小波神经网络模型预测二氧化碳+水溶液体系界面张力

江安; 刘平礼; 李年银; 张云飞; 杜新伟

doi:10.21656/1000-0887.370339

小波神经网络模型预测二氧化碳+水溶液体系界面张力

doi: 10.21656/1000-0887.370339

¹中海油能源发展股份有限公司工程技术分公司, 天津 300452;²西南石油大学,成都 610500;³中国石油西气东输管道公司, 江苏常州 213251

基金项目: 国家自然科学基金(面上项目)（51574197）

详细信息

作者简介:
江安（1981—），男，工程师(通讯作者. E-mail: 1787182084@qq.com).

中图分类号: TE311
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出版历程
- 收稿日期: 2016-11-07
- 修回日期: 2016-12-20
- 刊出日期: 2017-10-15

Prediction of Interfacial Tension Between CO₂ and Brine With the Wavelet Neural Network Method

¹CNOOC EnerTech-Drilling & Production Co., Tianjin 300452, P.R.China; ²Southwest Petroleum University, Chengdu 610500, P.R.China; ³WestEast Gas Pipeline Company of CNPC, Changzhou, Jiangshu 213251, P.R.China

Funds: The National Natural Science Foundation of China(General Program)（51574197）

摘要

摘要: 二氧化碳+水溶液体系界面张力(IFT)是影响地层中气水两相运移特性的重要参数之一，对二氧化碳捕集、埋存至关重要.为了快速准确地确定二氧化碳+水溶液体系IFT，对已有IFT实验结果进行了统计整理，得到了1 677组样本数据，考虑了压力,温度,气体中甲烷、氮气含量，水溶液中一价阳离子(Na⁺,K⁺)浓度、二价阳离子(Ca²⁺,Mg²⁺)浓度6个因素对IFT的影响，建立了小波神经网络（WNN）预测模型对二氧化碳+水溶液体系IFT进行预测.模拟结果表明，随机选取839组数据作为训练集样本，得到的小波神经网络结构为6-16-1，该模型预测IFT的平均绝对误差(M_MAE)、平均相对误差(M_MARE)、方差(M_MSE)和相关度(R²)分别为1.23 mN/m,3.30%,2.30 mN²/m²,0.988.与最新提出的多元拟合模型和BP神经网络模型对比结果表明，小波神经网络模型预测精度最高.
- 小波神经网络 /
- 界面张力 /
- 二氧化碳+水溶液体系
Abstract: Interfacial tension (IFT) between CO₂ and formation water is one of the most important parameters for CO₂ capture and storage, for it controls the transport properties of both phases in the formation. In order to rapidly and accurately predict the IFT of the CO₂-brine system, 1 677 sets of measured IFT data from previous studies were acquired. A wavelet neural network (WNN) prediction model was proposed in view of 6 parameters including the pressure, the temperature, the CH₄ molality and the N₂ molality in CO₂ gas, the monovalent cation (Na⁺ and K⁺) concentration and the bivalent cation (Ca²⁺ and Mg²⁺) concentration. The simulation results show that a 3-layer (6-16-1) WNN model comes out of 839 data as the training datasets. The mean absolute error (M_MAE), the mean relative error (M_MARE), the root mean squared error(M_MSE) and the determination coefficient (R²) of the WNN model were 1.23 mN/m, 3.30%, 2.30 mN²/m² and 0.988, respectively. The performance of the WNN model was further compared with one newly proposed multivariate fitting model and the BP neural network model. The comparison results suggest that the WNN model is better than the other 2.
- wavelet neural network /
- interfacial tension /
- CO₂-brine system

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