A systematic review of machine learning modeling processes and applications in ROP prediction in the past decade

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外文摘要：Fossil fuels are undoubtedly important,and drilling technology plays an important role in realizing fossil fuel exploration;therefore,the prediction and evaluation of drilling efficiency is a key research goal in the industry.Limited by the unknown geological environment and complex operating procedures,the prediction and evaluation of drilling efficiency were very difficult before the introduction of machine learning algorithms.This review statistically analyses rate of penetration(ROP)prediction models established based on machine learning algorithms;establishes an overall framework including data collection,data preprocessing,model establishment,and accuracy evaluation;and compares the effec-tiveness of different algorithms in each link of the process.This review also compares the prediction accuracy of different machine learning models and traditional models commonly used in this field and demonstrates that machine learning models are the most effective technical means in current ROP prediction modeling.

外文关键词：

DrillingRate of penetration(ROP)predictionMachine learningAccuracy evaluation

作者：

Qian Li、Jun-Ping Li、Lan-Lan Xie

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作者单位：

College of Environment and Civil Engineering,Chengdu University of Technology,Chengdu,610059,Sichuan,China

State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(Chengdu University of Technology),Chengdu,610059,Sichuan,China

Institute of Exploration Technology,CAGS,Chengdu,610059,Sichuan,China

College of Environment and Civil Engineering,Chengdu University of Technology,Chengdu 610059,Sichuan,China

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出版年：

2024

DOI：

10.1016/j.petsci.2024.05.013

石油科学(英文版)

中国石油大学（北京）

石油科学(英文版)

影响因子：0.88

ISSN：1672-5107

年,卷(期)：2024.21(5)