PRODUCTION OPTIMIZATION FOR OILFIELDS USING A MIXED-INTEGER NONLINEAR PROGRAMMING MODEL

摘要

A system performs production optimization for oilfields using a mixed--integer nonlinear programming (MINLP) model. The system uses an offline--online approach to model a network of interdependent wells in an online network simulator while modeling multiple interdependent variables that control performance as an offline MINLP problem. The offline model is based on production profiles established by assuming decoupled wells in the actual network of wells. In one example, an amount of lift-gas to inject and settings for subsurface chokes are optimized. An offline solver optimizes variables through the MINLP model. Offline results are used to prime the online network simulator. Iteration between the offline and online models results in a convergence, at which point values for the interdependent variables are communicated to the real-world oilfield to optimize hydrocarbon production. Priming the online model with results from the offline model drastically reduces computational load over conventional techniques. Additional techniques anneal initial data starting points, smooth pressure differences, and adapt constraint values to further reduce computational intensity.