A Systematic Approach to Reservoir Health and Field Development Planning Optimization

Abstract This paper discusses the application of an advanced automated tool for optimizing the performance of Field X, a geologically complex and mature reservoir with multiple faulted and stacked formations. The field faces numerous production challenges due to its complexity and depletion history. The study demonstrates how the tool systematically assesses reservoir performance, identifies inefficiencies, and prepares actionable plans to maximize recovery and extend field life. Three key modules were employed in the analysis of Field X: (i) interwell connectivity analysis and injection optimization, (ii) infill well location optimization, and (iii) reservoir health check analysis. These modules enabled a data-driven approach to diagnosing reservoir behavior and fine-tuning field development. Reduced physics-based models are then used to identify communication pathways between injectors and producers, optimizing water injection patterns. For infill well location optimization, a hybrid model that combines machine learning with reservoir simulations is used to determine well trajectories and the number of wells. Lastly, a comprehensive reservoir health check provided a holistic assessment of reservoir conditions. It highlighted critical areas, including pressure depletion zones, regions with suboptimal recovery, and the effects of historical production strategies. The proposed methods have been implemented in Field X, featuring over 100 wells across three distinct layers to optimize reservoir performance. Firstly, efficient and inefficient injectors were identified, and injection redistributions were adjusted, resulting in a potential increase in recovery. Additionally, two new infill well locations were determined with the potential to add approximately 4.5 MM barrels of oil over the next five years. Finally, the comprehensive reservoir health check provided a detailed analysis of all three layers, offering a clear path for optimizing the performance of existing wells and strategically planning the placement of new infill wells. These insights provide a robust framework for ongoing field development, ensuring sustained productivity and maximum recovery from Field X. By employing these automated modules, the study rapidly identified opportunities to enhance field performance, improve recovery, and sustain reservoir health. This approach reduces the time and manpower required for effective reservoir management, providing a cost-efficient optimizing production potential.