Uncertainty assessment and reduction are often elements of high-quality decision making, although they are not, in themselves, value creating. Value can be created only through decisions, and any decision changes resulting from assisted history matching should be modeled explicitly. This paper presents a comparison of existing work flows and introduces a practically driven approach, referred to as “drill and learn,” using elements and concepts from existing work flows to quantify the value of learning (VOL).
Introduction
The idea to apply numerical optimization methods to reservoir models in order to arrive at optimal field-development plans has been around for a long time. Early methods for optimization were quite limiting, however, in terms of the complexity of the problems that could be addressed.
