Journal
GEOTHERMICS
Volume 72, Issue -, Pages 348-357Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.geothermics.2017.12.007
Keywords
ROP prediction; EGS; Multiple regression; Neural network; FFT filtering
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Funding
- 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 [20133030000240]
- International R&D Program - Korea Institute for Advancement of Technology [N0002098]
- Korea Evaluation Institute of Industrial Technology (KEIT) [N0002098] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Drilling parameters are analyzed here to improve forecasting of the rate of penetration (ROP) in enhanced geothermal systems (EGSs). Data recorded during drilling a 4.2-km-deep well at a pilot EGS project in South Korea were analyzed. The greatly fluctuating ROP values were smoothed using a fast Fourier transform filter. Two drilling optimization methods (multiple regression and artificial neural networks) then evaluated the effect of smoothing: it improved ROP prediction in both cases, with over 90% correlation at relatively low degrees of filtering. A methodology to optimize the degree of smoothness for a given drilling data set is suggested.
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