Enterprise AI Analysis
Research on Intelligent Optimization of Water Injection Scheme for Water-Flooding Reservoirs Based on Improved Genetic Algorithm
This research proposes an improved genetic algorithm (IGA) for the intelligent optimization of water injection schemes in water-flooding reservoirs. By integrating multi-objective indicators, adaptive mutation, and local search strategies, the IGA significantly enhances oil recovery, energy utilization, and waterflood front balance, providing a robust solution for complex reservoir development.
Executive Impact
Our analysis highlights the critical advancements achieved by the Improved Genetic Algorithm (IGA) in optimizing water injection for water-flooding reservoirs. The following metrics demonstrate its superior performance compared to traditional methods and benchmark schemes.
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Oil Recovery Rate Improvement
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Energy Utilization Rate Improvement
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Waterflood Front Balance Improvement
Deep Analysis & Enterprise Applications
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Addressing Suboptimal Water-Flooding Reservoir Management
Water-flooding reservoirs suffer from low injection-production parameter matching and poor development effects. Traditional optimization models often focus on a single objective, failing to capture the dynamic, multi-physical global characteristics of the injection-production system. This leads to issues like water channeling and low sweep efficiency, hindering crude oil recovery and economic efficiency. Complex reservoir environments with high-dimensional variables and non-linear interactions exacerbate these challenges.
Intelligent Optimization with an Improved Genetic Algorithm
This research introduces an improved genetic algorithm (IGA) for optimizing water injection schemes. IGA integrates a multi-objective fitness function (considering oil recovery, energy utilization, waterflood front balance, and injection-production ratio), an adaptive mutation rate mechanism (based on population genetic diversity and water cut growth rate), and local search strategies (BFGS quasi-Newton method). This approach enhances global search, improves convergence speed, and avoids local optima, making it robust for complex reservoir environments.
Enterprise Process Flow
Initial Population Generation (Real-Number Encoding)
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Fitness Evaluation (Oil Recovery Calculation)
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Elite Preservation (Top 10% Retained)
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Selection Operation (Roulette Wheel)
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Crossover Operation (Adaptive Crossover Rate)
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Mutation Operation (Adaptive Mutation Rate)
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Check Termination Condition
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If No Improvement: Trigger Local Search (Gradient Fine-Tuning)
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Output Optimal Solution
+6.4%
Increase in Oil Recovery Rate by IGA
+8.9%
Improvement in Energy Utilization Rate by IGA
+0.15
Improvement in Waterflood Front Balance by IGA
| Algorithm | Oil Recovery Rate (%) | Monthly Water Cut Rise Rate (%) | Energy Utilization Rate (%) | Waterflood Front Balance Coefficient | Computation Time (h) |
|---|---|---|---|---|---|
| SGA | 45.2 | 1.8 | 70.5 | 0.72 | 12.5 |
| PSO | 46.8 | 1.6 | 71.3 | 0.75 | 10.8 |
| IGA | 48.7 | 1.3 | 77.6 | 0.84 | 9.2 |
| Benchmark Scheme | 42.3 | 2.5 | 68.7 | 0.69 | / |
Estimate Your Potential ROI
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Estimated Annual Savings
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Annual Hours Reclaimed
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Phase 04: Full-Scale Rollout & Optimization
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