Closed-Loop Field Development Under Uncertainty by Use of Optimization With Sample Validation

In this work, we develop and apply a general methodology for optimal closed-loop field development (CLFD) under geological uncertainty. CLFD involves three major steps: optimizing the field-development plan on the basis of current geological knowledge; drilling new wells, and collecting hard data and production data; and updating multiple geological models on the basis of all the available data. In the optimization step, the number, type, locations, and controls for new wells (and future controls for existing wells) are optimized with a hybrid particle swarm optimization-mesh adaptive direct search algorithm. The objective here is to maximize expected (over multiple realizations) net present value (NPV) of the overall project. History matching is accomplished with an adjoint-gradient-based randomized maximum likelihood procedure. Because the CLFD history-matching component is fast relative to the optimization component, we generate a relatively large number of history-matched models. Optimization is then performed with a set of representative realizations selected from the full set of history-matched models. We introduce a systematic optimization with sample validation (OSV) procedure, in which the number of realizations used for optimization is increased if an appropriate validation criterion is not satisfied. The CLFD methodology is applied to 2D and 3D example cases. Results show that the use of CLFD increases the NPV for the true (synthetic) model by 10 to 70% relative to that achieved by optimizing over a large number of prior realizations. We also compare the results for CLFD with OSV to results that use a fixed number of geological realizations. These comparisons show that the use of too few realizations in the CLFD optimization step can result in lower true-model NPVs, whereas OSV provides a systematic approach for determining the proper number of realizations.