The challenges to achieve real-time production optimization (RTPO) of oil and gas fields lie in the integration of asset-wide operations at multiple time scales, knowledge of reservoir phenomena, and efficient data management. Traditional approaches to production optimization workflows often make simplifying assumptions and work within artificial boundaries, to lower the complexity of an all-encompassing optimization problem. While this decomposition creates manageable workflows, it does not adequately support the integration of production optimization at multiple levels.

We propose a methodology to achieve hierarchical decomposition of the overall production optimization problem at different time scales, where real-time data are consistently used to identify reservoir performance and optimize production. The optimization tasks at each of these levels are organized through automated transactions of targets, constraints, and aggregate measurements. For example, strategic decisions such as long-term (e.g., yearly, monthly) injection targets, production plans etc. calculated using a full-physics reservoir model are resolved into tactical decisions for short-term (e.g., weekly, daily) production planning.

A moving-horizon based parametric model is proposed to provide fast predictions for production optimization in the short-term framework. Since the model structure is based on the decomposition of a full-physics reservoir model, it is reasonable to expect that the parametric model will be robust enough to be used for extrapolation outside the range of history data, a property needed for optimization purposes. In this paper, we present an analysis of the structure of the physics-compliant empirical model, the model's range of applicability, techniques that can be used for parameter identification, and use of the model for short-term production optimization. The paper presents a number of case studies to illustrate the benefits of the proposed methodology and its application in typical workflows for closed-loop reservoir management.

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