A Novel Method for Production and Injection Well Placement Optimization and Scheduling

Summary Well placement is a critical challenge in oilfield development, strongly influencing hydrocarbon recovery and project economics. With this study, we present a modular and computationally efficient method for well placement that is applicable to both greenfield and mature reservoirs. The approach is an alternative to conventional, computationally intensive optimization and uses 2D quality maps (QMs) representing key reservoir dynamics. Producer wells are positioned using hydrocarbon thickness maps, while injector placement is guided by pressure and permeability maps combined with a graph-based search algorithm that accounts for reservoir connectivity and development constraints that directly impact project economics. The proposed method is applicable to both vertically communicating and noncommunicating reservoirs, and it can be implemented with or without facility layout optimization depending on the study objectives. The method was evaluated on heterogeneous synthetic reservoirs (Olympus Field) using both vertical and horizontal wells. The results show that the injection placement algorithm successfully maintained reservoir pressure, achieving the main objective of waterflooding. In greenfield scenarios, the A* shortest-path algorithm outperformed that of Dijkstra, delivering approximately 6% higher cumulative oil production (COP) and 5% greater net present value (NPV) with vertical wells, while all tested path-search methods performed similarly in mature fields. These findings demonstrate the method’s potential for efficient injector scenario screening and its contribution to improved reservoir management and production optimization. The proposed method dramatically reduces computational requirements compared with traditional optimizers, typically requiring only a single full run, whereas conventional approaches require hundreds of runs, thus making it well suited to well placement within real-field development planning and reservoir-management timeframes.