Predicting rate of penetration during drilling of deep geothermal well in Korea using artificial neural networks and real-time data collection-Reference-Cited by-同舟云学术

Predicting rate of penetration during drilling of deep geothermal well in Korea using artificial neural networks and real-time data collection

Published:2019-07 Issue: Volume:67 Page:225-232
ISSN:1875-5100
Container-title:Journal of Natural Gas Science and Engineering
language:en
Short-container-title:Journal of Natural Gas Science and Engineering

Author:

Diaz Melvin B.,Kim Kwang Yeom,Shin Hyu-Soung,Zhuang Li

Funder

Korea Institute of Energy Technology Evaluation and Planning

Korean Ministry of Trade, Industry and Energy

Korea Institute for Advancement of Technology

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference30 articles.

1. Examination of the relationship between rate of penetration and mud weight based on unconfined compressive strength of the rock;Alkinani;J. King Saud Univ. Sci.,2018

2. Investigation of the most efficient approach of the prediction of the rate of penetration. Energy sources, Part A recover;Arabjamaloei;Util. Environ. Eff.,2012

3. Modeling and optimizing rate of penetration using intelligent systems in an Iranian southern oil field (Ahwaz oil field);Arabjamaloei;Pet. Sci. Technol.,2011

4. Rate of penetration of geothermal wells: a key challenge in hard rocks;Baujard,2017

5. Data Mining Techniques : for Marketing, Sales, and Customer Support;Berry,1997

Cited by 41 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research on a drilling rate of penetration prediction model based on the improved chaos whale optimization and back propagation algorithm;Geoenergy Science and Engineering;2024-09

2. Comparative analysis of machine learning techniques for predicting drilling rate of penetration (ROP) in geothermal wells: A case study of FORGE site;Geothermics;2024-07

3. Study on Evaluation and Prediction for Shale Gas PDC Bit in Luzhou Block Sichuan Based on BP Neural Network and Bit Structure;Applied Sciences;2024-05-22

4. A systematic review of machine learning modeling processes and applications in ROP prediction in the past decade;Petroleum Science;2024-05

5. Probing fractured reservoir of enhanced geothermal systems with fuzzy-genetic inversion model: Impacts of geothermal reservoir environment;Energy;2024-03