Efficient FDP Optimization for AI Enhanced Decision Making

Abstract

The present work introduces an efficient workflow for AI-enhanced decision-making in Field Development Planning Optimization. Despite the clear importance of uncertainty quantification in decision-making, we find that constraints in time, hardware, and costs are often limiting factors during field evaluation, with the drawback of having a biased uncertainty description or a wrong risk perception. The proposed work encompasses history matching, solution analysis, and production optimization with special emphasis on reducing both simulation and processing time, maximizing what we can call the result per core hour. At the center of our work is an AI-guided optimizer suited to avoid excessive convergence bias and maintain an optimal exploration vs. exploitation performance. The optimizer allows the integration of a multi-objective (MO) formulation in standard history matching and optimization workflows. Despite the flexibility of MO optimization and the vast literature in the energy industry, its usage in real-field cases has always been quite limited due to its formulation availability in commercial software and the increased computation time. This work will show improvement in solution accuracy and formulation flexibility compared to Single Objective (SO) formulations at no increase in runtime. MO is based on the iterative convergence of an efficient frontier from the results generated by the simulation. This same concept has been brought to a user analysis step to allow the identification of best solutions across multiple evaluation workflows, lowering the expertise level for a solution.