Siddharth Misra is an Associate Professor in Harold Vance Department of Petroleum Engineering at Texas A&M University. Misra holds a PhD in Petroleum Engineering from The University of Texas at Austin. Prior to that, from 2007 to 2010, he worked as a Wireline Field Engineer in Saudi Arabia, Egypt, and in the United States with Halliburton. He received his undergraduate degree in Electrical Engineering from Indian Institute of Technology Bombay, India, in 2007. Recently, he was awarded the prestigious Department of Energy Early Career Award, the American Chemical Society New Investigator Award, the SPE Mid-Continent Formation Evaluation Award, the SEG Clarence Karcher Award, and the SPWLA Young Professional Technical Award. His research interests include subsurface characterization, machine learning, sensing and sensors, and inverse problems.
SEG J. Clarence Karcher Award 2020
Siddharth Misra is a researcher and educator across the disciplines of formation evaluation, petrophysics, geophysics, and subsurface data analytics. His current research is focused on data-driven fracture characterization and quantification of fluid storage and connectivity. He has advanced new laboratory methods for the electromagnetic sensing of rocks as well as new machine-learning procedures for the interpretation of geophysical subsurface measurements. Misra has published in more than 35 journal and 25 conference proceedings and is coauthor of four patents and four patent applications related to subsurface geophysical sensing. Based on his innovative technical contributions in exploration geophysics and petrophysics, Misra was awarded the prestigious Department of Energy Early Career Award for Geosciences Research, the SPWLA Young Technical Professional Award, the American Chemical Society New Investigator Award, and the SPE Mid-Continent Formation Evaluation Award.
Biography Citation SEG J. Clarence Karcher Award 2020
Siddharth (Sid) Misra is an associate professor with the Harold Vance Department of Petroleum Engineering at Texas A&M University. Prior to that, he was an assistant professor with the Mewbourne College of Earth and Energy at the University of Oklahoma. He develops computational methods to quantify the interaction of sensor physics with physical processes/properties of the subsurface to improve the exploration, development, and production of subsurface resources. As part of the latter work, Sid pioneered several geophysical methods and interpretation techniques that directly benefit the development and production of hydrocarbon reservoirs.
Thus far, Sid’s major technical contribution is in the theory of electromagnetic (EM) responses of geologic formations due to charge polarization phenomena. He conducted laboratory experiments, mechanistic modeling, and numerical simulations of instrument physics to quantify changes in electrical conductivity and dielectric permittivity of fluid-filled porous rocks due to polarization phenomena associated with clay and electrically conductive minerals. His research group introduced several techniques for processing multifrequency EM borehole measurements for the estimation of fluid saturations and to detect and quantify fractures and clay minerals in hydrocarbon-bearing formations.
During the last five years, Sid has been involved in the implementation of machine-learning methods for the interpretation of subsurface geophysical measurements. Funded by the U.S. Department of Energy, he developed techniques to process acoustic-emission waveforms and sonic traveltimes for the detection of geomechanically altered zones due to fractures and fluids. Furthermore, as part of his financial support received from the American Chemical Society, he develops machine-learning techniques for analysis of 2D/3D images to quantify connectivity of various rock solid and fluid constituents. The assessment of connectivity of material constituents enables better understanding of the transport of energy, mass, and momentum in porous rocks. A common thrust in his data-driven research efforts is to improve and expedite the interpretation of geophysical measurements and better visualize geophysical processes via advanced machine-learning methods.
Sid’s research results are being used currently by several oil and gas companies, including Chevron, Baker-Hughes, GE, Saudi Aramco, Hess, Schlumberger, and BHP Billiton. He is actively pursuing several impactful research projects in the area of machine learning applied to near-wellbore rock description using borehole geophysical measurements. His most recent research findings and interpretation workflows are documented in his book, Machine Learning for Subsurface Characterization. The book describes and implements data-driven interpretation methods and concepts needed to support today’s complex subsurface engineering problems.
I find it extremely fitting that SEG’s J. Clarence Karcher Award be bestowed to such an active, driven, creative, and intelligent young member of our academic community. I strongly believe that this is only the humble beginning of a very productive and transcendental geophysical career for Sid.