Drill and Learn: A Decision-Making Work Flow To Quantify Value of Learning

Remus Hanea, Pierrick Casanova, Lars Hustoft, Reidar Bratvold, Rohith Nair, Christopher William Hewson, Olwijn Leeuwenburgh, Rahul Mark Fonseca

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The goal of reservoir management is to make decisions with the objective of maximizing the value creation from oil or gas production. To achieve this, models that preserve geological realism and have predictive capabilities are being developed and used. These models are commonly calibrated using assisted-history-matching (AHM) methods which, in general, will lead to reduced uncertainty in the predicted production. Although uncertainty assessment and reduction are often elements of high-quality decision making, they are not 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 surge in the application and understanding of value-of-information (VOI) work flows for reservoir management. In this text, we present a comparison of existing work flows and note the differences between them. After this, we introduce a practically driven approach, referred to as “drill and learn,” with elements and concepts from existing work flows to quantify the value of learning (VOL). VOL can be used as a metric to quantify the potential of such work flows and the strategies obtained. Ensemble methods [ensemble smoother with multiple data assimilation (ES-MDA) and stochastic simplex approximate gradient (StoSAG)] are used for the history matching and optimization. The results presented are obtained by applying the proposed drill-and-learn work flow 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 VOL to modify field-development decisions, which leads to a mature and robust decision-making framework.
Original languageEnglish
Article numberSPE-182719-PA
Pages (from-to)1131-1143
Number of pages13
JournalSPE Reservoir Evaluation and Engineering
DOIs
Publication statusPublished - 2018

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