基于GA-BPANN的钻井机械钻速预测模型
A Prediction Model for Drilling Speed of Drilling Machin-ery Based on GA-BPANN
李博 1王鲁朝1
作者信息
- 1. 山东省第三地质矿产勘查院,山东烟台 264000
- 折叠
摘要
在钻井过程中,优化钻井技术可以降低钻井成本和减少施工事故,而钻速预测是优化钻井的基础.为了提高机械钻速(ROP)预测模型的准确性,开发了一种遗传算法优化的BP人工神经网络(GA-BPANN)的ROP预测模型.首先,采用最大信息系数(MIC)方法进行特征选择降低模型冗余,并将数据进行标准化处理.其次,利用遗传算法(GA)对BPANN的初始权重和偏置进行优化,建立ROP预测新模型.最后,将新模型与BPANN、支持向量回归(SVR)模型进行对比分析.研究结果表明,GA-BPANN模型具有较高的预测精度,同时为钻井过程中提高ROP提供科学依据.
Abstract
During the drilling process,optimizing drilling tech-niques can reduce drilling costs and construction accidents,and drilling speed prediction is the foundation of optimizing drilling.In order to improve the accuracy of the mechanical drilling speed(ROP)prediction model,this paper developed a genetic algorithm optimized BP artificial neural network(GA-BPANN)ROP predic-tion model.Firstly,the maximum information coefficient(MIC)method is used for feature selection to reduce model redundancy,and the data is standardized.Secondly,using genetic algorithm(GA)to optimize the initial weights and biases of BPANN,a new ROP prediction model is established.Finally,compare and analyze the new model with BPANN and Support Vector Regression(SVR)models.The research results indicate that the GA-BPANN model has high prediction accuracy and provides scientific basis for improving ROP during the drilling process.
关键词
机械钻速/预测模型/BP人工神经网络/遗传算法Key words
mechanical drilling speed/prediction model/BP artifi-cial neural network/genetic algorithm引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据