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).
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. Recent developments in algorithms and computing power have made it possible to begin to address the full complexity of the field-development optimization problem, including a large number of decision variables of various types, a better characterization of geological uncertainty, handling of realistic platform constraints, and operating strategies of newly drilled wells.
Closed-loop reservoir management aims to incorporate new information into models and optimize field-development or reservoir-management strategies on a nearly continuous basis. The main assumption underlying the closed-loop-reservoir-management framework is that acquiring information can change decisions about how the field should be developed or operated such that certain performance objectives are improved. This assumption is identical to that underlying the concept of value-of-information (VOI) determination, which addresses the question of whether one should actually acquire specific data considering not only the expected effect on the system performance but also the cost of acquiring these data from which the information is to be extracted....
Drill and Learn: A Decision-Making Work Flow To Quantify Value of Learning
01 April 2017