Development optimization of new fields can be improved with integrated quantitative models that account for the technical and economical aspects of hydrocarbon recovery. A challenge in implementation is to understand the potential impact of uncertainty on optimal decision-making. To mitigate the risks and seize the opportunities arising from the uncertainty, the models used in the decision-making process should include a robust capability for stochastic optimization. This paper presents a case study in development optimization of a two-compartment offshore gas field. The analysis focuses on the optimization of facility size, well counts, compression power and production policy. A stochastic programming model is developed to investigate the impact of uncertainties in original gas in place and inter-compartment transmissibility. Reservoir tank equations are used to model pressure and production responses. The reservoir and well equations are coupled with economic and surface facility models. Results of two solution methods, optimization with Monte Carlo sampling and stochastic programming, are analyzed and compared. The models are then used in a value of information (VOI) analysis. The current work is part of an emerging effort in industry to introduce fast and efficient methods for optimizing field development under uncertainty. Computational efficiency is a significant advantage of the proposed approach because it eliminates most constraints on the scope of the uncertainty analysis. The intended applications of this approach are project screening, scenario and uncertainty analyses, including VOI analysis.
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