Over the past decades, many technological advances have unlocked new opportunities to boost efficiency in the oil and gas industry (e.g., complex well drilling, injection of advanced chemicals, sophisticated instrumentation). The real engineering challenge is to apply these technologies in the best possible way for each particular case. This leads to very difficult decisions to be made, mainly because every oil and gas field is one of its kind and our knowledge of the subsurface is very limited. Many efforts have been made to develop tools to support these decisions by applying a more systematic approach to determine smart exploitation strategies. Yet, very little has been done on the optimization of reservoir surveillance plans to establish the best observations to monitor de field response to the exploitation strategies, which, in turn, can also contribute to a better exploitation of the reservoir. In this thesis we propose a methodology to assess the value of future measurements as a first step towards the development of a framework to optimize the design of reservoir surveillance plans. We also investigate alternatives to improve current reservoir management approaches by recommending actions which anticipate the availability of future information and account for the impact of immediate decisions on the decisions to be made in the future. Throughout the chapters, we discuss how to combine a variety of topics (e.g., model-based optimization, data assimilation, uncertainty quantification) with other unusual ingredients (e.g., plausible truths, clairvoyance, flexible plans) to develop a methodology which can be applied in many problems involving decision making and learning. Despite being motivated by a real application, this research addresses abstract concepts such as value and information, but always from an engineering perspective. This makes us approach the problem in a different way, which, we hope, will inspire innovative solutions in the future.

Closed-loop reservoir management (CLRM) is a combination of life-cycle optimization and computerassisted history matching. The application of the CLRM framework to real field cases can be computationally demanding. An even higher computational load results from procedures to assess the value of information (VOI) in CLRM. Such procedures, which are performed prior to field operation, i.e. during the field development planning (FDP) phase, require extreme amounts of simulations. Therefore, we look for alternatives to reduce this computational cost. In particular we compare various clustering techniques to select a limited number of representative members from an ensemble of reservoir models. Using K-means clustering, multi-dimensional scaling and tensor decomposition techniques, we test the effectiveness of different dissimilarity measures such as distance in parameter space, distance in terms of flow patterns and distance in optimal sets of controls. As a first step towards large-scale application we apply several of these measures to a VOI-CLRM exercise using a simple 2D reservoir model which results in a reduction of the necessary number of forward reservoir simulations from millions to thousands

This paper extends previous work on value of information (VOI) assessment in closed-loop reservoir management (CLRM) to estimate the added value of performing multiple measurements along the producing life of the reservoir. The new procedure is based on the workflow from our previous paper which allows to quantify the VOI of a single observation under geological uncertainty. Here we show that, by modifying that workflow slightly, it is possible to assess the value of a series of measurements without a prohibitive increase in computational costs. The approach is illustrated with two cases based on a simple water flooding problem in a two-dimensional five-spot reservoir: the first one, in which we assess the value of a series of production measurements, and the second one, in which we estimate the additional value of water front positions tracked by an interpreted time-lapse seismic survey. We believe that our proposed workflow is a complete methodology to estimate the VOI in a CLRM context because we take into account that the production strategy is updated periodically after new information has been assimilated in the models. However, future work will be required to reduce the computational load to allow for the application of the workflow to real field cases.