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

Abstract

Summary 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.

Probability elicitation using geostatistics in hydrocarbon exploration

“…Future work will extend the approach discussed in this paper beyond the exploration phase of hydrocarbon fields where, traditionally, the decisions are embedded in the modeling workflow through optimization procedures [39,41,42]. An extension of the probabilistic approach presented here has the potential to improve decision-making during the development phase because the decisions will benefit from significantly better data support and the large number of reservoir simulations aimed to generate probabilistic production forecasts under geological uncertainty.…”

Real Options Approach for a Staged Field Development With Optional Wells

“…In general, accounting for this information can help optimize the well trajectory and increase the economic value. Hanea et al (2019) assess the value of learning created by the data generated within a sequential drilling strategy. Using a synthetic reservoir case, the researchers demonstrate how history matching and frequently updating the development strategy can enhance the field development.…”

Ensemble-based well trajectory and drilling schedule optimization—application to the Olympus benchmark model

“…The optimization in these studies was mostly performed on single model realizations only, which means that uncertainty in reservoir properties is implicitly assumed to be absent. Closed-loop field development in the presence of uncertainty was addressed only recently by Shirangi and Durlofsky [32] and Hanea et al [19].…”