Field Optimization Tool for Maximizing Asset Value

Abstract

Abstract A software tool is described that enables optimization for maximizing asset value, both with and without uncertainty. Modular architecture allows examination of different objective functions, optimization schemes and financial models. The method generates an efficient frontier that can be used for risk and decision analysis. The tool is demonstrated in the context of a field example optimizing for Net Present Value (NPV). Valuation of advanced completions is explored along with the returns gained from expanding surface gas handling facilities. The results clearly demonstrate the utility of such a tool for value-maximization in planning both near and long-term time horizons as well as providing the necessary foundation for maximizing asset value. Introduction In this article we demonstrate an application of asset value maximization through optimization of an existing infill program for a mature real onshore oil and gas field. We optimize a history-matched reservoir model and provide confidence levels under uncertainty by generating efficient frontiers. Application of search or optimization algorithms has been the subject of numerous studies and articles both inside and outside the petroleum industry1–14. Following in particular the work of Raghuraman et al.1 this article considers a real reservoir and attempts to maximize its value by analyzing various exploitation scenarios. This article first describes the main features of the software tool: the overall methodology and different optimization schemes. It then applies the optimization process to the field example. The main objective of the study is to maximize asset value, with and without the presence of uncertainty. The efficient frontier is discussed and its use for risk management and decision-making demonstrated. Methodology The process of optimizing a reservoir, under the assumption that everything is deterministically known, is relatively straightforward. One may want to extract the maximum fraction of oil and/or minimize the water production or maximize the net present value (NPV) of the oil produced, by optimally controlling various operational variables (e.g., individual completion flow rates), all the while accounting for physical constraints (e.g., single well production or pumps/valves limitations) and economic constraints (e.g., drilling, logging or stimulation costs). However, the presence of physical and/or financial uncertainties elevates the problem of optimization to the level of a risk management problem. A software tool has been developed with an easy-to-use dialog-driven interface that encompasses the necessary elements to perform reservoir optimization under uncertainty and to provide the risk analysis necessary for decision-making. A detailed description of the process, with an example on reservoir monitoring and control, is given in Raghuraman et al.1 Figure 1 shows a schematic of the algorithm for a problem with uncertainty.