Robust Optimization Technique Using Modified Net Present Value and Stochastic Simplex Approximate Gradient

Abstract

Summary This article aims to combine, from previous works, a modified objective function and the stochastic simplex approximate gradient (StoSAG) to provide a robust technique that optimizes reservoir production on the basis of a sequence of short-term optimizations. Usually, in reservoir optimization, the main goal is to maximize the net present value (NPV); this work used a modified NPV (MNPV) function that introduces reservoir parameters into the objective function. This MNPV analyzes the relation between cash flow and the reduction of the produced oil fraction, which is an indicator of the reduction of the well production life. On the other hand, the StoSAG is a well-established algorithm for robust optimization, and it was used to perform a constructive optimization with the MNPV cost function. The proposed technique (MNPV), together with StoSAG, is compared with other techniques from the literature using a regular base of all reservoir life cycle and the same proposed short-term optimizations, but using the classical NPV. These comparisons were made based on two benchmarks, SPE9 and Egg reservoir models, with an ensemble of 5 and 100 realizations, respectively. As a result, the MNPV StoSAG presents strong cash flow at the beginning of the reservoir production, a competitive NPV along the entire life cycle, and fast simulation time.