Closing the Gap Between Reservoir Simulation and Production Optimization

摘要

The operation of oil and gas fields requires that a multilevel decision hierarchy is used to address field optimization at all timescales. These decisions affect production volumes and costs, not only in their short-term outcomes but also over the life of the field. Depending on the time scale, several models with varying degrees of complexity and detail are typically used to characterize the reservoir (e.g. material balance, fullphysics model, and decline curve). However, the strategy to integrate and maintain these separate reservoir models (describing the same field) is often ad hoc and inconsistent. To make predictions suitable for short-term decisions, proxy-models (e.g. neural networks, response surface) have been proposed. However, these black-box models do not consider the underlying physics of the reservoir phenomena and are limited to the effects captured in the training data set. In this paper, we build upon our earlier work [1] on integration of full-field strategic models (physics-based) and short range operational models based on the moving-horizon, parametric identification approach for reservoir simulation. A short-range, reduced-order model structure is developed, and the model parameters are obtained from production history data. Because the model structure is motivated by the decomposition of a full-physics model, it is expected to be feasible to extrapolate outside the range of history data. The reduced-order model also increases the computational efficiency and effectiveness in carrying out the simulation objectives. The benefit of the proposed model is to assist in the short-term decision making in production operations. This paper provides a discussion of the methodology for identifying such physics-based parametric models for production operation workflows. It also presents case studies to illustrate the benefits of this method for real time production operations and closed-loop reservoir management.