A Comprehensive Review of Advancements in AI-Based Techniques for Field Development Optimization

Abstract

Optimizing well placement and production strategy in hydrocarbon reservoirs is a critical task during field development planning. Various optimization algorithms have been proposed in the literature to optimize different field development problems. Recent research in this area has shifted toward using artificial intelligence (AI) to assist field development optimization in an attempt to establish approaches that are more effective. This paper presents a comprehensive review of recent research on AI-based optimization techniques applied to field development, focusing on studies published in the last ten years. We identified the commonly adopted AI algorithms such as artificial neural networks, gradient boosting, random forest, and clustering. We discussed their specific applications in field development optimization and how they are combined with the classical optimization algorithms such as genetic algorithm, differential evolution, and particle swarm optimization.