Integrating mathematical optimization and decision making in intelligent fields

Abstract

In this paper a decision-making approach that can be applied to problems that are relevant to the oil and gas industry is presented. This methodology is supported by state-of-the-art mathematical optimization algorithms, and is based on the formal integration of the decisions in question with well-studied optimization procedures. The integration of the methodology with the application adds to its robustness. Two different types of problems are formulated and solved. The first kind is based on deciding which wells have to be shut in during a given production interval whilst simultaneously optimizing the controls for each selected well. The second category involves deciding for a group of wells which ones have to be injectors or producers, and at the same time searching for optimal well locations. In all the results obtained we can systematically see that the set of decisions proposed by the integrated approach mean substantial improvement in field production. For example, in the first class of problems studied, the production oil target is satisfied, and up to 50 percent of produced water is saved with respect to the reference case. The huge amount of information available, for example, in Intelligent/Smart Fields or Closed-Loop Reservoir Management can be utilized for rigorously making solid decisions. In this work we put an emphasis on integration of real-life decisions with a realistic simulation-based mathematical optimization framework. This framework can be also useful for establishing a common language for decision makers and researchers within a given organization, and as a consequence endowing the decision-making process with agility and robustness. It should be stressed that ultimately it is human interpretation and intuition that drives the making of crucial decisions. Automated tools should be understood as an additional (and hopefully valuable) source of information for making these important decisions. Copyright 2012, Society of Petroleum Engineers.