CouFrac 2022 Conference

lbearson

Chin-Fu Tsang Coupled Processes Award Lecture

· ·

Chin-Fu Tsang Coupled Processes Award Lecture

Predicting Transport, Mixing, and Reaction in Fractured Porous Media

Peter K. Kang

Department of Earth and Environmental Sciences, University of Minnesota, Twin Cities

Biosketch

I am a geoscientist whose research focuses on the physics of flow and reactive transport in porous and fractured media. My research group combines high-performance numerical simulations, visual laboratory experiments, stochastic upscaling, and machine learning to elucidate how the coupling between multiple processes controls transport processes in fractured porous media (https://pkkang.com).

I joined the Department of Earth and Environmental Science at the University of Minnesota as an Assistant Professor and a Gibson Chair of Hydrogeology in 2018. I was a researcher at Korea Institute of Science and Technology (KIST) from 2015-2018, and was a postdoctoral associate in the Earth Resources Laboratory (ERL) at MIT before joining KIST. I received my MSc (2010) and PhD (2014) in Civil & Environmental Engineering at MIT, and obtained BSc of Civil, Urban & Geosystem engineering at Seoul National University in South Korea with summa cum laude in 2008.

Introduction of the Lecture

Fluid flow and reactive transport in geologic fractures control many critical natural and engineered processes in the subsurface. For example, 99% of global unfrozen freshwater is stored in groundwater systems, and groundwater flow is often dominated by fracture flows. Also, engineered carbon mineralization is considered a key solution for climate change, and fractures serve as highways for the delivery of CO2 into mafic and ultramafic rocks, determining the efficiency of carbon mineralization. However, predicting transport processes in fractured porous media is challenging due to the multi-scale heterogeneity inherent to subsurface systems and the strong coupling between processes (i.e., coupled thermo-hydro-mechanical-biological-chemical processes).

In this talk, I will present how my research group uses cutting-edge methods to advance our fundamental understanding of coupled processes as well as our capacity to predict transport processes in fractured porous media. In particular, I will highlight the role of fluid inertia and pore-scale flow structures on transport, mixing, and biogeochemical reactions and our efforts to upscale transport processes in fractured porous media. The talk will conclude by sharing our ongoing efforts to extend the research findings to fractured aquifer sites.

Harrison Lisabeth

· ·

Low-Frequency Nonlinear Elasticity: a Powerful Tool for Probing Fractures

Harrison Lisabeth

Lawrence Berkeley National Laboratory, USA

Biosketch

Harry Lisabeth is a Research Scientist in the Energy Geosciences Division at Lawrence Berkeley National Laboratory (LBNL). He received AB’s in Geological Sciences and Literary Arts from Brown University in 2010 and a Ph.D. in Geology from the University of Maryland, College Park in 2016.  His graduate work focused on the effects of chemistry on rock deformation.  From 2016 to 2018 he was a Postdoctoral Research Fellow at Stanford University working with the Stanford Center for Carbon Storage (SCCS) and the Stress and Crustal Mechanics Lab (SCML).  Harry’s research focuses on the interaction between chemistry and materials under stress and utilizing novel experimental tools to characterize material behavior under conditions relevant to the Earth.  Specific research applications range from Geological Storage (GS) and geothermal energy to planetary geophysics.

Introduction of the Lecture

Wave propagation in rocks is typically treated as a linear elastic phenomenon; however, as strain increases, nonlinear stress-strain behavior can result.  Experimental evidence for nonlinear elastic behavior in geomaterials has existed for years, but the ability to use this property as a diagnostic tool has only begun development in the last several decades.  The basis of this tool is that the degree of nonlinearity in fractured material is much greater than that in intact material, resulting in signals that are highly sensitive to the state of the fracture (stress, chemistry, fluids).  Elastic nonlinearity can cause a propagating wave to distort, resulting in generation of harmonics, multiplication of waves of different frequencies and, under resonant conditions, shifts in resonance frequency peaks.  One way to exploit these behaviors is to propagate waves of differing frequencies through a material and observe nonlinear wave mixing phenomena, a technique referred to in the literature as nonlinear wave modulation spectroscopy (NWMS).  When two waves propagate colinearly through a nonlinear material, nonlinear wave mixing manifests as wave distortion, harmonic generation and the creation of sum and difference frequencies (sidebands).  I’ll discuss the results of a NWMS study of fractured rocks at a range of conditions to highlight the strain (10^-6 – 10^-5), frequency (0.001 – 100 Hz), normal stress (0.1 – 10 MPa) and fluid dependence of nonlinear parameters in rocks. Analysis of various dependencies can provide insight into the mechanisms of nonlinearity and has the potential to provide methods of monitoring damage processes in the subsurface.

