首页|Findings in the Area of Machine Learning Reported from Stevens Institute of Technology (Underground Hydrogen Storage: a Recovery Prediction Using Pore Network Modeling and Machine Learning)
Findings in the Area of Machine Learning Reported from Stevens Institute of Technology (Underground Hydrogen Storage: a Recovery Prediction Using Pore Network Modeling and Machine Learning)
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Elsevier
Investigators publish new report on Machine Learning. According to news reporting from Hoboken, New Jersey, by NewsRx journalists, research stated, “Understanding the hydrogen-brine transport properties and the hydrogen trapping rate (i.e., ratio of residual hydrogen saturation after recovery to initial hydrogen saturation) is critical to the site selection for underground hydrogen storage (UHS). In this study, a three-dimensional pore network model (PNM) was used to simulate hydrogen-brine two phase flow in various porous media, including sandstone, carbonate, and sand packs.” Financial support for this research came from Stevens Institute of Technology. The news correspondents obtained a quote from the research from the Stevens Institute of Technology, “Surface contact angles measured from previous experiments were used to study the influence of the wettability on hydrogen transport in porous media. Many studies have investigated the impact of these factors on carbon dioxide sequestration. However, because of the difference in the thermal dynamic properties of the fluids and the purpose of UHS and carbon dioxide sequestration, it is still essential to analyze the UHS performance under the different rock and fluid properties. PNM simulations showed that a relatively larger contact angle with low water affinity was more suitable for UHS due to its low hydrogen trapping rate. Two machine learning methods, the least square fitting and the support vector machine (SVM), were developed to classify the ability of a rock to trap hydrogen and to predict hydrogen trapping rates. Hydrogen trapping rates simulated using the PNM were used as the training data in the machine learning models. The SVM classified rock samples into two groups which had high hydrogen trapping rates (>50 %) and low hydrogen trapping rates (<50 %). The machine learning results showed that rock samples with a low ratio of pore size to throat size and high pore connectivity (i.e., average number of throats connected to a given pore) were favorable for a low hydrogen trapping rate. This study illustrated that the impact of both rock surface wettability and pore structural geometry should be accounted for when evaluating a hydrogen-brine two-fluid system in porous media.”
HobokenNew JerseyUnited StatesNorth and Central AmericaCarbon DioxideChemicalsCyborgsElementsEmerging TechnologiesGasesHydrogenInorganic ChemicalsMachine LearningStevens Institute of Technology
NETL
NSTL
Elsevier
