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Tackling climate change with machine learning
Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
From fluid flow to coupled processes in fractured rock: Recent advances and new frontiers
Quantitative predictions of natural and induced phenomena in fractured rock is one of the
great challenges in the Earth and Energy Sciences with far‐reaching economic and …
great challenges in the Earth and Energy Sciences with far‐reaching economic and …
Efficient prediction of hydrogen storage performance in depleted gas reservoirs using machine learning
Abstract Underground hydrogen (H 2) storage (UHS) has emerged as a promising
technology to facilitate the widespread adoption of fluctuating renewable energy sources …
technology to facilitate the widespread adoption of fluctuating renewable energy sources …
Application of machine learning in carbon capture and storage: An in-depth insight from the perspective of geoscience
P Yao, Z Yu, Y Zhang, T Xu - Fuel, 2023 - Elsevier
Greenhouse gas emissions cause serious global climate change, and it is urgent to curb CO
2 emissions. As the last-guaranteed technology to reduce carbon emissions, carbon capture …
2 emissions. As the last-guaranteed technology to reduce carbon emissions, carbon capture …
Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities
Carbon capture, utilization, and sequestration (CCUS) is a promising solution to
decarbonize the energy and industrial sectors to mitigate climate change. An integrated …
decarbonize the energy and industrial sectors to mitigate climate change. An integrated …
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 …
Subsurface sedimentary structure identification using deep learning: A review
The reliable identification of subsurface sedimentary structures (ie, geologic heterogeneity)
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …
Advances in machine learning technology for sustainable biofuel production systems in lignocellulosic biorefineries
In view of the global climate change concerns, the society is approaching towards the
development of 'green'and renewable energies for sustainable future. The non-renewable …
development of 'green'and renewable energies for sustainable future. The non-renewable …
A review of risk and uncertainty assessment for geologic carbon storage
Carbon capture, utilization, and storage (CCUS) in geological formations play a key role in
mitigating anthropogenic CO 2 emissions and achieving the aggressive goal of net-zero …
mitigating anthropogenic CO 2 emissions and achieving the aggressive goal of net-zero …