This study aimed to automatically identify optimal well trajectories that maximize cumulative gas production under specified surface and well constraints. Using a history-matched model provided by Medco Team, Rock Flow Dynamics Indonesia (RFD) developed an automated workflow in the tNavigator Model Designer (MD) module, integrating existing MD calculations with custom Python code. The workflow utilized Assisted History Matching (AHM) with the Particle Swarm Optimization (PSO) algorithm to enhance forecast accuracy. The constraints included surface locations, dogleg severity, inclination, well length, and clearance to the gas-water contact (GWC), with a focus on the Upper North and West Sectors, which contain 24.5 MMSTB of OOIP and 1395 BSCF of OGIP.
The workflow was tested using three optimization methods: sequential, hybrid sequential-concurrent, and fully concurrent well optimization. The sequential method demonstrated superior stability and faster convergence, recommending 15 additional wells with a cumulative gas recovery of 864.8 BSCF (62% recovery factor) by January 2054. These wells maintained a constant production plateau for 165 months. The study recommends further work on hydraulic fracture design, geomechanically coupled simulation, and targeted horizontal drilling to enhance future gas recovery. This project highlights the expertise of Oil and Gas Consultancy in Indonesia and the advanced capabilities of Rock Flow Dynamics Indonesia in optimizing hydrocarbon extraction processes.