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

TL;DR

This paper reviews the potential of Masked Autoencoder Foundation Models (MAEFMs) for predicting downhole metrics from surface drilling data, highlighting their advantages and the need for future empirical validation.

Contribution

It systematically maps existing research and identifies MAEFMs as a promising yet unexplored approach for drilling analytics.

Findings

Current methods mainly use ANNs and LSTMs.

MAEFMs can leverage unlabeled data for better generalization.

Future work should empirically test MAEFMs against existing models.

Abstract

Oil and gas drilling operations generate extensive time-series data from surface sensors, yet accurate real-time prediction of critical downhole metrics remains challenging due to the scarcity of labelled downhole measurements. This systematic mapping study reviews thirteen papers published between 2015 and 2025 to assess the potential of Masked Autoencoder Foundation Models (MAEFMs) for predicting downhole metrics from surface drilling data. The review identifies eight commonly collected surface metrics and seven target downhole metrics. Current approaches predominantly employ neural network architectures such as artificial neural networks (ANNs) and long short-term memory (LSTM) networks, yet no studies have explored MAEFMs despite their demonstrated effectiveness in time-series modeling. MAEFMs offer distinct advantages through self-supervised pre-training on abundant unlabeled data, enabling multi-task prediction and improved generalization across wells. This research establishes that MAEFMs represent a technically feasible but unexplored opportunity for drilling analytics, recommending future empirical validation of their performance against existing models and exploration of their broader applicability in oil and gas operations.