fitness of different parameter combinations, performs selection and crossover operations, and converges to optimal parameters autonomously, eliminating the need for manual parameter tuning and reducing operational complexity.
Solution Approach 2:
The patent transforms the complex parameter adjustment problem into an automated optimization process where parameters are systematically varied according to genetic algorithm operations. The fitness function guides parameter changes toward optimal values, converting the manual trial-and-error parameter adjustment into an automated parameter evolution process.
3Loss of information
If physical model establishing method is used to characterize aquifer distribution, then the water influx process can be visualized, but insurmountable problems exist in realization of a physical model similar to a prototype due to large-scale fluid movement
Solution Approach 1:
The patent creates a virtual copy of the physical aquifer system through numerical simulation. Instead of building a physical scale model, the system develops a computational representation that replicates water influx dynamics, allowing visualization and analysis without the complexity and limitations of physical modeling.
Solution Approach 2:
The patent substitutes the physical mechanical model with a numerical computational model. The complex large-scale fluid movement that cannot be physically replicated is instead simulated through mathematical equations and computational algorithms, replacing physical constraints with computational capabilities.
Data Source
AI summary
An intelligent identification method of aquifer distribution in complex edge-water oil and gas reservoirs is provided. The problem, that targeted water control countermeasures cannot be proposed due to unclear identification of edge-water distribution in complex edge-water reservoirs, is resolved. Based on geological data and production data of a single well, establish a numerical simulation model of a water influx unit for simulating inflow dynamic of complex edge-water influx; by a genetic algorithm, correct characteristic parameters of an aquifer unit, including volume and water influx of the aquifer unit; automatically fit dynamic production data calculated by the model with actual dynamic production data, to obtain optimal characteristic parameters of the aquifer unit; and assign the characteristic parameters to the aquifer unit for inversion to determine aquifer distribution. The method has simple steps and accuracy by comparing the result of aquifer distribution inversion and that of numerical simulator.