Navigating Static and Dynamic Uncertainty: Ensemble Modelling for Strategic Field Integrated Redevelopment

Field S, located approximately 150 km offshore Terengganu, Malaysia, is poised for significant redevelopment with up to 30 infill wells planned over five years. To support this expansion and manage subsurface uncertainties, an ensemble-based reservoir modeling workflow was implemented. A comprehensive ensemble of 100 reservoir models was generated to represent key geological and engineering uncertainties, including petrophysics, facies distribution, fluid contacts, and PVT data. These models were dynamically conditioned to historical production data using an ensemble Kalman smoother, allowing iterative history matching and uncertainty reduction, particularly near existing producers. Post-history matching, representative models capturing P10, P50, and P90 cumulative production outcomes were selected to produce probabilistic forecasts under various infill drilling scenarios. This approach provided a robust framework for uncertainty quantification and risk-informed decision-making, supporting economic evaluation and development planning. The workflow is repeatable and scalable, enabling rapid updates as new data emerges. Results demonstrate the practical value of ensemble-based modeling in reducing uncertainty and optimizing field development strategies in complex offshore environments.