Author

Fajr Masri

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Sep 2026

Enhancing Drilling Performance Using Real-Time Machine Learning Models: A Framework for Predicting Formation Transitions During Drilling Operations

Accurate real-time identification of formation transitions is essential for improving drilling efficiency and reducing nonproductive time, particularly in geologically complex environments. Accurate estimates of sonic and bulk density logs are crucial inputs for subsurface formation identification; however, acquiring...

R. Elgaddafi, R. Ahmed, B. Biltayib et al. · GOTECH · 0 citations

Conference Aug 2026

Real-Time Prediction of Near-Bit Rock Geo-Mechanical Properties from Drilling and Gamma Ray Data: A Comparative Analysis Using Machine Learning and Deep Learning Techniques

A robust and reliable machine learning framework for real-time prediction of young's modulus is presented, reducing dependence on core testing and enabling dynamic decision-making during drilling operations, and can be seamlessly integrated into real-time drilling advisory systems.

R. Elgaddafi, B. M. Bitayib, K. Amadi et al. · SPE Nigeria Annual Internati... · 0 citations