Real-Time Fault Detection and Diagnosis for Oil Well Drilling Using a Multitask Neural Network

Marios Gkionis, Ole Morten Aamo, Ulf Jakob Flø Aarsnes

2025

Abstract

Drilling operations can be unexpectedly laden with mechanical faults, mud loss, and insufficient cuttings transport that incur substantial costs. This can be avoided via accurate and early fault detection and diagnosis. We present a novel Drilling Fault Detection and Diagnosis (FDD) system that leverages Multitask Neural Networks (MTL-NNs). It accounts for the practical limitation that down-hole measurements are normally not available in real-time and can perform FDD relying only on flow and pressure measurements at the drilling rig. Data for training and testing are produced by a simulator based on the distributed flow and pressure dynamics in the entire well governed by four coupled hyperbolic partial differential equations. Faults are incorporated into the simulations so that the data contain information about how diagnostics of faults affect the dynamics. Our numerical experiments, admittedly under quite ideal conditions, show that the proposed method exhibits high generalization performance on diagnosis for fixed well depths, while incorporating varying well depths into a single network requires increased size in both network and training data to maintain performance.

Paper Citation

in Harvard Style

Gkionis M., Aamo O. and Aarsnes U. (2025). Real-Time Fault Detection and Diagnosis for Oil Well Drilling Using a Multitask Neural Network. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 350-361. DOI: 10.5220/0013783800003982

in Bibtex Style

@conference{icinco25,
author={Marios Gkionis and Ole Aamo and Ulf Aarsnes},
title={Real-Time Fault Detection and Diagnosis for Oil Well Drilling Using a Multitask Neural Network},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2025},
pages={350-361},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013783800003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Real-Time Fault Detection and Diagnosis for Oil Well Drilling Using a Multitask Neural Network
SN - 978-989-758-770-2
AU - Gkionis M.
AU - Aamo O.
AU - Aarsnes U.
PY - 2025
SP - 350
EP - 361
DO - 10.5220/0013783800003982
PB - SciTePress