Pour Point Prediction Method for Mixed Crude Oil Based on Ensemble Machine Learning Models

Abstract

Pipelines are the most common way to transport crude oil. The crude oil developed from different fields is mixed first and then transported. The pour point of mixed crude oil is very important for pipeline schemes and ensuring the safe, efficient, and flexible operation of the pipeline. An integrated machine learning model based on XGBoost is identified as optimal to predict the pour point of mixed crude oil by comprehensive comparison among six different types of machine learning models: multiple linear regression, random forest, support vector machine, LightGBM, backpropagation neural network, and XGBoost. A mixed crude oil pour point prediction model with strong engineering adaptability is proposed, focusing on enhancing the flexibility of machine learning model inputs (using density and viscosity instead of component crude oil pour points) and addressing challenges such as data volume and input missing in engineering scenarios. With the inputs of pour point Tg, density ρ, viscosity μ, and ratio Xi in component oils, the mean absolute error of the model prediction estimations after training with 8912 data is 1.12 °C, when the pour point Tg of the component crude oil is missing, the mean absolute error is 1.93 °C and the percentage of the predicted absolute error within 2 °C is 88.0%. This study can provide support for the intelligent control of flow properties of pipeline transport mixed oil.