Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream

IG Olaizola, M Quartulli, E Unzueta, JI Goicolea… - Sensors, 2022 - mdpi.com
Industry 4.0 concept has become a worldwide revolution that has been mainly led by the
manufacturing sector. Continuous Process Industry is part of this global trend where there …

[HTML][HTML] Artificial general intelligence for the upstream geoenergy industry: a review

JX Li, T Zhang, Y Zhu, Z Chen - Gas Science and Engineering, 2024 - Elsevier
Abstract Artificial General Intelligence (AGI) is set to profoundly impact the traditional
upstream geoenergy industry (ie, oil and gas industry) by introducing unprecedented …

Human centric digital transformation and operator 4.0 for the oil and gas industry

TR Wanasinghe, T Trinh, T Nguyen, RG Gosine… - Ieee …, 2021 - ieeexplore.ieee.org
Working at an oil and gas facility, such as a drilling rig, production facility, processing facility,
or storage facility, involves various challenges, including health and safety risks. It is …

Towards drilling rate of penetration prediction: Bayesian neural networks for uncertainty quantification

M Bizhani, E Kuru - Journal of Petroleum Science and Engineering, 2022 - Elsevier
Drilling rate of penetration (ROP) prediction has long been a part of any drilling activity. By
accurate prediction of ROP, optimization can be done that maximizes ROP and reduces …

Selection of sand control completion techniques using machine learning

H Laoufi, Z Megherbi, N Zeraibi, A Merzoug… - ARMA/DGS/SEG …, 2022 - onepetro.org
Sand production is one of the major problems in many oil and gas assets around the world.
Uncontrollable sand production can affect hydrocarbon recovery and increase operational …

Machine Learning Models for Predicting Asphaltene Stability Based on Saturates-Aromatics-Resins-Asphaltenes

X Gao, P Dong, X Meng, D Tian, X Wang - SPE J, 2023 - onepetro.org
Asphaltene precipitation is one of the challenging flow assurance problems as it can cause
permeability impairment and pipeline blockages by depositing on the surface of well tubing …

Selection of a dimensionality reduction method: An application to deal with high-dimensional geostatistical realizations in oil reservoirs

LM Da Silva, LM Ferreira, GD Avansi… - … Reservoir Evaluation & …, 2023 - onepetro.org
One of the challenges related to reservoir engineering studies is working with essential high-
dimensional inputs, such as porosity and permeability, which govern fluid flow in porous …

Uncertainty quantification of reservoir performance using machine learning algorithms and structured expert judgment

M Fathy, FK Haghighi, M Ahmadi - Energy, 2024 - Elsevier
The increasing demand for fossil energy necessitates forecasting of reservoir performance
and informed decision-making under various production scenarios. Although reservoir …

Optimizing Rig Scheduling Through AI

J Thatcher, M Eldred, A Suboyin, A Rehman… - Abu Dhabi International …, 2022 - onepetro.org
Traditionally, a significant amount of time is invested in producing the most optimal drilling
schedule to deliver the targets considering various constraints and changing priorities. This …

Big data analytics in oil and gas industry

V Shah, J Shah, K Dudhat, P Mehta… - … Technol. Sustain. Smart …, 2022 - api.taylorfrancis.com
An oilfield is a commercial asset similar to any other in that it requires investment to generate
revenue. When it comes to the way the oil and gas (O&G) sector spends to create cash flow …