Liu Xiaojie, Mutailipu Meiheriayi, Zhao Jiafei, Liu Yu
Key Laboratory of Ocean Energy Utilization and Energy Conservation of the Ministry of Education, Dalian University of Technology, Dalian 116024, China.
ACS Omega. 2021 Feb 2;6(6):4282-4288. doi: 10.1021/acsomega.0c05290. eCollection 2021 Feb 16.
During the CO injection of geological carbon sequestration and CO-enhanced oil recovery, the contact of CO with underground salt water is inevitable, where the interfacial tension (IFT) between gas and liquid determines whether the projects can proceed smoothly. In this paper, three traditional neural network models, the wavelet neural network (WNN) model, the back propagation (BP) model, and the radical basis function model, were applied to predict the IFT between CO and brine with temperature, pressure, monovalent cation molality, divalent cation molality, and molar fraction of methane and nitrogen impurities. A total of 974 sets of experimental data were divided into two data groups, the training group and the testing group. By optimizing the WNN model (I_WNN), a most stable and precise model is established, and it is found that temperature and pressure are the main parameters affecting the IFT. Through the comparison of models, it is found that I_WNN and BP models are more suitable for the IFT evaluation between CO and brine.
在地质碳封存和二氧化碳强化采油过程中注入二氧化碳时，二氧化碳与地下盐水接触不可避免，而气液界面张力决定了这些项目能否顺利进行。本文应用三种传统神经网络模型，即小波神经网络（WNN）模型、反向传播（BP）模型和径向基函数模型，来预测二氧化碳与盐水之间的界面张力，该界面张力受温度、压力、单价阳离子摩尔浓度、二价阳离子摩尔浓度以及甲烷和氮气杂质的摩尔分数影响。总共974组实验数据被分为两个数据组，即训练组和测试组。通过优化WNN模型（I_WNN），建立了一个最稳定、精确的模型，并且发现温度和压力是影响界面张力的主要参数。通过模型比较，发现I_WNN和BP模型更适合用于评估二氧化碳与盐水之间的界面张力。
