[1]申静波,闫 铁,李井辉,等.基于改进 BP 算法的摩阻因数预测方法研究[J].计算机技术与发展,2018,28(01):164-168.[doi:10.3969/ j. issn.1673-629X.2018.01.035]
　SHEN Jing-bo,YAN Tie,LI Jing-hui,et al. Research on Friction Coefficient Prediction Based on Improved BP Algorithm[J].Computer Technology and Development,2018,28(01):164-168.[doi:10.3969/ j. issn.1673-629X.2018.01.035]
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基于改进 BP 算法的摩阻因数预测方法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年01期
页码:
164-168
栏目:
应用开发研究
出版日期:
2018-01-10

文章信息/Info

Title:
 Research on Friction Coefficient Prediction Based on Improved BP Algorithm
文章编号:
1673-629X(2018)01-0164-05
作者:
申静波1 ; 闫 铁2 ; 李井辉1; 2 ; 孙丽娜1
1. 东北石油大学 计算机与信息技术学院,黑龙江 大庆 163318;
2. 东北石油大学 油气钻井技术国家工程实验室,黑龙江 大庆 163318
Author(s):
SHEN Jing-bo 1 ; YAN Tie 2 ; LI Jing-hui 1; 2 ; SUN Li-na 1
1. School of Computer and Information Technology,Northeast Petroleum University,Daqing 163318,China;
2. National Engineering Laboratory of Oil and Gas Drilling Technology,Northeast Petroleum University,Daqing 163318,China)
关键词:
神经网络; 摩阻因数; BP 算法; 摩阻扭矩
Keywords:
neural network; friction coefficient; improved BP algorithm; drag and torque
分类号:
TP391.9
DOI:
10.3969/ j. issn.1673-629X.2018.01.035
文献标志码:
A
摘要:
在钻井工程设计与实钻过程中,恰当指定摩阻因数是准确预测摩阻、扭矩的前提条件,摩阻因数受众多因素影响且具有不确定性,很难利用普适的数学公式明确表达摩阻因数及其影响因素之间的关系。 针对摩阻因素的特点,提出了采用改进的 BP 神经网络对钻柱力学分析中的摩阻因数进行计算的方法。 首先研究 BP 算法的原理和数学表示,然后结合预测实际将增加动量项、自适应学习率等方法对其进行改进,最后根据摩阻因数的内涵建立以改进 BP 算法为基础的摩阻因数预测模型。 实验结果表明,利用改进 BP 神经网络能够有效实现摩阻因数的准确预测,解决了钻井过程中普遍存在的摩阻因数个体差异问题。
Abstract:
In the process of drilling engineering design and drilling,the reliable friction coefficient should be provided to predict the drag and torque accurately. The friction coefficient is influenced by many factors with uncertainty,so it is difficult to express the relationship between friction coefficient and its factors. According to the characteristics of friction coefficient,we propose a method for computing friction coefficient in drilling string mechanics analysis by BP neural network. Firstly,we research the principle and mathematical representation of BP algorithm,and then improve it with addition of momentum and adaptive learning rate combined with the actual situation. Finally,the friction coefficient prediction model based on improved BP algorithm is established under the connotation of friction coefficient. The simulation shows that the improved BP neural network can be used to improve the prediction accuracy of friction coefficient,which solves the individual difference of friction coefficient in the drilling process.

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更新日期/Last Update: 2018-03-13