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Prediction Model of wax deposition rate in waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

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Energy Engineering 2024年第4期121卷 1007-1026页

作者： Zhuo Chen Ningning Wang Wenbo Jin Dui Li The Second Oil Production Plant Sinopec Northwest Oilfield CompanyUrumqi830011China Yingmaili Oil and Gas Production Management Area PetroChina Tarim Oilfield CompanyKorla841001China College of Petroleum Engineering Xi’an Shiyou UniversityXi’an710065China

A hard problem that hinders the movement of waxy crude oil is wax deposition in oil *** ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil *** at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is *** validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil *** two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,***,it shows a higher prediction accuracy compared to the ENN prediction *** fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.

关键词： waxy crude oil wax deposition rate chaotic map improved reptile search algorithm Elman neural network prediction accuracy

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A prediction method for the wax deposition rate based on a radial basis function neural network

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Petroleum 2017年第2期3卷 237-241页

作者： Ying Xie Yu Xing School of Petroleum Engineering Southwest Petroleum UniversityChengdu610500China

The radial basis function neural network is a popular supervised learning tool based on machinery learning *** high precision having been proven,the radial basis function neural network has been applied in many *** accumulation of deposited materials in the pipeline may lead to the need for increased pumping power,a decreased flow rate or even to the total blockage of the line,with losses of production and capital investment,so research on predicting the wax deposition rate is significant for the safe and economical operation of an oil *** paper adopts the radial basis function neural network to predict the wax deposition rate by considering four main influencing factors,the pipe wall temperature gradient,pipe wall wax crystal solubility coefficient,pipe wall shear stress and crude oil viscosity,by the gray correlational analysis *** software is employed to establish the RBF neural *** with the previous literature,favorable consistency exists between the predicted outcomes and the experimental results,with a relative error of 1.5%.It can be concluded that the prediction method of wax deposition rate based on the RBF neural network is feasible.

关键词： Crude oil Prediction model Radial basis function neural network wax deposition rate

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