Robust Production Optimization with Capacitance-Resistance Model as Precursor

Abstract

Many of model-based hydrocarbon production optimization techniques come at significant computational cost, especially for Robust Production Optimization (RO). The Ensemble Based Optimization (EnOpt) as a promising RO method is still computationally expensive when based on rich grid-based reservoir models with hundreds of realizations. We introduce a new workflow where a grid-based model is supplemented by a proxy model which must be both useful and tractable. The Capacitance-Resistance Model (CRM) is potential to be a proxy model for waterflooding systems. We illustrate the use of CRM-based models, and investigate their pros and cons using a synthetic 2D model. The selected proxy model is embedded into the new workflow. The results obtained from the new and traditional workflows are contrasted. The impact of any differences is illustrated by investigating a relevant decision-making context. The main contributions are (1) a general RO workflow embedding proxy models, (2) a discussion of the desiderata of proxy models, and (3) illustration and discussion of using CRM-based models in the new workflow. Based on our study, we conclude that the new workflow, leveraging a faster, but relevant, production model, significantly speeds up the optimization whilst still giving robust results leading to an optimal or near-optimal solution.