Production Enhancement Through Integrated Asset Modeling Optimization

Abstract

Abstract Gas lift optimization is often used to enhance production of mature oilfields consisting of multiple reservoirs. For increased efficiency, several of those reservoirs often share the same surface processing facilities. In such context, we wish to find the optimal allocation of gas lift over an entire network of wells and pipelines, while accounting for the constraints imposed by the reservoir operating conditions. The latter may include a limited amount of commodities that can be processed or a limited amount of available lift gas per unit of time. Such engineering optimization problems are extremely difficult to model and to solve efficiently because they involve non-smooth models and are subject to constraints that might be simulation-based and as costly to compute as the objective function. Traditionally, they are solved using either penalty methods, which aggregate objective functions and all constraint evaluations, or classical lexicographic ordering, which evaluates all constraints simultaneously. While those techniques might be appropriate for computationally simple constraints, they could exhibit inefficiency when evaluating simulation-based constraints like the ones encountered in production optimization. In this modeling study, we describe a cost-effective approach to perform such optimizations. An adaptive proxy is used to replace the original constraints and objective function calculation, and a novel sequential lexicographic ordering approach is used to handle the constraints: the linear constraints take precedence over nonlinear constraints and simple nonlinear constraints take precedence over simulated-based ones, which, in turn, take precedence over objective function values. The proxy is periodically validated against the original function to maintain consistency. The methodology and two oilfield-production optimization examples involving oil and gas separation are described. These serve to demonstrate that the proposed method is more efficient than the traditional ones.