Since the early 2000's there has been a significant focus from many groups around the world towards the development and application of innovative technologies in order to improve reservoir management strategies and optimize field development plans. Benchmark studies are a very valuable way of evaluating and demonstrating the status and potential of developing technology. Numerical optimization is seen as a valuable technology for decision support in various stages of the life cycle of hydrocarbon fields. Its potential has been demonstrated in previous benchmark studies such as the 2008 Brugge study on Closed-Loop Reservoir Management albeit for primarily well control problems. Additionally since the Brugge benchmark exercise also involved history matching it was difficult to separate and thus draw significant conclusions about the performance of the optimization methods. Thus the OLYMPUS optimization benchmark challenge was setup and aimed at field development (FD) optimization under uncertainty. In this talk we will provide an overview of the OLYMPUS case and the optimization problems defined. In addition we aim to provide an anonymized overview of validated results from the participants for the OLYMPUS workshop which takes place the day after ECMOR.
The goal of reservoir management is to make decisions with the objective of maximizing the value creation from the oil or gas production. In order to do this models that preserve geological realism and have predictive capabilities are being developed and used. These models are commonly calibrated by using Assisted History Matching (AHM) methods which, in general, will lead to reduced uncertainty in the predicted production. Although uncertainty assessment and reduction is often an element of high-quality decision making, it is not, in itself, value-creating. Value can only be created through decisions and any decision changes resulting from AHM should be modeled explicitly. Recently there has been a spurt in the application and understanding of Value of Information workflows for reservoir management. In this talk we present a comparison of existing workflows and point out the differences between them. Following this we introduce, a practically driven approach, referred to as Drill and Learn, with elements and concepts from existing workflows to quantify the Value of Learning. The difference and definitions of Value of Information and Value of Learning (VoL) are also presented. Ensemble methods (ES-MDA and StoSAG) are used for the history matching and optimization. The results presented are obtained by applying the proposed Drill and Learn workflow on a realistic synthetic case. Sensitivities to the amount of information obtained before a closed loop exercise is performed are also investigated. We show the benefit of performing the closed loop approach to quantify the value of learning (VoL) to modify field development decisions which leads to a mature robust decision making framework.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.