Predicting rate of penetration (ROP) based on a deep learning approach: A case study of an enhanced geothermal system in Pohang, South Korea

Drilling optimization is essential in constructing an enhanced geothermal system (EGS) and can be facilitated through predicting the rate of penetration (ROP). The ROP evolution along the depth was forecasted by considering the current and previous ROP values as input to a gated recurrent unit (GRU)-based deep learning model. Drilling data was obtained from two geothermal wells in Pohang, South Korea. Multiple data configurations for training and testing were designed from both wells. The proximity of the training section to the target results in improved accuracy in prediction (MAPE smaller than 3