A unified intelligent model for estimating the (gas + n-alkane) interfacial tension based on the extreme gradient boosting (XGBoost) trees

Zhang Jiyuan; Sun Yanchun; Shang Lin; Feng Qihong; Gong Lirong; Wu Kuankuan

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A unified intelligent model for estimating the (gas + n-alkane) interfacial tension based on the extreme gradient boosting (XGBoost) trees

机译：基于极端梯度升压（XGBoost）树的统一智能模型估算（煤气+ N-烷烃）界面张力

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The interfacial tension (IFT) between the injecting gas and host oil is a key parameter that affects the ultimate displacement efficiency and gas injection enhanced oil recovery (EOR) performance. The accurate characterization of the IFT between varying n-alkanes and injecting gases is crucial to obtaining deeper insight into the predominating mechanisms behind the interfacial behaviors of (gas + oil) systems, and in turn ensuring the optimal design of gas injection EOR projects. Laboratory measurement of the IFT usually requires expensive experimental apparatus, time-consuming operation procedure and cumbersome data deduction. This paper proposed the use of a novel supervised learning (SL) method, namely the eXtreme gradient boosting (XGBoost) trees, for the fast estimation of (gas + n-alkane) IFT. A unified estimation model was constructed for varying injecting gas species and n-alkanes based on a large database consisting of a number of 1561 data sets. Results showed that the unified model is capable of accurately reproducing the experimental IFT based on pressure, temperature, n-alkane molecular weight and gas composition. It was also demonstrated that the new model outperforms the multi-layer perceptron (MLP), support vector regression (SVR) and existing correlations in terms of accuracy and robustness. Furthermore, the permutation importance (PI) was applied to quantify the importance of each input feature to the IFT, which concluded that the ranking of features in terms of decreasing importance to the (gas + n-alkane) IFT are: pressure : n-alkane molecular weight gas composition temperature.

机译：注入气体和主体油之间的界面张力（IFT）是影响最终位移效率和气体注入增强的采油（EOR）性能的关键参数。在不同的N-烷烃和注射气体之间的IFT的精确表征对于获得更深的洞察（气体+油）系统的界面行为背后的主要洞察力，并且反过来确保气体喷射EOR项目的最佳设计是至关重要的。 IFT的实验室测量通常需要昂贵的实验装置，耗时的操作程序和麻烦的数据扣除。本文提出了一种新颖的监督学习（SL）方法，即极端梯度升压（XGBoost）树，用于快速估计（气体+ N-烷烃）IFT。构建统一的估计模型，用于基于由多个1561个数据集组成的大型数据库来改变注射气体物种和N-烷烃。结果表明，统一模型能够基于压力，温度，N-烷烃分子量和气体组合物准确地再现实验IFT。还证明了新模型优于多层的Perceptron（MLP），支持向量回归（SVR）以及在准确性和稳健性方面的存在相关性。此外，应用折射重要性（PI）来量化每个输入特征到IFT的重要性，这得出结论，即在降低（气体+ N-烷烃）IFT的重点方面的调节是：压力： N-烷烃分子量>气体组合物>温度。

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来源
《Fuel》 |2020年第15期|118783.1-118783.9|共9页
作者
Zhang Jiyuan; Sun Yanchun; Shang Lin; Feng Qihong; Gong Lirong; Wu Kuankuan;
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作者单位

China Univ Petr East China Minist Educ Key Lab Unconvent Oil & Gas Dev Qingdao 266580 Peoples R China|China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China;

PetroChina Jidong Oilfeld Co Explorat & Dev Res Inst Tangshan 063000 Peoples R China;

PetroChina Jidong Oilfeld Co Explorat & Dev Res Inst Tangshan 063000 Peoples R China|PetroChina Jidong Oilfeld Co Postdoctoral Stn Tangshan 063000 Peoples R China|Res Inst Petr Explorat & Dev Postdoctoral Program PetroChina Beijing 100083 Peoples R China;

China Univ Petr East China Minist Educ Key Lab Unconvent Oil & Gas Dev Qingdao 266580 Peoples R China|China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China;

PetroChina Jidong Oilfeld Co Explorat & Dev Res Inst Tangshan 063000 Peoples R China;

China Univ Petr East China Minist Educ Key Lab Unconvent Oil & Gas Dev Qingdao 266580 Peoples R China|China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Interfacial tension (gas plus n-alkane) mixtures; Estimation model; EXtreme gradient boosting (XGBoost) trees; Supervised learning; Permutation importance;

机译：界面张力（气体加N-烷烃）混合物;估计模型;极端梯度升压（XGBoost）树木;监督学习;排列重要性;

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A unified intelligent model for estimating the (gas + n-alkane) interfacial tension based on the extreme gradient boosting (XGBoost) trees

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