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Pressure Drop Prediction of Crude Oil Pipeline Based on PSO-BP Neural Network

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Listed:
  • Lixin Wei

    (Key Laboratory of Enhanced Oil Recovery, Northeast Petroleum University, Ministry of Education, Daqing 163318, China)

  • Yu Zhang

    (Key Laboratory of Enhanced Oil Recovery, Northeast Petroleum University, Ministry of Education, Daqing 163318, China)

  • Lili Ji

    (Key Laboratory of Enhanced Oil Recovery, Northeast Petroleum University, Ministry of Education, Daqing 163318, China)

  • Lin Ye

    (Bohai Petroleum Institute, Tianjin Branch of CNOOC China Limited, Tianjin 300452, China)

  • Xuanchen Zhu

    (Key Laboratory of Enhanced Oil Recovery, Northeast Petroleum University, Ministry of Education, Daqing 163318, China)

  • Jin Fu

    (Key Laboratory of Enhanced Oil Recovery, Northeast Petroleum University, Ministry of Education, Daqing 163318, China)

Abstract

Pipeline transportation of crude oil has great advantages over traditional oil transmission methods, in terms of economic and environmental protection. The main costs in the oilfield surface system are the fuel costs for heating the crude oil during transportation and the electricity costs for the pumping units. In the northeast of China, where winter temperatures are extremely low and the oil has a high freezing point and high viscosity, higher temperatures, and pressures are required to transport crude oil. With machine learning widely used in many industries and achieving better results, the digital management of oil pipelines has stored a large amount of production and operation data, which has laid the foundation for the research of oil pipeline process calculation using machine learning methods. In this paper, a crude oil pressure drop calculation of an oil pipeline in Northeast China is carried out based on a neural network. For pipeline pressure drop calculation, the back propagation neural network (BP) pressure drop calculation model and particle swarm optimization for back propagation neuron network (PSO-BP) pressure drop calculation model are established. Two models were used to calculate and compare the measured data, and the average absolute error of the PSO-BP model was the smallest, which was 0.015%. Compared with the BP model, the average relative error is reduced by 13.16%. Therefore, The PSO-BP pressure drop calculation model has high accuracy and is of practical significance for predicting pipeline pressure drop.

Suggested Citation

  • Lixin Wei & Yu Zhang & Lili Ji & Lin Ye & Xuanchen Zhu & Jin Fu, 2022. "Pressure Drop Prediction of Crude Oil Pipeline Based on PSO-BP Neural Network," Energies, MDPI, vol. 15(16), pages 1-12, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5880-:d:887535
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    References listed on IDEAS

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    1. Tiezhu Sun & Xiaojun Huang & Caihang Liang & Riming Liu & Xiang Huang, 2022. "Prediction and Analysis of Dew Point Indirect Evaporative Cooler Performance by Artificial Neural Network Method," Energies, MDPI, vol. 15(13), pages 1-14, June.
    2. Yuan Guo & Ge Xiong & Liangcai Zeng & Qingfeng Li, 2021. "Modeling and Predictive Analysis of Small Internal Leakage of Hydraulic Cylinder Based on Neural Network," Energies, MDPI, vol. 14(9), pages 1-14, April.
    3. Wei Han & Lingbo Nan & Min Su & Yu Chen & Rennian Li & Xuejing Zhang, 2019. "Research on the Prediction Method of Centrifugal Pump Performance Based on a Double Hidden Layer BP Neural Network," Energies, MDPI, vol. 12(14), pages 1-14, July.
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