Abstract

Standard approaches to optimization under uncertainty in reservoir simulation require use of multiple realizations, with variable parameters representing operational constraints and actions as well as uncertain scenarios. We will show how appropriate use of local optimization within the simulation model, using customized logic for field management strategies, can bring improved workflow flexibility and efficiency, by reducing the effort needed for uncertainty iterations.

To achieve meaningful forecasts for an ensemble of uncertain scenarios, it is important to distinguish between different types of decision. Investment decisions, such as facilities sizing, depend on global unknowns and must be optimized for the complete ensemble. Operational actions, such as closing a valve, can be optimized instantaneously for individual scenarios, using measurable information, although subject to constraints determined at a global level. In this study, we implement local optimization procedures within simulation cases, combining customized objective criteria to rank reactive or proactive actions, with the ability to query reservoir flow entities at appropriate frequencies.

The methods presented in the paper can be used for reactive response modeling for smart downhole control; optimization of ESP/PCP pump performance; and implementation of production plans subject to defined downstream limits. For selected cases, we compare the advantages and disadvantages of the local optimization approach with standardized "big-loop" uncertainty workflows. The methodology can significantly reduce optimization costs, particularly for high-frequency actions, achieving similar objective function values in a fraction of the time needed for post-processing optimizers. Use of tailored scripting provides the capability to modernize the logic framework for field management decisions, with realistic representation of smart field equipment and flow entities at any level of complexity.

Use of efficient workflows as described in this paper can reduce the cost of multiple realization studies significantly, or enable engineers to consider a wider range of possible scenarios, for deeper understanding and better risk mitigation.