Optimization of Production Operations in Petroleum Fields

Abstract

Abstract For some petroleum fields, optimization of production operations can be a major factor for increasing production rates and reducing production costs. While for single wells or other small systems simple nodal analysis can be adequate, large complex systems demand a much more sophisticated approach. Many mature fields are produced by gas-lift under multiple constraints imposed by the field handling capacity of the system. In this paper we present an optimization technique for allocating production rates and lift-gas rates to wells of large fields subject to multiple flow rate and pressure constraints. The well rate and lift-gas rate allocation problem has been addressed in the literature1–13. However, existing methods are either inefficient or make significant simplifications. This often leads to suboptimal operations. This paper proposes a new formulation of the problem that is able to handle flow interactions among wells and can be applied to a variety of problems of varying complexities. We show that the proper formulation of the optimization problem is important in the practical use of modern optimization techniques. Once formulated, the optimization problem is solved by a sequential quadratic programming algorithm. Our results show that the procedure developed in this paper is capable of handling complex oil production problems. Introduction In petroleum fields, hydrocarbon production is often constrained by reservoir conditions, deliverability of the pipeline network, fluid handling capacity of surface facilities, safety and economic considerations, or a combination of these considerations. While production can be controlled by adjusting well production rates, allocating lift-gas rates, and in some fields, by switching well connections from one flowline to another flowline, implementation of these controls in an optimal manner is not easy. The objective of dynamic production optimization is to find the best operational settings at a given time, subject to all constraints, to achieve certain operational goals. These goals can vary from field to field and with time. Typically one may wish to maximize daily oil rates or minimize production costs. Various aspects of production optimization have been addressed in the literature. For example, several researchers1–5 have studied the problem of allocating limited amount of available gas to specified wells for continuous gas-lift. Fang and Lo6 proposed a linear programming technique to allocate lift-gas and well rates subject to multiple flow rate constraints. Barnes et al.7 developed an optimization technique for a portion of the Prudhoe Bay field in Alaska. This model maximizes oil production while minimizing the need for gas processing. Several papers8–10 have reported results for the production optimization of the Kuparuk River field in Alaska. The techniques published so far1–10 either addressed only a part of the optimization problem of interest to us or made significant simplifications during the optimization process. In most commercial reservoir simulators11,12, flow rate constraints on facilities are handled sequentially by ad hoc rules. In addition, gas-lift optimization is done separately from the allocation of well rates. Because of the nonlinear nature of the optimization problem and complex interactions, results from such procedures can be unsatisfactory. In a companion paper Wang et al.13 presented a procedure for the simultaneous optimization of well rates, lift-gas rates, and well connections subject to multiple pressure, flow rate, and velocity constraints. While this approach was successful, it was limited in its ability to handle flow interactions among wells when allocating well rates and lift-gas rates. Here we extend the work of Wang et al.13 and propose a new formulation for the problem of simultaneously optimizing the allocation of well rates and lift-gas rates. The optimization problem is solved by a sequential quadratic programming algorithm, which is a derivative-based nonlinear optimization algorithm. The proposed method is tested on several examples. Results show that the method is capable of handling flow interactions among wells and can be applied to a variety of problems of varying complexities and sizes.