“Neutron Vision: A New Window into Enhanced Oil Recovery” by Dr. Fahrurrozi Akba
When: Thursday July 30, 2026
At 16:00 CEST (10:00 EDT)
REGISTER HERE (to receive the Zoom link promptly, even without being APS member)
Dr Fahrurrozi Akbar, National Research and Innovation Agency (BRIN), Indonesia
Biography:
Dr Fahrurrozi Akbar is an Instrument and Beamline Scientist at the National Research and Innovation Agency (BRIN), where he specializes in the advancement of neutron imaging systems. Since 2021, in collaboration with Trisakti University, his research has focused on the application of neutron tomography to characterize fluid dynamics in Enhanced Oil Recovery (EOR). Beyond hardware development, Mr. Akbar is actively engaged in integrating Artificial Intelligence and deep learning architectures for nuclear data analysis. His current research trajectory aims to synthesize these fields to enable high-temporal-resolution 4D Dynamic Computed Tomography, facilitating real-time observation of complex physical phenomena.
Abstract:
Neutron Imaging (NI) has emerged as a high-fidelity diagnostic tool for the non-destructive characterization of hydrogenous fluids within geological media. Due to the high neutron attenuation cross-section of hydrogen and carbon, NI provides superior contrast for visualizing hydrocarbons sequestered within sandstone matrices. Despite advancements in extraction technology, conventional methods often leave 40–50% of original oil in place (OOIP). Enhanced Oil Recovery (EOR) utilizing chemical surfactants represents a critical frontier in maximizing reservoir yields; however, the pore-scale fluid dynamics and interfacial interactions between surfactants and crude oil within the rock remains insufficiently understood.
This study utilizes Neutron Computed Tomography (NCT) to provide three dimensional visualization of these complex multiphase interactions in situ. By quantifying the spatial distribution and displacement mechanisms of oil when subjected to surfactant flooding, this research offers essential insights for the development of more efficient chemical agents. Furthermore, this work explores the integration of Artificial Intelligence and high-temporal- resolution imaging to transition toward 4D-dynamic NCT, enabling the real-time observation of fluid transport phenomena in porous media.
View Past Recordings
More about PHYSICS MATTERS
Playlist