Parameter Inversion of Water Injection-Induced Fractures in Tight Oil Reservoirs Using EDFM & Intelligent Optimization | Advanced Research Insights

 

 Intelligent Optimization Algorithms in Parameter Inversion

Parameter inversion involves estimating unknown reservoir and fracture properties by minimizing the difference between observed and simulated data. Intelligent optimization algorithms, such as genetic algorithms, particle swarm optimization, and machine learning-based methods, enhance this process by efficiently searching complex solution spaces. This research integrates these algorithms with EDFM to achieve precise parameter estimation, reducing uncertainty and improving the predictive capability of reservoir models.

Simulation of Water Injection-Induced Fractures

Water injection is widely used to enhance oil recovery, but it often leads to the formation and propagation of fractures in tight reservoirs. This research investigates the mechanisms of fracture initiation and growth under injection conditions. By combining EDFM with optimization techniques, the study simulates fracture behavior under varying operational parameters, providing insights into fracture dynamics and their impact on reservoir productivity.

Model Validation and Performance Evaluation

To ensure reliability, the developed model is validated against field data and experimental observations. The performance of the parameter inversion framework is evaluated based on its ability to accurately reproduce reservoir behavior. Metrics such as pressure response, production rates, and fracture geometry consistency are analyzed. The results demonstrate that the integration of EDFM with intelligent optimization significantly improves model accuracy and computational efficiency.

#TightOil #FractureModeling #ReservoirEngineering #HydrologyResearch #OptimizationAlgorithms #EnergyResearch