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基于神经网络的液力马达提速效果预测研究

熊秀丽 李谦 刘君豪 姜杰

钻探工程2025,Vol.52Issue(5):42-50,9.
钻探工程2025,Vol.52Issue(5):42-50,9.DOI:10.12143/j.ztgc.2025.05.006

基于神经网络的液力马达提速效果预测研究

Research on evaluating of using hydraulic motor based on neural network

熊秀丽 1李谦 1刘君豪 1姜杰1

作者信息

  • 1. 成都理工大学环境与土木工程学院,四川 成都 610059
  • 折叠

摘要

Abstract

Drilling technology is an indispensable technical support for deep resource exploration,and the prediction of drilling efficiency is an important way to improve drilling technology.In response to the requirements for drilling speed-up in a certain block in the South China Sea,this paper collected actual drilling data from 10 wells,and these data were first interpolated and normalized.In order to eliminate the high correlation among different parameters,the initial 43 parameters were reduced to 21 common factors based on factor analysis,where there was no correlation between the 21 factors.Based on well number and depth,combining with a 10-fold cross-validation scheme,stratified sampling and grouping were performed on the original data.Through an optimized structure with a single hidden layer and 15 neurons,two neural network models were established on the basis of whether a hydraulic motor was used,and they both achieved an accuracy of over 96%.The model prediction shows that the use of speed-up tools in the target block with low silica content can effectively improve the drilling efficiency.At the same time,the model also predicts that for the high silicon content section,the use of speed-up drilling tools will increase wear on the drilling tools and cause a decrease in drilling speed.The results of the study show that the drilling speed prediction model based on a neural network can effectively make up for the differences among wellbores.Through accurate drilling speed prediction,it is possible to efficiently evaluate the effect of using speed-up tools and improve drilling efficiency.

关键词

钻速预测模型/神经网络/提速工具/钻探数据处理

Key words

ROP prediction model/artificial neuron network/speed-up tools/drilling data processing

分类

天文与地球科学

引用本文复制引用

熊秀丽,李谦,刘君豪,姜杰..基于神经网络的液力马达提速效果预测研究[J].钻探工程,2025,52(5):42-50,9.

基金项目

四川省自然科学基金项目青年基金"基于数字孪生的动态时变钻进工况自适应迁移模型研究"(编号:2024NSFSC0817) (编号:2024NSFSC0817)

中海石油(中国)有限公司项目"南海西部油田上产2000万方钻完井关键技术研究"子课题"乐东10区超高温高压井综合提速技术研究"(编号:CNOOC-KJ135ZDXM38ZJ05ZJ) (中国)

钻探工程

2096-9686

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