Automated Infill Well Placement Optimization Using Differential Evolution in TNavigator for Mature Oil Fields
Abstract
Well placement optimization is a critical factor in maximizing hydrocarbon recovery and improving the economic performance of oil and gas field developments, particularly in mature reservoirs where well interference and water production present significant challenges. This study presents an automated framework for infill well placement optimization implemented in tNavigator, integrating reservoir simulation with a Differential Evolution (DE) optimization algorithm to identify optimal well configurations. A customized workflow was developed to optimize the placement of a new infill well (W8) in a mature oil field containing existing producers and injectors. The optimization variables include key well geometry parameters such as surface location, step-out distance, and azimuth, enabling the evaluation of thousands of candidate well configurations. The DE algorithm efficiently explores the high-dimensional and non-linear search space, iteratively updating well locations based on simulation feedback to maximize oil recovery while minimizing adverse interference effects. The optimized well placement demonstrates a 33% increase in cumulative oil production and recovery factor compared to the base development scenario. Additionally, total water production is reduced by approximately 25%, leading to improved operational efficiency and enhanced project economics. The results confirm the robustness of the proposed approach in balancing production enhancement with water control in a complex reservoir environment. This study concludes that automated well placement optimization using evolutionary algorithms within tNavigator offers significant potential for improving recovery from mature oil fields. The proposed framework can be effectively integrated into conventional field redevelopment workflows and provides a foundation for future research into advanced optimization strategies for reservoir management and field development planning.