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

被引：51

作者：

Diaz, Melvin B. ^{[1
]}

Kim, Kwang Yeom ^{[2
]}

Kang, Tae-Ho ^{[2
]}

Shin, Hyu-Soung ^{[2
]}

机构：

[1] Univ Sci & Technol, 34113 217 Gajeong Ro, Daejeon, South Korea

[2] Korea Inst Civil Engn & Bldg Technol, 283 Goyang Daero, Goyang Si 10223, Gyeonggi Do, South Korea

来源：

GEOTHERMICS | 2018年 / 72卷

关键词：

ROP prediction; EGS; Multiple regression; Neural network; FFT filtering; FFT;

D O I：

10.1016/j.geothermics.2017.12.007

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

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.

引用

页码：348 / 357

页数：10

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