A New Rate-Allocation-Optimization Framework

Abstract

Summary Determination of the operating conditions of a field under a set of physical system constraints (e.g., compressor limits) and engineering preferences (e.g., voidage replacement) is a primary concern for petroleum engineers. Rule-based systems have been proposed for this, but the process is most suitably defined as an optimization problem. An optimization procedure that uses mixed-integer linear programming (MILP) is discussed in this study. Well rates that honor system and engineering constraints are handled simultaneously while the maximum for an objective is calculated (e.g., field oil rate or cash revenue). Optimal rates for the current conditions of the field are determined. Note that this results in instantaneous optimization and, thus, cannot account for recurrent events such as water breakthrough. Nevertheless, an efficient and robust instantaneous optimizer is useful within a grander optimization scheme, short forecast periods and, also, in real-time allocation situations. The approach is able to efficiently handle the nonlin-earities in the system by way of piecewise linear functions. Also, as a result of the formulation, the exact optimal solution of the problem is guaranteed. Another property of the approach is that, in cases in which it is not possible to honor all the targets and limits of the system simultaneously, a scheme is introduced that enables the engineer to prioritize the constraints. This prioritization scheme proves to be of great practical significance because most real cases have conflicting targets and limits that result in optimization systems with no feasible solutions. Also, a heuristic is used that ensures realistic results by elimination of mathematical artifacts (rate oscillations in time) that often arise when the reservoir contains wells with similar properties [e.g., water/oil ratio (WOR) and gas/ oil ratio (GOR)]. The optimization system is applied to synthetic cases and two real-field cases. The real-field cases pose problems that cannot be handled by conventional rule-based systems.