Drilling data from an enhanced geothermal project and its pre-processing for ROP forecasting improvement

Author:

Diaz Melvin B.,Kim Kwang Yeom,Kang Tae-Ho,Shin Hyu-Soung

Funder

New and Renewable Energy Program of the Korea Institute of Energy Technology Evaluation and Planning

Ministry of Trade, Industry and Energy of the Korean Government

International R&D Program

Korea Institute for Advancement of Technology

Publisher

Elsevier BV

Subject

Geology,Geotechnical Engineering and Engineering Geology,Renewable Energy, Sustainability and the Environment

Reference20 articles.

1. Estimating drilling parameters for diamond bit drilling operations using artificial neural networks;Akin;Int. J. Geomech.,2008

2. Rate of penetration prediction and optimization using advances in artificial neural networks, a comparative study;Amar;IJCCI,2012

3. A comparison of geothermal with oil and gas well drilling costs;Augustine,2006

4. Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art;Bello;J. Artif. Intell. Soft Comput. Res.,2015

5. A multiple regression approach to optimal drilling and abnormal pressure detection;Bourgoyne;Soc. Pet. Eng. J.,1974

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