摘要：The primary impediment to the recovery of heavy oil lies in its high viscosity, which necessitates a deeper understanding of the molecular mechanisms governing its dynamic behavior for enhanced oil recovery. However, there remains a dearth of understanding regarding the complex molecular composition inherent to heavy oil. In this study, we employed high-resolution mass spectrometry in conjunction with various chemical derivatization and ionization methods to obtain semi-quantitative results of molecular group compositions of 35 heavy oils. The gradient boosting (GB) model has been further used to acquire the feature importance rank (FIR). A feature is an independently observable property of the observed object. Feature importance can measure the contribution of each input feature to the model prediction result, indicate the degree of correlation between the feature and the target, unveil which features are indicative of certain predictions. We have developed a framework for utilizing physical insights into the impact of molecular group compositions on viscosity. The results of machine learning (ML) conducted by GB show that the viscosity of heavy oils is primarily influenced by light components, specifically small molecular hydrocarbons with low condensation degrees, as well as petroleum acids composed of acidic oxygen groups and neutral nitrogen groups. Additionally, large molecular aromatic hydrocarbons and sulfoxides also play significant roles in determine the viscosity. ? 2024 The Authors

ISSN号：1672-5107

卷、期、页：卷21期6页4446-4453

发表日期：2024-12-01

期刊分区（SCI为中科院分区）：一区

收录情况：SCI（科学引文索引印刷版）,CSCD（中国科技引文期刊）（核心）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：Petroleum Science

通讯作者：肇千慧

第一作者：吴建勋,周天航,赵锁奇,史权

论文类型：期刊论文

论文概要：肇千慧,吴建勋,周天航,赵锁奇,史权，Impact of molecular composition on viscosity of heavy oil: Machine learning based on semi-quantitative analysis results from high-resolution mass spectrometry，Petroleum Science，2024，卷21期6页4446-4453

论文题目：Impact of molecular composition on viscosity of heavy oil: Machine learning based on semi-quantitative analysis results from high-resolution mass spectrometry