Well control space out: A deep-learning approach for the optimization of drilling safety operations

A Magana-Mora, M Affleck, M Ibrahim… - IEEE …, 2021 - ieeexplore.ieee.org
As drilling of new oil and gas wells increase to meet energy demands, it is essential to
optimize processes to ensure the health and safety of the crew as well as the protection of …

Camera-based edge analytics for drilling optimization

CP Gooneratne, A Magana-Mora… - … Conference on Edge …, 2020 - ieeexplore.ieee.org
Hydrocarbon exploration and production involve drilling through rock formations to reach
and access target hydrocarbon reservoirs deep below the ground in the safest and most …

The development and application of real-time deep learning models to drive directional drilling efficiency

D Cao, D Hender, S Ariabod, C James… - SPE/IADC Drilling …, 2020 - onepetro.org
Abstract Analysis of mechanical and thermal-induced wellhead growth and resultant loads is
critical for tubular stress design and failure analysis of offshore platform wells. A particular …

Lab-scale drilling rig autonomously mitigates downhole dysfunctions and geohazards through bit design, control system and machine learning

E Zarate-Losoya, T Cunningham, I El-Sayed… - SPE/IADC Drilling …, 2018 - onepetro.org
Oilfield economic conditions today continue to emphasize the need to recognize and
respond to drilling dysfunctions quickly to maximize performance and minimize well costs. It …

Leveraging Targeted Machine Learning for Early Warning and Prevention of Stuck Pipe, Tight Holes, Pack Offs, Hole Cleaning Issues and Other Potential Drilling …

VK Payrazyan, TS Robinson - Offshore Technology Conference, 2023 - onepetro.org
Stuck pipe and other related drilling hazards are major causes of non-productive time while
drilling. Being able to spot early indications of potential drilling risks manually by analyzing …

Real-Time Drilling Engineering and Data-Driven Solutions for Upstream Cost Savings: Coupling Earth Models, Digital Twins, Data & Drilling Automation

MR Amin, A Baruno, R AitAli, J De Vreugd… - SPE Gas & Oil …, 2023 - onepetro.org
Despite the market up-cycle efficient use of CAPEX for upstream well construction projects
remain a key topic for E&Ps. Therefore, non-productive-time and invisible-lost-time (NPT & …

Classification of Drilled Lithology in Real-Time Using Deep Learning with Online Calibration

ML Arnø, JM Godhavn, OM Aamo - SPE Drilling & Completion, 2022 - onepetro.org
Decision making to optimize the drilling operation is based on a variety of factors, among
them real-time interpretation of drilled lithology. Because logging while drilling (LWD) tools …

Machine learning for deepwater drilling: Gas-kick-alarm Classification using pilot-scale rig data with combined surface-riser-downhole monitoring

Q Yin, J Yang, M Tyagi, X Zhou, X Hou, N Wang… - SPE Journal, 2021 - onepetro.org
Gas kicks occur frequently in deepwater drilling because of the extremely narrow mud-
weight window [minimum 0.01 specific gravity (sg)]. The traditional kick-detection method …

Development and application of a real-time drilling state classification algorithm with machine learning

Y Ben*, C James, D Cao - … Conference, Denver, Colorado, 22-24 July …, 2019 - library.seg.org
A fundamental component of a real-time drilling analytics system is automatic rig state
detection. High frequency time series data (typically one data point per second) from …

Recorded well data enriches the testing of automation systems by using a deep neural network approach

Y Yu, S Chambon, Q Liu, JP Belaskie - SPE/IADC Drilling Conference …, 2018 - onepetro.org
One technique useful in the testing and development of drilling automation system is to use
synthetic data. A good drilling time series simulator can enrich a dataset for testing, and …