Assessing the Potential of Masked Autoencoder Foundation Models in Predicting Downhole Metrics from Surface Drilling Data

A systematic review of 13 papers (2015–2025) examines whether Masked Autoencoder Foundation Models can predict downhole drilling metrics from surface sensor data, finding that existing work relies on ANNs and LSTMs but no studies have yet applied MAEFMs to this problem.

MentionsMasked Autoencoder Foundation Models · LSTM · ANN

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