Application of BP Neura1 Network in the Gel Point Prediction of Blend Crude Oil

doi:10.3696/j.issn.1006-396X.2009.03.021

Journal of Petrochemical Universities ›› 2009, Vol. 22 ›› Issue (3): 86-88.DOI: 10.3696/j.issn.1006-396X.2009.03.021

Previous Articles Next Articles

Application of BP Neura1 Network in the Gel Point Prediction of Blend Crude Oil

1. Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum(Beijing), Beijing 102249, P. R. China;
2. China National Petroleum Corporation International Turkmenistan Ltd., Beijing 100034, P. R. China

Received:2008-09-22 Revised:2008-12-12 Published:2009-09-25 Online:2009-09-25

BP神经网络在混合原油凝点预测中的应用

侯磊¹,许新裕¹,刘秀联²

1.中国石油大学（北京）城市油气输配技术北京市重点实验室,北京102249;
2.中国石油（土库曼斯坦）阿姆河天然气公司，北京100034

作者简介:侯　磊(1966 -), 男, 山东禹城市, 副教授, 博士
基金资助:
中国石油天然气集团公司中青年创新基金项目(07E1007);863 计划专题课题(2006AA09Z357)

Abstract

Abstract:

The BP neura1 network and the tested gel points of the blend crude oil were studied. A prediction model based on the BP neura1 network was established to calculate the gel point of the blend crude oil. The back-propagation artificial neural network has the satisfactory nonlinear prediction ability. The gel points of the component crude oil and their proportions were taken as the input vector and the gel points of the blend crude oil as the output one. The relationship between the gel points of the blend crude oil and the component crude oil was researched. The results show that the gel point prediction model based on the BP neura1 network is better than other gel point prediction models. The maximum average deviation of the predicted gel point is only 0.128 °C and the average absolute deviation is 0.022 °C. 

Key words: BP neura1 network , Blend crude oil , Gel point , Prediction

摘要： 通过研究BP神经网络算法和混合原油凝点测量数据，用BP神经网络建立一种混合原油的凝点预测模型。引入具有高度非线性预测能力的误差反向传播的人工神经网络方法，以单组分原油凝点及其配比作为神经网络的输入向量，以混合原油凝点为输出向量，研究混合原油凝点与单组分原油凝点及配比之间的相关性。预测结果表明，混合原油凝点预测值的最大绝对偏差为0.128℃，平均绝对偏差为0.022℃，BP神经网络的凝点预测结果准确性明显优于其它混合原油凝点预测模型。

关键词: BP 神经网络 , 混合原油 , 凝点 , 预测

HOU Lei,XU Xin-yu,LIU Xiu-lian. Application of BP Neura1 Network in the Gel Point Prediction of Blend Crude Oil[J]. Journal of Petrochemical Universities, 2009, 22(3): 86-88.

侯磊,许新裕,刘秀联. BP神经网络在混合原油凝点预测中的应用[J]. 石油化工高等学校学报, 2009, 22(3): 86-88.

[1]	Yu Li, Lei Hou, Lei Xu, Xiaozhong Bai, Jinhai Liu, Xin Sun, Wenyuan Gu. Power Consumption Prediction Method for Crude Oil Pipeline Based on Hybrid BP Neural Network [J]. Journal of Petrochemical Universities, 2022, 35(2): 68-73.
[2]	Liu Dejun， Qi Lipeng. Research Progress on Formation Mechanism and Reserve Prediction of Submarine Sandstone Hydrate [J]. Journal of Petrochemical Universities, 2021, 34(2): 76-84.
[3]	Zhou Runnan， Wei Jianguang. Study on Infill Adjustment of Well Pattern Based on Steam Flooding Reservoir Fitting [J]. Journal of Petrochemical Universities, 2019, 32(3): 76-81.
[4]	Zhang Yaxin，Li Si，Wang Weiqiang，Wang Guofu，Xiang Nan，Huang Yue. Analysis of Water Content on Rheology and Wax Precipitation Characteristics [J]. Journal of Petrochemical Universities, 2019, 32(2): 71-76.
[5]	Lan Jinyu. Study on Fine Prediction Method of Sand Body near Fault [J]. Journal of Petrochemical Universities, 2019, 32(1): 47-51.
[6]	Zheng Jianqin, Wang Bohong, Zhang Haoran, Liang Yongtu. Advances in Study of Natural Gas Demand Prediction [J]. Journal of Petrochemical Universities, 2018, 31(04): 1-6.
[7]	Ren Xidong，Li Yongzhou. Numerical Simulation of Large Slope Fracturing Well Productivity in Z Oilfield [J]. Journal of Petrochemical Universities, 2018, 31(02): 76-81.
[8]	Li Shiguang,Lyu Yuling. Research Progress in Prediction Models of Cyclone Performance [J]. Journal of Petrochemical Universities, 2017, 30(3): 89-94.
[9]	Qiao Wenbo,Li Haitao,Zhang Futian,et al. Prediction of Formation Damage of Water Injection Well Based on Orthogonal Design and Statistical Analysis [J]. Journal of Petrochemical Universities, 2015, 28(6): 44-48.
[10]	Zhao Guozhu. Prediction of the Productivity of Multiple Transverse Fractured  Horizontal Wells in Low Permeability Reservoirs [J]. Journal of Petrochemical Universities, 2014, 27(6): 24-28.
[11]	Guo Longjiang,Li Wei,Qin Guoshun,et al. Analysis and Prediction of the Scaling in Gas Wells in Songnan Gas Field [J]. Journal of Petrochemical Universities, 2014, 27(5): 50-53.
[12]	Zhang Jun,Wang Qiang,Xin Mindong,Zhang Aijuan. Canada SAGD Oil Sand Emulsion Gathering Pipeline Network Adaptability Evaluation [J]. Journal of Petrochemical Universities, 2014, 27(5): 92-98.
[13]	ZHANG Jun, CAO Xuewen, ZHANG Nan, LI Xianchao, JI Junyi. Evaluation of Viscosity Prediction Model of Binary Oil Mixtures [J]. Journal of Petrochemical Universities, 2013, 26(6): 65-70.
[14]	LIANG Yu, YUAN Zongming. Effect of Water Cut on Rheological Properties of HZ253 Crude Oil with Emulsions [J]. Journal of Petrochemical Universities, 2013, 26(6): 71-73.
[15]	MA Yun,LI Hai-tao,LIU Li-feng,et al. Corrosion Comprehensive Evaluation of Offshore Oil Field Water Injection Process [J]. Journal of Petrochemical Universities, 2012, 25(5): 52-55.

Application of BP Neura1 Network in the Gel Point Prediction of Blend Crude Oil

BP神经网络在混合原油凝点预测中的应用

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics