Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS)–a state-of-the-art review
Carbon capture, utilisation and storage (CCUS) will play a critical role in future
decarbonisation efforts to meet the Paris Agreement targets and mitigate the worst effects of …
decarbonisation efforts to meet the Paris Agreement targets and mitigate the worst effects of …
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 sector to mitigate climate change. An integrated …
decarbonize the energy and industrial sector to mitigate climate change. An integrated …
Machine-learning predictions of solubility and residual trapping 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 …
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 …
Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones
H Vo Thanh, Y Sugai, K Sasaki - Scientific reports, 2020 - nature.com
Abstract Residual Oil Zones (ROZs) become potential formations for Carbon Capture,
Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of …
Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of …
Application of machine learning to predict CO2 trapping performance in deep saline aquifers
HV Thanh, KK Lee - Energy, 2022 - Elsevier
Deep saline formations are considered potential sites for geological carbon storage. To
better understand the CO 2 trapping mechanism in saline aquifers, it is necessary to develop …
better understand the CO 2 trapping mechanism in saline aquifers, it is necessary to develop …
CO2 hydrate stability in oceanic sediments under brine conditions
MF Qureshi, H Khandelwal, A Usadi, TA Barckholtz… - Energy, 2022 - Elsevier
Carbon capture and storage (CCS) is a critical approach to reducing atmospheric carbon
emissions. The United Nations [UN] Climate Change Conference COP26 Glasgow [2021] …
emissions. The United Nations [UN] Climate Change Conference COP26 Glasgow [2021] …
Whole process analysis of geothermal exploitation and power generation from a depleted high-temperature gas reservoir by recycling CO2
The geothermal resource in depleted high-temperature gas fields is abundant, and CO 2 is
more suitable to exploit geothermal energy from these gas fields due to its high mobility and …
more suitable to exploit geothermal energy from these gas fields due to its high mobility and …
Machine-learning-based prediction of oil recovery factor for experimental CO2-Foam chemical EOR: Implications for carbon utilization projects
Enhanced oil recovery (EOR) using CO 2 injection is promising with economic and
environmental benefits as an active climate-change mitigation approach. Nevertheless, the …
environmental benefits as an active climate-change mitigation approach. Nevertheless, the …
A gradient-based deep neural network model for simulating multiphase flow in porous media
Simulation of multiphase flow in porous media is crucial for the effective management of
subsurface energy and environment-related activities. The numerical simulators used for …
subsurface energy and environment-related activities. The numerical simulators used for …