An Advanced Long Short-Term Memory (LSTM) Neural Network Method for Predicting Rate of Penetration (ROP)
Author:
Affiliation:
1. College of Petroleum Engineering, Yangtze University, WuhanHubei430100, China
2. Key Laboratory of Hubei Province for Oil and Gas Drilling and Production Engineering, Wuhan430100, Hubei, China
Funder
Ministry of Science and Technology of the People's Republic of China
Natural Science Foundation of Hubei Province
China Oilfield Services Limited
Publisher
American Chemical Society (ACS)
Subject
General Chemical Engineering,General Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acsomega.2c06308
Reference58 articles.
1. Performance Comparison of Algorithms for Real-Time Rate-of-Penetration Optimization in Drilling Using Data-Driven Models
2. ROP and TOB optimization using machine learning classification algorithms
3. Application of hybrid artificial neural networks for predicting rate of penetration (ROP): A case study from Marun oil field
4. Computational prediction of the drilling rate of penetration (ROP): A comparison of various machine learning approaches and traditional models
5. Prediction of Penetration Rate for PDC Bits Using Indices of Rock Drillability, Cuttings Removal, and Bit Wear
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