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Applying hybrid support vector regression and genetic algorithm to water alternating CO2 gas EOR
Greenhouse Gases: Science and Technology ( IF 2.7 ) Pub Date : 2020-05-01 , DOI: 10.1002/ghg.1982
Menad Nait Amar 1 , Noureddine Zeraibi 1 , Ashkan Jahanbani Ghahfarokhi 2
Affiliation  

Water alternating CO2 gas injection (WAG CO2) is one of the most promising enhanced oil recovery techniques. The optimization of this process requires performing many time‐consuming simulations. In this paper, an intelligent hybridization based on support vector regression (SVR) and genetic algorithm (GA) is introduced for the WAG process optimization in the presence of time‐dependent constraints. Multiple SVRs are used as dynamic proxy to mimic numerical simulator behavior in real time. Latin hypercube design (LHD) is applied to generate the proper runs to train the proxy and ten supplementary runs are randomly chosen to validate it. The goal of GA in this study is twofold. First, it is employed during the training of multiple SVRs to find their appropriate hyper‐parameters. Second, once the training and validation of the dynamic proxy are done, the GA is coupled with it to find the optimum WAG parameters which maximize field oil production total (FOPT) subject to time‐dependent water‐cut constraint and some domain constraints. The task is formulated as a non‐linear constrained optimization problem. A semi‐synthetic WAG CO2 case is used to examine the reliability of the approach. The results show that the established dynamic proxy is fast and accurate in reproducing the simulator outputs. The hybridization proxy‐GA is demonstrated to be reliable for the real‐time optimization of the formulated WAG process. © 2020 The Authors. Greenhouse Gases: Science and Technology published by Society of Chemical Industry and John Wiley & Sons, Ltd.

中文翻译:

混合支持向量回归与遗传算法在交替水CO2气采收率中的应用

水交替注入CO 2气体(WAG CO 2)是最有前途的强化采油技术之一。要优化此过程,需要执行许多耗时的模拟。本文介绍了一种基于支持向量回归(SVR)和遗传算法(GA)的智能杂交技术,用于在存在时间依赖性约束的情况下进行WAG工艺优化。多个SVR用作动态代理,可以实时模拟数值模拟器的行为。应用拉丁超立方体设计(LHD)生成适当的运行来训练代理,并随机选择十个补充运行以对其进行验证。GA在这项研究中的目标是双重的。首先,它是在训练多个SVR的过程中使用的,以找到其合适的超参数。其次,一旦完成了动态代理的培训和验证,遗传算法与它相结合,找到了最佳的WAG参数,该参数在受时间依赖的含水约束和某些域约束的约束下,使油田的石油总产量(FOPT)最大化。该任务被表述为非线性约束优化问题。半合成WAG CO2个案例用于检验该方法的可靠性。结果表明,建立的动态代理可以快速,准确地再现模拟器的输出。事实证明,杂交proxy-GA对于配制的WAG工艺的实时优化是可靠的。©2020作者。温室气体:化学工业协会和John Wiley&Sons,Ltd.出版的科学和技术
更新日期:2020-05-01
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