A Workflow for Decision Making Under Uncertainty

We propose a workflow for decision making under uncertainty aiming at comparing different development plan scenarios under uncertainty. The approach applies to mature fields where the residual uncertainty is estimated using a probabilstic inversion approach. Moreover a robust optimization method is discussed to optimize controllable parameters in the presence of uncertainty. The key elements of this approach are the use of response surface models to reduce the very high number of simulator model evaluations needed. To build efficient and reliable response surfaces for this application we discuss an experimental design method for correlated input variables where the correlation is induced by the probabilistic inversion process. For the problem of optimization under uncertainty an iterative approach is proposed aiming at refining the response surface iteratively such as to reduce effectively approximation errors and converging faster to the true solution. The workflow is illustrated on a realistic test case of a mature field where the approach is used to compare two new development plan scenarios both in terms of expectation and of risk mitigation and to optimize well position parameters in the presence of uncertainty.