A systematic review of data science and machine learning applications to the oil and gas industry

Z Tariq, MS Aljawad, A Hasan, M Murtaza… - Journal of Petroleum …, 2021 - Springer
This study offered a detailed review of data sciences and machine learning (ML) roles in
different petroleum engineering and geosciences segments such as petroleum exploration …

Modeling of multi-scale transport phenomena in shale gas production—a critical review

H Wang, L Chen, Z Qu, Y Yin, Q Kang, B Yu, WQ Tao - Applied Energy, 2020 - Elsevier
Shale gas, although unconventional, is a prospective clean energy source. Shale gas
production is a complex multi-scale process with its spatial size ranging from the nanoscale …

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 …

A framework for predicting the production performance of unconventional resources using deep learning

S Wang, C Qin, Q Feng, F Javadpour, Z Rui - Applied Energy, 2021 - Elsevier
Predicting the production performance of multistage fractured horizontal wells is essential for
develo** unconventional resources such as shale gas and oil. Accurate predictions of the …

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
develo** reservoirs. Reservoir numerical simulation is the most mature and effective …

[HTML][HTML] A deep-learning-based prediction method of the estimated ultimate recovery (EUR) of shale gas wells

YY Liu, XH Ma, XW Zhang, W Guo, LX Kang, RZ Yu… - Petroleum Science, 2021 - Elsevier
The estimated ultimate recovery (EUR) of shale gas wells is influenced by many factors, and
the accurate prediction still faces certain challenges. As an artificial intelligence algorithm …

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 …

A CNN-BiGRU-AM neural network for AI applications in shale oil production prediction

G Zhou, Z Guo, S Sun, Q ** - Applied Energy, 2023 - Elsevier
In the coming decades, the demand for shale oil will likely surge because of predicted
increases in the global population and productivity. Efficiently predicting shale oil production …

[HTML][HTML] A hybrid machine learning approach based study of production forecasting and factors influencing the multiphase flow through surface chokes

W Kaleem, S Tewari, M Fogat, DA Martyushev - Petroleum, 2024 - Elsevier
Surface chokes are widely utilized equipment installed on wellheads to control hydrocarbon
flow rates. Several correlations have been suggested to model the multiphase flow of oil and …

A Comprehensive review of data-driven approaches for forecasting production from unconventional reservoirs: best practices and future directions

H Rahmanifard, I Gates - Artificial Intelligence Review, 2024 - Springer
Prediction of well production from unconventional reservoirs is a complex problem given an
incomplete understanding of physics despite large amounts of data. Recently, Data …