Past Event: Oden Institute Seminar

Numerical Simulations of Seismic Wave Propagation in Fractured Media and Parameter Estimation

Janaki Vamaraju, Machine Learning Scientist, Data Science RD and Machine Learning, US Shell Global Solutions US Inc

Abstract

Natural fractures are frequently observed in rocks at all scales. Their characterization is critically important not only for drilling, well completion and reservoir management but also for hydrocarbon exploration. Since fractures greatly influence the porosity and permeability of a reservoir, several techniques have been developed to identify and characterize them. In principle, seismic data can be effective to locate and characterize these fractures because of the sensitivity of wave velocities, amplitudes and spectral characteristics to fracture compliances. However, the detection of subsurface fractures from seismic data is a challenging problem. Besides the low seismic resolution and the complexities of geological bodies in the subsurface, the main contributing causes for the problems are the challenge of understanding the characteristic response of fractures in seismic data and the correct estimation of fracture properties from seismic data. Therefore, the motivation is to develop novel numerical methods that can accurately model seismic waves in presence of fractures and are computationally very efficient. Specifically, my talk will be about the following: a) Development of enriched/hybrid finite elements to model seismic wave propagation in fractured elastic media at reduced computational costs (forward problem). b) Modeling seismic wave propagation in fractured poroelastic media to examine the effects of fluid filled cracks and pores on scattering (forward problem). c) Finally, to estimate fault networks (seismic migration) from synthetic seismic data, we employ mini-batch stochastic inversion strategies (inverse problem). Bio Janaki Vamaraju recently earned a Ph.D. in Computational Geophysics from the University of Texas at Austin in 2019. Previously, she also received M.Sc. and M.S. degrees in Mathematics and Computational Mechanics. She joined Shell Research in 2020 as a machine learning scientist. Her research interests include numerical methods, inverse problems, and applied machine learning. **Note: Please join this Zoom seminar online with the "Audio Only" function (no video)**