A decision support approach to value flexibility considering uncertainty and future information

Abstract

Flexible strategies have become increasingly important for making strategic decisions under uncertainty conditions, once to consider flexibility in projects to engender the option of making return-maximizing decisions, since uncertainty can be resolved over a project's lifetime. The activities related to reservoir development are inherently associated with decisions making under uncertainty conditions and flexible solutions are very advantageous, allowing the option of making return-maximizing decisions, the uncertainty is resolved over a project's lifetime. One of the technologies that fit in this context are the smart wells, since this technology has the ability to acquire relevant requested information for future decision-making, enabling the generation of a development strategy with future flexibility. However, to value its flexibility if we do not account for uncertainty when performing an optimization, the resulting strategy will have two key shortcomings: it will assign too high value to the smart wells, and it will not take advantage of the ability of the smart wells to adapt and mitigate to uncertainty. We propose a strategy that allows the valuation of flexibility under uncertainty, seeking the flow control strategy that maximizes the expected net present value, dynamically reacting to new information as it is acquired. We demonstrate the proposed approach using a tank drainage problem for which we can evaluate optimization solutions for a broad range of scenarios, including model uncertainty and the flexibility to accommodate future measurements, with a reduced time of evaluation. Then we applied this proposed approach on a reservoir model that highlights its novel aspects: asset optimization under uncertainty, flexible control based on future information, and quantification of the value of flexibility and future measurements. In addition to valuing flexibility and future information, which contributes to the selection of appropriate smart-well technology and measurements, the proposed approach delivers a decision tree that describes a flexible strategy of optimum valve settings that properly accounts for future measurements and their impact on uncertainty reduction. This approach gives a qualitative value, indicating whether the field has the potential of significant improvement using smart wells, and a quantitative valuation of the benefits of smart completions resulting in a realizable strategy to guide the control of these completions in a real field management project.