A workflow for decision making under uncertainty.
Abstract
We propose a workflow for decision making under uncertainty aiming at comparing different field development plan scenarios. The approach applies to mature fields where the residual uncertainty is estimated using a probabilistic inversion approach. Moreover a robust optimization method is discussed to optimize controllable parameters in the presence of uncertainty. The key element of this approach is the use of response surface models to reduce the very high number of simulator model evaluations that are classically needed to perform such workflow. The major issue is to be able to build an efficient and reliable response surfaces which is achieved using a Gaussian process (Kriging) statistical model and using a particular training set (experimental design) developed to take into account the variables correlation induced by the probabilistic inversion process. For the problem of optimization under uncertainty an iterative training set 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.