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

作　　者：Qian Li Jun-Ping Li Lan-Lan Xie 

机构地区：[1]College of Environment and Civil Engineering,Chengdu University of Technology,Chengdu,610059,Sichuan,China [2]State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(Chengdu University of Technology),Chengdu,610059,Sichuan,China [3]Institute of Exploration Technology,CAGS,Chengdu,610059,Sichuan,China

出　　处：《Petroleum Science》2024年第5期3496-3516,共21页石油科学（英文版）

基　　金：financially supported by CNOOC China Co., Ltd. Zhanjiang Branch (CNOOC-KJ135ZDXM3 8ZJ05ZJ)。

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 effectiveness 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.

关 键 词：DRILLING Rate of penetration(ROP)prediction Machine learning Accuracy evaluation 

分 类 号：TE24[石油与天然气工程—油气井工程]