Stanford Earth researchers awarded by the SEG
The Society of Exploration Geophysicists (SEG) has honored new research on retrieving the subsurface speed of sound, studying waveguide properties of shale gas reservoirs, and using machine learning to characterize rock properties in the subsurface from seismic images.
Nine researchers from the departments of Geophysics and Energy Resources Engineering have received awards for their 2019 contributions to the Society of Exploration Geophysicists (SEG). Five researchers were awarded for their contributions at the SEG Annual Meeting held Sept. 15-20 in San Antonio, Texas. Four others were awarded for studies in its flagship journal, Geophysics.
Geophysics PhD candidates Guillaume Barnier and Ettore Biondi, and Robert Clapp, a senior research engineer in geophysics, received the award for the Best Paper Presented by a Student at the Annual Meeting.
Their work focused on the retrieval of the speed of sound – or velocity model – of the subsurface using seismic data, which is an essential tool for locating potential natural resources and hazards. The team developed a technique called full-waveform inversion by model extension (FWIME), which reduces the need for an accurate initial guess that is necessary within conventional methods.
Ariel Lellouch, a postdoctoral scholar in geophysics, and co-author Biondo Biondi, the Barney and Estelle Morris Professor, received an honorable mention in the category of Best Paper Presented at the Annual Meeting. The award was shared with co-authors Steve Horne, Mark Meadows and Tamas Nemeth from Chevron.
They used the unprecedented resolution offered by seismic sensing with optical fibers to study the physical properties of guided waves in the subsurface. Their analysis showed that shale gas reservoirs act as seismic waveguides. Waves propagating through them are strongly influenced by fluid-filled fractures, and they can thus be used to characterize reservoir behavior during production stages.
“This discovery can lead to optimal well-spacing design and minimization of injected fluids, thus mitigating the environmental impact of natural gas recovery,” Lellouch said.
Vishal Das, geophysics PhD ’19, energy resources engineering PhD candidate Ahinoam Pollack, Uri Wollner, geophysics PhD ’18, and energy resources engineering professor Tapan Mukerji received an honorable mention in the category of Best Paper in Geophysics.
Their paper is about using machine learning methods for seismic impedance inversion, or the detection of different rock layers – a process that can take weeks to months using current methods. Pollack, Wollner, and Das began the research in 2017 as a final project for the Machine Learning (CS 229) course at Stanford, which was later developed into a conference proceeding and then a paper.
“After the network was trained, it predicted rock impedance instantaneously, as opposed to several hours or days,” Das said. “This work showed the promise of using modern deep learning for detecting and depicting important energy and water resources in the ground.”
Congratulations to the winners, who were selected from 1,077 studies presented at the 2019 SEG Annual Meeting and six volumes of Geophysics published in 2019.