|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|100523||2018||10 صفحه PDF||سفارش دهید||4672 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Geothermics, Volume 72, March 2018, Pages 348-357
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.