Typically, subsurface knowledge and operational aspects are key for the success of field-development strategy, well planning, and reservoir-management decisions. In general, reservoir models are used to: 1. quantify subsurface understanding, 2. obtain quality prediction, and 3. support decision making. In practice, it is challenging to build or calibrate reservoir models that preserve enough physical and operational representation to the degree at which quality decisions can be obtained. Therefore, it is necessary to identify the interlink between subsurface understanding and decision quality. The interlink is basically a collection of few subsurface and operational elements that have significant impact to decision – This interlink is referred to as the value of (subsurface and operational) learning. This paper demonstrates the use of an ensemble-based workflow to: 1. identify value of learning, 2. evaluate value of learning with a variety of decisions, and 3. access the consequences of value of learning.

In this work, we used EVEREST, a technology for optimization under uncertainty co-owned by TNO and Equinor, to quantify the impact of both geological and operational uncertainties (e.g. drilling time, production time, rig arrival and departure availability) to decision quality. We derived value of learning from the computationally efficient and attractive approximate gradient (StoSAG method) used in EVEREST. The approximate gradient, which here serves as a sensitivity metric, is used to rank the influence of geological and operational parameters to the decision. The key subsurface and operational elements are captured and evaluated for a robust decision making.

The proposed workflow is demonstrated with a synthetic but realistic REEK model. The paper addresses on how value of learning could be used in practice with the potential benefits for decision making. The results showed that the interlink between key parameters and key actions are crucial for robust decision making from which the consequences and optimization scenarios are evaluated systematically. In addition, the value of learning helps practitioner access relevant key information that connects key uncertainty to the key decision. The proposed method leads to decision maturation and support system that build the connection between subsurface knowledge, operational aspects, and decision making.

Increasing the ultimate recovery of a producing oil field bears challenges related to an already implemented production strategy. As the producing stage advances, the flexibility to drill new wells or install additional facilities becomes limited. Acquiring additional 4D seismic (4DS) information may increase the Chance of Success (CoS) of a revitalization project supporting decision making about production strategy selection based on increased reservoir knowledge. We consider that a seismic study is performed to identify the most likely model, enabling the selection of the optimal production strategy obtained from numerical reservoir simulation. We present a practical methodology to account for imperfect information when quantifying the Expected Value of Information (EVoI) and the CoS of a 4DS acquisition for a producing oil field. The inputs include production and economic forecasts for a set of possible production strategies and uncertain reservoir scenarios. We apply Bayesian analysis to update prior probabilities of the uncertain scenarios for different reliability degrees, eliminating the need to sample new scenarios. The benefits of acquiring new data are computed using the revised probabilities, acquisition costs, and value estimates for the input set of production strategies. Results show that production strategy selection depends on 4DS outcomes and their reliability. The 4DS can give especially relevant information in implemented production strategy cases, maximizing the value of the low existent flexibility. A gradual decline in reliability decreases EVoI and CoS considerably. Our methodology gives the range of reliability where acquisition is economically feasible, indicating the minimum degree required to approve a new acquisition.