Weighted mapping of productivity potential based on simulated annealing algorithm for well placement optimization

In the realm of reservoir development, the optimization of well placement constitutes a cornerstone challenge with significant implications that directly determine the recovery rate and economic benefits of oil and gas production. This research proposes a novel approach to optimizing well placement in reservoirs by integrating reservoir numerical simulations with intelligent optimization algorithms. The quintessence of this inquiry revolves around the strategic placing of wells amidst the complex geological fabric of reservoirs, where the objective function landscape often manifests with non-smooth, multimodal characteristics. To address those issues, the Weighted Mapping of Productivity Potential (WMPP) technique, fortified by the Simulated Annealing algorithm to judiciously ascertain specific weighting coefficients for the computation of WMPP across reservoirs is introduced in this study. Furthermore, an emblematic carbonate reservoir model serves to corroborate the adaptability and viability of WMPP for well placement optimization, underscoring its efficacy as a swift, economically viable instrument for the delineation of prospective reservoir zones and the guidance of drilling initiatives. The optimization results show that the well placement scheme guided by WMPP, which required 7 fewer wells than the oil initially in place (OOIP)-based scheme, improved 21.74% oil production over the twenty years production period. This comprehensive workflow proffers invaluable insights and benchmarks for the formulation of well placement strategies, with the proposed methodology, in its apparent simplicity, showcasing remarkable efficiency.