A systematic review of data science and machine learning applications to the oil and gas industry
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 …
different petroleum engineering and geosciences segments such as petroleum exploration …
Modeling of multi-scale transport phenomena in shale gas production—a critical review
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 …
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 …
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
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 …
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 …
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 …
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
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 …
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 …
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
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 …
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
Prediction of well production from unconventional reservoirs is a complex problem given an
incomplete understanding of physics despite large amounts of data. Recently, Data …
incomplete understanding of physics despite large amounts of data. Recently, Data …