Subhashis Mallick is the SER Professor of Geophysics at the University of Wyoming, Laramie, Wyoming, USA. Before joining the academia in 2008, he worked in the oil and gas industry for several years where he worked on the development and application of full waveform seismic modeling and inversion methods. He was the Associate Editor of Geophysics during 1993–1994. He has published several research papers in peer-reviewed journals and presented his work in international conferences around the world. His current research interests include seismic anisotropy, seismic modeling, inversion, and imaging, solving multi-physics optimization problems using machine learning and its application in reservoir characterization, and carbon dioxide sequestration.
SEG Reginald Fessenden Award 2023
Subhashis Mallick, currently a professor of geophysics at the University of Wyoming with previous employment at Chevron and WesternGeco, has made numerous contributions to exploration geophysics. In particular, his modeling and processing computer code ANIVEC has been widely used in industry and academia. Along with his extensive technical contributions, Mallick has also acted as an associate editor and Honorary Lecturer for SEG
Biography Citation for the Reginald Fessenden Award
Subhashis Mallick is an active researcher and a professor of geophysics, specializing in seismic exploration. He earned bachelor’s and master’s degrees from the Indian Institute of Technology and a PhD from the University of Hawaii at Manoa.
Subhashis has made significant contributions in analyzing seismic data from areas of complex subsurface geology. Though his works have theoretical and computational bent, they are of significant practical use. He displayed his brilliance at an early stage of his career. The full-waveform seismic anisotropic wave propagation code, ANIVEC, that he developed as a graduate student, has become an industry standard. He was also the first to demonstrate azimuthal variation of amplitude in fractured (anisotropic) media using synthetic seismograms, which is now a routine procedure for fracture mapping from seismic data. Along with continuing this line of work in the industry, he also developed 1D full-waveform inversion using the genetic algorithm.
After a very successful career in the industry (Schlumberger and Chevron) for almost two decades, Subhashis accepted a faculty position. Currently, he is a professor of geophysics in the Department of Geology and Geophysics and the School of Energy Resources at the University of Wyoming. At the university, he led a large project on seismic characterization of subsurface fractures, for which he meticulously designed a 3D land seismic experiment and followed it up with analysis of the data set. He is currently involved in CO2 sequestration projects and in predicting ocean-water temperature and salinity from marine seismic data using artificial intelligence for use in climate modeling and marine biology research.
Even though he is busy with teaching and research, he has been involved in several SEG activities. In the past, he served as an associate editor of GEOPHYSICS and an SEG Honorary Lecturer. He is also writing a book, Computational Seismology, Optimization, and Machine Learning, that will be published by Wiley
2019 SEG Honorary Lecturer, South and East Asia
Reservoir characterization for the next generation
Future advancements in subsurface characterization will require a superior integration of the multi-physics problems through modern artificial intelligence techniques and advanced statistical methods compared to existing methods. From the geophysical end, isotropic and anisotropic visco-elastic prestack seismic waveform inversion need to be integrated with advanced imaging tools, such as reverse-time migration, to simultaneously estimate depth images and the subsurface visco-elastic properties of the P- and S-wave velocities, density, and P- and S- wave attenuations, and other anisotropic properties from three-dimensional seismic data volumes. Rock-physics models for estimating lithological and fluid properties (mineralogy, fluid saturation, porosity, permeability, in-situ stress field, etc.) from these visco-elastic models, well, and core data must be developed and incorporated into a rock-physics inversion scheme under a Bayesian framework to estimate lithological and fluid properties and the associated uncertainties. The visco-elastic models from selected locations must be used to train a machine- learning system, i.e., an artificial intelligence system, for predicting the visco-elastic models and the lithological and fluid properties directly from seismic data. Finally, combining these advanced geophysics/rock-physics/machine-learning tools with the reservoir simulation tools into an integrated framework for predicting the dynamic properties of the subsurface fluids and stress-fields would enable developing the reservoir characterization of the next generation.
A recording of the lecture is available.