中国石油大学学报（自然科学版）2025，Vol.49Issue(2)：214-222,9.DOI:10.3969/j.issn.1673-5005.2025.02.021
基于集成机器学习模型的混合原油凝点预测方法
Gel point estimation method of mixed crude oil based on ensemble machine learning model
摘要
Abstract
Mixed transport is the most common way to transport multiple crude oil in the same pipeline.Grasping the flow properties of the mixed oil quickly and accurately is the basis of making the mixed crude oil distribution scheme and ensuring the safe,efficient and flexible operation of the pipeline.The gel point of mixed crude oil is often determined by the manual sampling test,so it is difficult to effectively control the crude oil into the pipeline in time.It is simple and easy to calculate the gel point of mixed crude oil by using the empirical model based on the ratio and gel point of component crude oil,but there is a bottleneck in the method to improve the prediction accuracy.An integrated machine learning model based on XG-Boost was proposed to predict the gel point of mixed crude oil.The results show that,with the inputs of gel point,density,viscosity and ratio in component oils,the mean absolute error of the model prediction estimations after training with 8912 data is 1.12℃.When the gel point of the component crude oil is missing,the mean absolute error is 1.93℃and the percentage of the predicted absolute error within 2℃is 88.0%.关键词
混合原油/凝点/机器学习/预测Key words
mixed crude oil/gel point/machine learning/prediction分类
能源科技引用本文复制引用
何宇轩,苏怀,张成,苏杨,李鸿英,黄骞,张劲军..基于集成机器学习模型的混合原油凝点预测方法[J].中国石油大学学报（自然科学版）,2025,49(2):214-222,9.基金项目
国家自然科学基金青年科学基金项目(51904316) （51904316）
中国石油大学(北京)科研基金项目(2462021YJRC013) （北京）
