Shuvajit Bhattacharya

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Shuvajit Bhattacharya
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SEG J. Clarence Karcher Award 2022

Shuvajit Bhattacharya is a researcher at the Bureau of Economic Geology at the University of Texas at Austin. Characterized as a “rising star in our profession,” Bhattacharya is an impactful applied geophysicist with expertise in petrophysics, seismic attributes, and machine learning. The significance and impact of his work are broad across the interpretation community. This is demonstrated by the popularity of his publications showing the quantitative integration of several 3D seismic attributes, petrophysics, and rock physics to identify and map sweet spots, one such publication being the most downloaded article in Interpretation in 2020. He has also contributed to our science with contributions in machine learning, carbon sequestration, and integration of 3D attributes with deep learning. With his crossdisciplinary focus and attention to scientific rigor, he has already become a sought-after reviewer at this early stage in his career. Bhattacharya’s service to the Society is noteworthy and includes service as cochair of technical sessions at Annual Meetings, multiple assignments as special issue editor for Interpretation, and deputy associate editor of Interpretation.

Biography Citation for the J. Clarence Karcher Award

by Satinder Chopra

It is with great pleasure that I write this citation for Shuvajit Bhattacharya on receiving SEG’s prestigious J. Clarence Karcher Award. Shuvajit is an outstanding applied geophysicist and petrophysicist whose career so far is characterized by the application of geoscience for subsurface interpretation. He has been at the forefront when it comes to applying new state-of-the-art techniques or modeling for the interpretation of seismic and petrophysical data for a fundamental understanding of the subsurface, with implications on prospect generation and development. His achievements over the last decade have brought credit to our profession, and it continues.

During the last two decades, the focus of exploration activity in North America shifted toward unconventional low-permeability reservoirs that are exploited by horizontal wells and multistage hydraulic fracturing technology. Shuvajit sensed this early and worked on the petrophysical joint inversion modeling and machine learning-assisted shale lithofacies analysis of the Bakken Formation in the Williston Basin for his Ph.D. in 2016 from West Virginia University under Timothy Carr. Shuvajit taught at the University of Alaska, Anchorage, as an assistant professor of geophysics for three years and delivered courses on geophysics and petrophysics, in addition to advising students. He also carried out different research projects on integrated 3D seismic, petrophysics, and machine learning for hydrocarbon exploration and carbon storage in Alaska and other areas in the United States. His published work on the integrated 3D seismic attributes and petrophysics revealed the heterogeneities of the low-resistivity Nanushuk-Torok shaly sand reservoirs on the North Slope, Alaska, which became the most downloaded article in the Interpretation journal in 2020. These reservoirs are touted as major recent discoveries in the frontier. In 2020, he joined the Bureau of Economic Geology at the University of Texas at Austin as a Research Associate. He is busy carrying out quantitative 3D seismic interpretation and petrophysical analysis and developing new concepts and workflows for unconventional reservoirs, geothermal energy, and carbon dioxide and hydrogen storage. These are critical to mitigating climate change.

Shuvajit’s recent work on multivariate unsupervised time series clustering and ensemble class-based machine learning has shown potential solutions to some fundamental challenges of using machine learning with borehole geophysical data, such as attribute interdependence, cyclicity, and multimodality. Shuvajit’s approach (Toeplitz inverse covariance-based clustering with adaptive window) incorporates interdependence among attributes, which is fundamental and often ignored.

Shuvajit’s research is of the highest caliber and has been published 16 times in some of the leading geophysics journals and in more than 50 conference papers and abstracts, which many others have cited. More recently, Shuvajit has published two books, A Primer on Machine Learning in Subsurface Geosciences and Advances in Subsurface Data Analytics, which will help professional geoscientists understand and appreciate current trends and future potential in machine learning approaches applied to the geosciences.

Besides the high-quality technical work, Shuvajit has generously devoted his time to professional outreach and volunteering. He has been deputy associate editor for Interpretation since 2020 and has been the associate and assistant editor for three special issues of this journal.

Achieving all the above-mentioned accomplishments early in his career can only be a sign of what more Shuvajit will show us in the future. I have no doubt that he will continue to contribute to the constantly evolving and highly interesting field of subsurface geoscience. His significant scientific contributions and achievements deserve encouragement and recognition from peers. The 2022 SEG J. Clarence Karcher Award is a fitting recognition of the outstanding contributions Shuvajit has made so far and will encourage him to explore and address the challenges that are being faced by practitioners in our industry.