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Global optimization of grey-box computational systems using surrogate functions and application to highly constrained oil-field operations
Computers & Chemical Engineering ( IF 4.3 ) Pub Date : 2018-02-15 , DOI: 10.1016/j.compchemeng.2018.01.005
Burcu Beykal , Fani Boukouvala , Christodoulos A. Floudas , Nadav Sorek , Hardikkumar Zalavadia , Eduardo Gildin

This work presents recent advances within the AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems (ARGONAUT) framework, developed for optimization of systems which lack analytical forms and derivatives. A new parallel version of ARGONAUT (p-ARGONAUT) is introduced to solve high dimensional problems with a large number of constraints. This development is motivated by a challenging case study, namely the operation of an oilfield using water-flooding. The objective of this case study is the maximization of the Net Present Value over a five-year time horizon by manipulating the well pressures, while satisfying a set of complicating constraints related to water-cut limitations and water handling and storage. Dimensionality reduction is performed via the parametrization of the pressure control domain, which is then followed by global optimization of the constrained grey-box system. Results are presented for multiple case studies and the performance of p-ARGONAUT is compared to existing derivative-free optimization methods.



中文翻译:

使用代理函数对灰箱计算系统进行全局优化,并将其应用于高度受限的油田作业

这项工作介绍了针对灰箱计算问题的全局全局优化算法(ARGONAUT)框架的最新进展,该框架是为缺乏分析形式和派生形式的系统的优化而开发的。引入了新的并行版本的ARGONAUT(p-ARGONAUT),以解决具有大量约束的高维问题。这一发展是由具有挑战性的案例研究推动的,即使用注水法开采油田。本案例研究的目的是通过控制井压力,同时满足与含水率限制以及水处理和存储相关的一系列复杂约束条件,在五年时间范围内使净现值最大化。降维是通过压力控制域的参数化来实现的,然后对受约束的灰箱系统进行全局优化。给出了多个案例研究的结果,并将p-ARGONAUT的性能与现有的无导数优化方法进行了比较。

更新日期:2018-02-15
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