Intelligent Horizontal Well Placement Optimization: Modeling and Application – A Case Study of Low Permeability Carbonate Reservoir in the Middle East

Horizontal wells are widely used in the development of carbonate reservoirs in the Middle East, and optimizing their placement is crucial for efficient and cost-effective production. This study focuses on creating an intelligent optimization model for horizontal well placement in a carbonate reservoir, aiming to enhance oil recovery and maximize economic benefits. By integrating intelligent optimization algorithms with advanced reservoir simulations, the study addresses the challenges posed by the heterogeneity and complex geological characteristics of carbonate formations. The proposed workflow combines the Weighted Mapping of Productivity Potential (WMPP) method with the grey wolf optimizer (GWO) to optimize well trajectories. WMPP incorporates key reservoir parameters like porosity, permeability, and fluid saturation to generate productivity maps, while GWO is used to determine the optimal weighting coefficients for WMPP, ensuring precise identification of high-productivity zones and avoiding areas of high water saturation or geological uncertainty. A representative carbonate reservoir model was developed using geological, petrophysical, and dynamic data to validate the methodology. The WMPP-guided placement was compared to conventional oil initially in place (OOIP)-based methods, with a focus on production performance, efficiency, and economic outcomes. The study, applied to a low-permeability carbonate reservoir in the Middle East, demonstrated the effectiveness of WMPP-guided horizontal well placement in improving reservoir performance. Compared to the OOIP approach, the WMPP method required fewer wells while increasing cumulative oil production over a 20-year period. The optimization results highlighted the WMPP technique’s ability to navigate the complex, multimodal objective function landscape of carbonate reservoirs, ensuring maximum reservoir contact with high-permeability zones, minimizing water breakthrough, and reducing drilling costs. This case study illustrates the importance of integrating advanced optimization algorithms with reservoir simulations to enhance production efficiency and resource utilization. Ultimately, this study presents a novel application of WMPP and grey wolf optimizer for optimizing horizontal well placement in carbonate reservoirs, providing a scalable framework applicable to similar reservoirs worldwide and contributing to the advancement of petroleum engineering knowledge.