Prediction Method of Wax Deposition Rate in Crude Oil Pipeline Based on RBF Neural Network and Support Vector Machine

Zhang Yu,Wang Ruoyu,Wang Xue,T Zhu,A Sharifi,M Anpo

doi:10.1051/e3sconf/202127104007

Abstract

Wax-bearing crude oil will precipitate wax crystals in pipeline transportation, which will cause hidden dangers and affect the economic benefits of the pipeline. In order to study the complex wax deposition on the pipe wall and calculate the wax deposition under other conditions, this paper uses RBF neural network and support vector machine to predict the wax deposition data in Huachi operation area. The results show that the errors of the two methods meet the requirements. Because support vector machine can model and calculate finite samples, it is found that the accuracy of support vector machine is higher.

Highlights

Due to the wax crystallization of waxy crude oil in pipeline transportation, wax deposition layer is attached to the wall of the pipeline, and the pressure required for pipeline oil transportation is increased by reducing the cross-sectional area of pipeline flow, which affects the economic benefits of pipeline transportation[1]
4.1 Introduction to Support Vector Machine Support Vector Machine (SVM) is a machine learning method based on statistical learning theory VC dimension theory and structural risk minimization principle
Support vector in support vector machine (SVM) is the nearest series of points to hyperplane in the sample

Summary

Introduction

Due to the wax crystallization of waxy crude oil in pipeline transportation, wax deposition layer is attached to the wall of the pipeline, and the pressure required for pipeline oil transportation is increased by reducing the cross-sectional area of pipeline flow, which affects the economic benefits of pipeline transportation[1]. When the wax layer is too thick or even plugs the pipeline, it may cause a condensate pipe accident. With the flow of crude oil, the wax crystals along the pipeline are cleaned[2]. The number of balls is proportional to the investment, so it is of great significance to master the wax deposition law of the pipeline and predict the wax deposition phenomenon according to the law, so as to scientifically plan the cycle of wax removal. Seven relevant factors affecting wax deposition, including wall temperature, crude oil temperature, viscosity, flow rate, wall shear stress, wall temperature gradient and wax molecular concentration gradient at the wall, were selected as the input data for simulation, and the rate prediction model of wax deposition was established[4]

Structure principle of RBF neural network

Influencing factors of wax deposition rules in crude oil

Prediction of wax deposition rate based on support vector machine

Example Calculation and Comparison

Conclusions