Optimization of Well Placement in Real-Time Production Optimization of Intelligent Fields with Use of Local and Global Methods

Abstract

Abstract With recent advances in technology and because of its application in the oil industry, real-time production optimization in intelligent fields has become more practical. The optimal control methods developed previously are now applied in smart workflows to maximize recovery or net present value (NPV). Development of new and mature fields can be improved with integrated workflows that account for the technical and economic aspects of hydrocarbon recovery, as well as the potential impact of uncertainty. This approach is important for a high-impact decision-making process such as well placement. Optimization of well placement in complex reservoirs requires a thorough understanding of both local and global optimization methods, key parameters, and constraints. A major drawback in this process is that most optimization algorithms are incorrectly understood and applied by the engineers. In this paper, that gap is addressed by presenting not only the theoretical background but also the application of optimization algorithms existing in workflows that couple reservoir simulation with commercial optimization and uncertainty software updated with continuous data flow from the field. Different optimization techniques are used for optimal well placement. Some of the most commonly used techniques are exploratory, direct, and gradient optimization methods. This paper provides a thorough analysis of these approaches and illustrates the performance evaluations using reservoir simulation case studies as applied in intelligent fields. The analysis considers the computational complexity, scale of the problem, and definition of the objective function. The performance evaluation builds on the analysis to compare the optimal well location, time of convergence to optimal solution, and the corresponding reservoir simulation results. The intended application of this study is to assist in selecting the right approach to make sound engineering decisions for substantial improvement in field production rather than selection of methods which may lead to decreased ultimate recovery.