Waseda University · Engineering
Professor Hung Vo Thanh's research lab specializes in advanced modeling and machine learning applications for sustainable energy and carbon management. The lab focuses on enhancing carbon capture, utilization, and storage (CCUS) through innovative geological modeling, predictive analytics, and artificial intelligence. Key research directions include 3D subsurface modeling under uncertainty, CO₂ storage capacity assessment in onshore and offshore reservoirs, and optimizing gas storage and hydrogen adsorption in porous materials. The lab integrates machine learning with reservoir simulation and geostatistical techniques to improve the accuracy and efficiency of energy resource evaluation.
Figures are computed from collected data and may differ slightly.
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 studies to propose the fast tool for evaluating the performance of a CO<sub>2</sub> injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO<sub>2</sub> storage capacity in ROZs. The uncertainties parameters, including the geological factors and well ope
This study proposed a new geological modelling procedure for CO2 storage assessment in offshore Vietnam by integrating artificial neural networks, co-kriging and object-based methods. These methods could solve the limitations of well data that have not been addressed by conventional modelling. Petrel software was used to build a geological model for comparing conventional and new modelling workflows. Moreover, the Eclipse simulator was used for CO2 injection scenarios on the geological model of
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