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

机译:基于混合整数非线性规划模型的油田生产优化

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.
机译:系统使用混合整数非线性规划(MINLP)模型对油田进行生产优化。该系统使用离线在线方法在在线网络模拟器中对相互依赖的井网进行建模,同时对多个相互依赖的变量进行建模,这些变量将性能作为离线MINLP问题进行控制。离线模型基于通过在实际油井网络中假设油井解耦而建立的生产剖面。在一个示例中,优化了要注入的提升气量和地下扼流圈的设置。离线求解器通过MINLP模型优化变量。脱机结果用于启动在线网络模拟器。离线模型和在线模型之间的迭代导致收敛,在这一点上,相互依存变量的值被传达给现实世界的油田,以优化油气产量。用离线模型的结果对在线模型进行预备工作,大大降低了传统技术的计算量。其他技术可对初始数据起点,平滑的压差和适应约束值进行退火,以进一步降低计算强度。

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