Digitalization in response to carbon neutrality: Mechanisms, effects and prospects

J Ma, L Yang, D Wang, Y Li, Z Xie, H Lv… - … and Sustainable Energy …, 2024 - Elsevier
Digitalization has unfolded great opportunities for its ability to promote carbon neutrality.
Nevertheless, it is still in a nascent stage enduring uncertainties due to the lack of clear …

Machine learning and multi-agent systems in oil and gas industry applications: A survey

KM Hanga, Y Kovalchuk - Computer Science Review, 2019 - Elsevier
The oil and gas industry (OGI) has always been associated with challenges and
complexities. It involves many processes and stakeholders, each generating a huge amount …

Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model

X Song, Y Liu, L Xue, J Wang, J Zhang, J Wang… - Journal of Petroleum …, 2020 - Elsevier
Oil production forecasting is one of the most critical issues during the exploitation phase of
the oilfield. The limitations of traditional approaches make time-series production prediction …

Well performance prediction based on Long Short-Term Memory (LSTM) neural network

R Huang, C Wei, B Wang, J Yang, X Xu, S Wu… - Journal of Petroleum …, 2022 - Elsevier
Fast and accurate prediction of well performance continues to play an increasingly important
role in development adjustment and optimization. It is now possible to predict performance …

Forecasting oil production using ensemble empirical model decomposition based Long Short-Term Memory neural network

W Liu, WD Liu, J Gu - Journal of Petroleum Science and Engineering, 2020 - Elsevier
Oil production forecasting is an important means of understanding and effectively
developing reservoirs. Reservoir numerical simulation is the most mature and effective …

A survey on industry 4.0 for the oil and gas industry: upstream sector

O Elijah, PA Ling, SKA Rahim, TK Geok, A Arsad… - IEEE …, 2021 - ieeexplore.ieee.org
The market volatility in the oil and gas (O&G) sector, the dwindling demand for oil due to the
impact of COVID-19, and the push for alternative greener energy are driving the need for …

[HTML][HTML] A survey on the application of machine learning and metaheuristic algorithms for intelligent proxy modeling in reservoir simulation

CSW Ng, MN Amar, AJ Ghahfarokhi… - Computers & Chemical …, 2023 - Elsevier
Abstract Machine Learning (ML) has demonstrated its immense contribution to reservoir
engineering, particularly reservoir simulation. The coupling of ML and metaheuristic …

Hydrocarbon production dynamics forecasting using machine learning: A state-of-the-art review

B Liang, J Liu, J You, J Jia, Y Pan, H Jeong - Fuel, 2023 - Elsevier
Accurate prediction of hydrocarbon production is crucial for the oil and gas industry.
However, the strong heterogeneity of underground formation, the inconsistency in oil–gas …

[HTML][HTML] Well production forecast in Volve field: Application of rigorous machine learning techniques and metaheuristic algorithm

CSW Ng, AJ Ghahfarokhi, MN Amar - Journal of Petroleum Science and …, 2022 - Elsevier
Developing a model that can accurately predict the hydrocarbon production by only
employing the conventional mathematical approaches can be very challenging. This is …

Artificial intelligence applications in reservoir engineering: a status check

T Ertekin, Q Sun - energies, 2019 - mdpi.com
This article provides a comprehensive review of the state-of-art in the area of artificial
intelligence applications to solve reservoir engineering problems. Research works including …