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 …
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 …
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 …
the oilfield. The limitations of traditional approaches make time-series production prediction …
Ensemble boosting and bagging based machine learning models for groundwater potential prediction
Due to the rapidly increasing demand for groundwater, as one of the principal freshwater
resources, there is an urge to advance novel prediction systems to more accurately estimate …
resources, there is an urge to advance novel prediction systems to more accurately estimate …
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 …
An integrated machine learning-based approach to identifying controlling factors of unconventional shale productivity
The controlling factors of unconventional shale productivity by comprehensive analysis of
mineralogy, petrophysics, geochemistry, and geomechanics have not been well understood …
mineralogy, petrophysics, geochemistry, and geomechanics have not been well understood …
A new ensemble machine-learning framework for searching sweet spots in shale reservoirs
Knowing the location of sweet spots benefits the horizontal well drilling and the selection of
perforation clusters. Generally, geoscientists determine sweet spots from the well-logging …
perforation clusters. Generally, geoscientists determine sweet spots from the well-logging …
Machine learning-based production forecast for shale gas in unconventional reservoirs via integration of geological and operational factors
Hundreds of horizontal wells have been performed fracturing operations to exploit the
unconventional shale gas resources in the Duvernay Formation of Fox Creek, Alberta …
unconventional shale gas resources in the Duvernay Formation of Fox Creek, Alberta …
Applicability of deep neural networks on production forecasting in Bakken shale reservoirs
The unconventional shale and tight formations, such as the Bakken in Williston Basin, are
becoming an important hydrocarbon sources since the development of advanced horizontal …
becoming an important hydrocarbon sources since the development of advanced horizontal …
[HTML][HTML] Enhancing wettability prediction in the presence of organics for hydrogen geo-storage through data-driven machine learning modeling of rock/H2/brine …
The success of geological H 2 storage relies significantly on rock–H 2–brine interactions
and wettability. Experimentally assessing the H 2 wettability of storage/caprocks as a …
and wettability. Experimentally assessing the H 2 wettability of storage/caprocks as a …