A Soft and Law-Abiding Framework for History Matching and Optimization under Uncertainty

摘要

Current frameworks for optimization and assisted history matching lack the ability to control and guide the sampling engine and to incorporate geo-engineering knowledge. Defining the interactions between uncertain parameters and handling multiple constraints are also arduous tasks. Despite recent advances in adaptive population-based sampling algorithms and other gradient and ensemble-based methods, these specific drawbacks have left engineers with several history-matched models that are inconsistent with the physical and geological knowledge of the field. We introduce a novel rule-based framework based on fuzzy reasoning to integrate engineering knowledge with optimization and assisted history matching workflows. The system can handle multiple complex constraints both in parameter and objective function space. The use of fuzzy set theory in this workflow is a natural way to address uncertainty arising from imprecision of definition. This type of uncertainty is important in expressing the parameters of interest; however, it has been less addressed in existing workflows. The proposed system can be coupled with any algorithm used for assisted history matching, including gradient-based, population-based and particle filter approaches. The framework is coupled with differential evolution algorithm and is tested for three cases. The results show that fuzzy rule- based engine preserves the computational efficiency of the sampling engine, while allowing for definition of flexible rules in history matching and optimization that honor engineering knowledge.