Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran.
Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Sci Rep. 2021 Dec 22;11(1):24403. doi: 10.1038/s41598-021-03643-8.
Accurate prediction of the solubility of gases in hydrocarbons is a crucial factor in designing enhanced oil recovery (EOR) operations by gas injection as well as separation, and chemical reaction processes in a petroleum refinery. In this work, nitrogen (N) solubility in normal alkanes as the major constituents of crude oil was modeled using five representative machine learning (ML) models namely gradient boosting with categorical features support (CatBoost), random forest, light gradient boosting machine (LightGBM), k-nearest neighbors (k-NN), and extreme gradient boosting (XGBoost). A large solubility databank containing 1982 data points was utilized to establish the models for predicting N solubility in normal alkanes as a function of pressure, temperature, and molecular weight of normal alkanes over broad ranges of operating pressure (0.0212-69.12 MPa) and temperature (91-703 K). The molecular weight range of normal alkanes was from 16 to 507 g/mol. Also, five equations of state (EOSs) including Redlich-Kwong (RK), Soave-Redlich-Kwong (SRK), Zudkevitch-Joffe (ZJ), Peng-Robinson (PR), and perturbed-chain statistical associating fluid theory (PC-SAFT) were used comparatively with the ML models to estimate N solubility in normal alkanes. Results revealed that the CatBoost model is the most precise model in this work with a root mean square error of 0.0147 and coefficient of determination of 0.9943. ZJ EOS also provided the best estimates for the N solubility in normal alkanes among the EOSs. Lastly, the results of relevancy factor analysis indicated that pressure has the greatest influence on N solubility in normal alkanes and the N solubility increases with increasing the molecular weight of normal alkanes.
准确预测气体在碳氢化合物中的溶解度是通过注气设计提高石油采收率（EOR）操作以及石油精炼厂中的分离和化学反应过程的关键因素。在这项工作中，使用五种有代表性的机器学习（ML）模型，即带有类别特征支持的梯度提升（CatBoost）、随机森林、轻梯度提升机（LightGBM）、k-最近邻（k-NN）和极端梯度提升（XGBoost），对作为原油主要成分的正构烷烃中的氮（N）溶解度进行建模。利用一个包含 1982 个数据点的大型溶解度数据库，建立了模型，用于预测正构烷烃中 N 溶解度作为压力、温度和正构烷烃分子量的函数，操作压力范围很宽（0.0212-69.12 MPa）和温度（91-703 K）。正构烷烃的分子量范围为 16 至 507 g/mol。此外，还比较了五种状态方程（EOS），包括 Redlich-Kwong（RK）、Soave-Redlich-Kwong（SRK）、Zudkevitch-Joffe（ZJ）、Peng-Robinson（PR）和扰动链统计关联流体理论（PC-SAFT），与 ML 模型一起估计正构烷烃中的 N 溶解度。结果表明，CatBoost 模型是这项工作中最精确的模型，均方根误差为 0.0147，决定系数为 0.9943。EOS 中 ZJ EOS 也为正构烷烃中的 N 溶解度提供了最佳估计。最后，相关性因子分析的结果表明，压力对正构烷烃中的 N 溶解度影响最大，N 溶解度随正构烷烃分子量的增加而增加。
