Reservoir-Specific Intelligent Horizontal Well Placement: Productivity Mapping and Application in the Middle East

Horizontal wells have been widely applied in the development of carbonate reservoirs in the Middle East, and optimizing horizontal well placement plays an essential role in achieving effective and economical production from these reservoirs. This study establishes an intelligent horizontal well placement optimization workflow and applies it to a low-permeability carbonate reservoir in the Middle East, aiming to enhance oil recovery and maximize economic benefits. By integrating intelligent optimization algorithms with advanced reservoir numerical simulation, the approach addresses challenges posed by heterogeneity and complex geological characteristics of carbonate formations during well placement optimization. The proposed method is designed to improve oil production efficiency, reduce the number of required wells, and streamline the well placement process to meet long- term production goals. Specifically, the workflow combines a Weighted Mapping of Productivity Potential (WMPP) technique with a Simulated Annealing (SA) algorithm to optimize horizontal well trajectories. WMPP incorporates key reservoir parameters – such as porosity, permeability, oil saturation, and distances to fluid contacts – to compute a productivity potential map across the reservoir. The SA algorithm is employed to determine optimal weighting coefficients for WMPP, ensuring precise identification of high-productivity zones while avoiding areas prone to early water breakthrough or other geological risks. A representative carbonate reservoir model (H-3B) was constructed using geological, petrophysical, and dynamic data to simulate various development scenarios and validate the methodology. The WMPP-guided placement scheme was compared to a conventional oil initially in place (OOIP)-based approach, focusing on production performance, operational efficiency, and economic outcomes. Through application in the low-permeability carbonate reservoir, the study demonstrates the effectiveness of WMPP-guided horizontal well placement optimization in improving reservoir performance. Compared to a traditional OOIP-based placement scheme, the WMPP approach required 7 fewer wells while achieving a 21.74% increase in cumulative oil production over a 20-year production period. The optimization results highlight WMPP's ability to navigate the complex, multimodal objective landscape of well placement in carbonate reservoirs, strategically positioning wells to maximize contact with high-permeability and high-saturation zones while minimizing water breakthrough and drilling costs. This case study underscores the importance of integrating advanced optimization algorithms with reservoir simulation for improved production efficiency and optimal resource utilization. The proposed WMPP+SA framework offers a scalable model for optimizing well placement in complex geological environments and contributes to the state of knowledge in intelligent petroleum engineering.