A Prediction Model for Pressure and Temperature in Geothermal Drilling Based on Physics-Informed Neural Networks
Yin Yuan,
Weiqing Li,
Lihan Bian
et al.
Abstract:With the global expansion of geothermal energy, accurate prediction of pressure and temperature during drilling has become essential for ensuring the safety and efficiency of geothermal wells. Traditional numerical methods, however, often struggle to handle complex wellbore environments due to their high data demands and limited computational accuracy. To address these challenges, this paper introduces an innovative predictive model based on Physics-Informed Neural Networks (PINNs). By integrating physical law… Show more
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