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PREDICTION MODEL OF WAX DEPOSITION RATE BASED ON WOABPNN ALGORITHM

Rongge Xiaoa,* , Qi Zhuanga, Shuaishuai Jina , Wenbo Jina

a Shaanxi Key Laboratory of Advanced Stimulation Technology for Oil & Gas Reservoirs, College of Petroleum Engineering, Xi’an Shiyou University, Xian, Shaanxi, 710065, China
* Corresponding Author. E-mail: xiaorongge@163.com.

Frontiers in Heat and Mass Transfer 2022, 18, 1-7. https://doi.org/10.5098/hmt.18.8

Abstract

A model for predicting wax deposition rate in pipeline transportation is constructed to predict wax deposition in actual pipeline, which can provide decision support for the flow guarantee of waxy crude oil in pipeline transportation. This paper analyzes the working principle of Back Propagation Neural Networks (BPNN). Aiming at the problems of BPNN model, such as over learning, long training time, low generalization ability and easy to fall into local minimum, the paper proposes an improved scheme of using Whale Optimization Algorithm (WOA) to optimize BPNN model(WOABPNN).Taking 38 groups of crude oil wax deposition experimental data in Huachi operation area as an example, the simulation calculation is carried out in MATLAB, and the Genetic Algorithm optimized BPNN(GA-BPNN) and the non Optimized BP neural network are used as comparative models for comparative analysis. The results show that the Mean Relative Error (MRE) of WOA-BPNN model in predicting wax deposition rate is 2.72% and the coefficient of determination(R 2 ) is 0.9966, which are better than those of BPNN and GA-BPNN models. It is proved that WOA-BPNN model has higher accuracy and robustness in predicting wax deposition rate.

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Cite This Article

Xiao, R., Jin, S. (2022). PREDICTION MODEL OF WAX DEPOSITION RATE BASED ON WOABPNN ALGORITHM. Frontiers in Heat and Mass Transfer, 18(1), 1–7.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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