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How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions
Big Data and machine learning (ML) technologies have the potential to impact many facets
of environment and water management (EWM). Big Data are information assets …
of environment and water management (EWM). Big Data are information assets …
[HTML][HTML] Data-driven machine learning for disposal of high-level nuclear waste: A review
G Hu, W Pfingsten - Annals of Nuclear Energy, 2023 - Elsevier
The application of the data-driven machine learning (DDML) for the disposal of the high-
level nuclear waste (HLW) is of emerging interest in the recent years. This review aims to …
level nuclear waste (HLW) is of emerging interest in the recent years. This review aims to …
Machine-learning predictions of solubility and residual trap** indexes of carbon dioxide from global geological storage sites
Ongoing anthropogenic carbon dioxide (CO 2) emissions to the atmosphere cause severe
air pollution that leads to complex changes in the climate, which pose threats to human life …
air pollution that leads to complex changes in the climate, which pose threats to human life …
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 …
Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers
Carbon dioxide storage in underground saline aquifers is considered a promising technique
for decreasing atmospheric CO 2 emissions. The CO 2 residual and solubility in deep saline …
for decreasing atmospheric CO 2 emissions. The CO 2 residual and solubility in deep saline …
Predicting CO2 Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network
Numerical simulation of flow and transport in heterogeneous formations has long been
studied, especially for uncertainty quantification and risk assessment. The high …
studied, especially for uncertainty quantification and risk assessment. The high …
Predicting field production rates for waterflooding using a machine learning-based proxy model
Waterflooding, during which water is injected in the reservoir to increase pressure and
therefore boost oil production, is extensively used as a secondary oil recovery technology …
therefore boost oil production, is extensively used as a secondary oil recovery technology …
Physics-informed deep learning for prediction of CO2 storage site response
Accurate prediction of the CO 2 plume migration and pressure is imperative for safe
operation and economic management of carbon storage projects. Numerical reservoir …
operation and economic management of carbon storage projects. Numerical reservoir …
[HTML][HTML] Impact of deep learning-based dropout on shallow neural networks applied to stream temperature modelling
AP Piotrowski, JJ Napiorkowski, AE Piotrowska - Earth-Science Reviews, 2020 - Elsevier
Although deep learning applicability in various fields of earth sciences is rapidly increasing,
shallow multilayer-perceptron neural networks remain widely used for regression problems …
shallow multilayer-perceptron neural networks remain widely used for regression problems …
A deep-learning-based approach for reservoir production forecast under uncertainty
This paper presents a deep-learning-based proxy modeling approach to efficiently forecast
reservoir pressure and fluid saturation in heterogeneous reservoirs during waterflooding …
reservoir pressure and fluid saturation in heterogeneous reservoirs during waterflooding …