摘要：Mixed transport is the most common way to transport multiple crude oil in the same pipcline. Grasping the flow properties of the mixed oil quickly and accurately is the basis of making the mixed crude oil distribution scheine and ensuring die 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 °C. When the gel point of the component crude oil is missing, the mean absolute error is 1. 93 ^ and the percentage of die predicted absolute error within 2 Xl is 88. 0%. ? 2025 University of Petroleum, China. All rights reserved.

ISSN号：1673-5005

卷、期、页：卷49期2：214-222

发表日期：2025-04-01

期刊分区（SCI为中科院分区）：无

收录情况：EI（工程索引）

发表期刊名称：Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science)

通讯作者：何宇轩,苏杨

第一作者：苏怀,张诚恺,李鸿英,黄强,张劲军

论文类型：期刊论文

论文概要：何宇轩,苏怀,张诚恺,苏杨,李鸿英,黄强,张劲军，Gel point estimation method of mixed crude oil based on ensemble machine learning model，Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science)，2025，卷49期2：214-222

论文题目：Gel point estimation method of mixed crude oil based on ensemble machine learning model