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.
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.
A key objective in any reservoir development plan is to achieve maximum reservoir exploitation which is usually quantified using an economic objective such as net present value (NPV). A key element of such an optimized development plan is an optimized well planning scheme (number, placement and trajectories of the wells). In the well planning phase, it is important to quantify the geological uncertainty. In this study, a new approach is presented in which the targets and thereby the trajectories of the wells are optimized while the geological uncertainties are taken into account. The latter is achieved by using an ensemble of updated reservoir models resulting from assisted history matching (AHM) as the input for the optimization of the field development plan. For the case presented in this study, the reservoir structure, more specific the top and bottom of the reservoir, is assumed to be the main source of uncertainty. To optimize the well targets and trajectories, the Stochastic Simplex Approximate Gradients (StoSAG) methodology is used. A parameterization of the well path is proposed, in which the angles, azimuths and measured depths of the targets are used as controls to optimize the trajectories of the horizontal wells. With this parameterization, the horizontal section is not always straight, in contrast to the approaches presented in many previous publications. The proposed workflow has been applied successfully on a realistic synthetic case inspired from a real field case. The results show that significant increases in objective function can be achieved when well trajectories are optimized constrained to uncertainties in the structural model.
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