Masked autoencoder pretraining on 3.5 million timesteps of real drilling telemetry reduces total mud volume prediction error by 19.8% versus supervised GRU but trails LSTM by 6.4% on Utah FORGE wells.
A novel hybrid transfer learning method for bottom hole pressure prediction
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Do Masked Autoencoders Improve Downhole Prediction? An Empirical Study on Real Well Drilling Data
Masked autoencoder pretraining on 3.5 million timesteps of real drilling telemetry reduces total mud volume prediction error by 19.8% versus supervised GRU but trails LSTM by 6.4% on Utah FORGE wells.