Response Surface Approaches for Large Decision Trees: Decision Making Under Uncertainty

Traditionally, the connection between simulation and decision analysis is done by using simulation outputs as inputs to decision algorithms. We propose to use simulation input uncertainties directly in decision algorithms by extending existing probabilistic reservoir simulation tools (experimental design, proxy models), and existing decision analysis tools (decision trees, Pareto fronts). This approach addresses questions on field development options under uncertainty (facility sizing, completion decisions or data collection campaigns). When linking probabilistic simulation with decision analysis, three practical problems arose. First, the number of reservoir uncertainties creates huge decision trees. We solve this problem by creating composite solutions, with some branches evaluated exhaustively, and others evaluated with calibrated response surfaces. Then, assumption of independence between uncertainties, often encountered, was too restrictive for practical uses. We thus specify probabilities on all uncertainty branches. Last, we must handle multiple decision drivers and understand the consequences of decisions on several metrics. We have therefore implemented multi-objective optimization capabilities. The technique developed here extends beyond the capability of existing decision analysis and uncertainty quantification tools. Its practical value is demonstrated on two field problems, and proves useful to identify optimal decision paths.