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.
Machine learning models for equivalent circulating density prediction from drilling data
2 Pith papers cite this work. Polarity classification is still indexing.
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A literature review of thirteen papers finds that masked autoencoders have not been applied to downhole metric prediction from surface drilling data despite their advantages for unlabeled time-series modeling.
citing papers explorer
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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.
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Assessing the Potential of Masked Autoencoder Foundation Models in Predicting Downhole Metrics from Surface Drilling Data
A literature review of thirteen papers finds that masked autoencoders have not been applied to downhole metric prediction from surface drilling data despite their advantages for unlabeled time-series modeling.