Method for Adaptive Optimization of EOR Performance Under Uncertainty

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 173295, “Method for Adaptive Optimization of EOR Performance Under Uncertainty,” by N. Chugunov, T.S. Ramakrishnan, A. Lukyanov, and W.J. Bailey, Schlumberger, and D. Wilkinson, Efficient Solutions, prepared for the 2015 SPE Reservoir Simulation Symposium, Houston, 23–25 February. The paper has not been peer reviewed. Enhanced-oil-recovery (EOR) performance may be assessed quantitatively through a variety of metrics. Because formation and fluid properties are often poorly characterized, however, performance predictions of EOR processes are uncertain. The authors present a method to reduce such uncertainty in EOR performance adaptively while identifying an optimal operational strategy for a given tolerance to risk. The proposed approach allows one to reduce uncertainty progressively in the predicted performance of an iteratively optimized EOR strategy by systematic reduction of uncertainty in identified properties of the reservoir. Introduction Classic approaches to optimization under uncertainty use a mean-variance approach. However, they do not provide necessary insight into the underlying nature of the uncertainties inherent in the optimized model. More importantly, they do not provide any specific quantitative guidance on reducing such uncertainty, which is necessary from an operational point of view. This issue is addressed in this study directly by combining global sensitivity analysis (GSA) with optimization under uncertainty, in an adaptive work flow, for systematic uncertainty reduction of the optimized model prediction. Application of GSA to address various problems arising in the industry has been discussed in a number of studies. However, these studies focused mainly on quantifying uncertainty for specific physical quantities and using that analysis to gain insight into the subsequent measurement-program design and interpretation.