David D. Nolte

· ·

Neural Network Simplex Encoders to Monitor Fracture Processes using Transportable Acoustic Sources

David D. Nolte

Purdue University, USA

Biosketch

David D. Nolte is the Edward M Purcell Distinguished Professor of Physics and Astronomy at Purdue University performing research in the fields of optical interferometry and holography.  He received his baccalaureate from Cornell University in 1981, his PhD from the University of California at Berkeley in 1988 and was a post-doctoral member of AT&T Bell Labs before joining the physics faculty at Purdue.  He has been elected Fellow of the Optical Society of America, Fellow of the American Physical Society and Fellow of the AAAS. In 2005 he received the Herbert Newby McCoy Award of Purdue University.  He has founded two biotech startup companies in the area of medical diagnostics.  David is the author of four books, and he blogs regularly on topics of nonlinear physics at https://galileo-unbound.blog/

Introduction of the Lecture

Transportable acoustic sources, known as chattering dust, are a novel geophysical tool to study laboratory-based fracture properties and processes.  These sources mimic natural acoustic emission with the added advantage that they can transport through fractures and fracture networks.  A challenge posed by the chattering dust is the uncontrolled character of the acoustic signals that masks arrival-time and amplitude information, which are usually the two most valuable information channels for seismic characterization of fractures.  Fortunately, late-arriving coda waves caused by multiple scattering in fracture networks provide subtle information channels that can be extracted using machine learning to identify fracture processes. 

This talk will describe the design and performance of a general class of neural networks called simplex encoders.  These encoders reduce the dimensionality of the dataset and accurately classify fracture saturation properties deduced from the chattering dust data.  One type of simplex encoder, the so-called Siamese neural network, has gained popularity for its ability to discern subtle differences among different classes of behavior.  The classification accuracy of the Siamese encoder applied to the fracture data will be related to more general simplex encoders.  Simplex encoders are a potentially powerful tool for the study of coupled processes in the earth sciences and engineering.

Hari Viswanathan

· ·

A Multi-Scale Experimental and Simulation Approach for Fractured Subsurface Systems

Hari Viswanathan

Los Alamos National Laboratory, USA

Biosketch

Dr. Viswanathan has advanced degrees in chemical (B.Sc.) and environmental engineering (M.S., PhD). He is an expert in subsurface flow and transport modeling and is a Senior Scientist in Earth and Environmental Sciences Division at Los Alamos National Laboratory. He has worked across multiple scales to study subsurface flow and transport in fractured and porous media. Viswanathan has over 150 publications in the area of energy and global security with an h-index of 46 with over 7000 citations and has large multi-disciplinary projects such as reducing the water footprint of hydraulic fracturing operations. He was worked on a wide range of applications such as nuclear waste disposal, carbon sequestration, unconventional oil and gas and nuclear nonproliferation.

Introduction of the Lecture

Fractured systems play an important role in numerous subsurface applications including hydraulic fracturing, carbon sequestration, geothermal energy and underground nuclear test detection. Fractures that range in scale from microns to meters and their structure control the behavior of these systems which provide over 85% of our energy and 50% of US drinking water. Determining the key mechanisms in subsurface fractured systems has been impeded due to the lack of sophisticated experimental methods to measure fracture aperture and connectivity, multiphase permeability, and chemical exchange capacities at the high temperature, pressure, and stresses present in the subsurface. In this study, we developed and use microfluidic and triaxial core flood experiments required to reveal the fundamental dynamics of fracture-fluid interactions. In addition we have developed high fidelity fracture propagation and discrete fracture network flow models to simulate these fractured systems. We also have developed graph-based machine learning emulators of these fracture simulators in order to conduct uncertainty quantification and enable near real-time analysis for these systems. We describe an integrated experimental/modeling multi-scale approach that allows for a comprehensive characterization of fractured systems and develop models that can be used to optimize the reservoir operating conditions over a range of subsurface conditions.

H. Viswanathan, J.W. Carey, L. Frash, S. Karra, J. Hyman, Q. Kang, E. Rougier, G. Srinivasan

Nantheera Anantrasirichai

· ·

Machine learning for monitoring ground deformation with InSAR data Nantheera Anantrasirichai

Nantheera Anantrasirichai

University of Bristol, UK

Biosketch

Dr Nantheera Anantrasirichai is a Senior Lecturer (Assoc. Prof.) in the Department of Computer Science at University of Bristol. Previously she was a Senior Research Fellow associated with the Bristol and Bath Creative Industry Cluster working on AI-enable production, and a Fellow of the Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET). Her research interests include AI-based production, image analysis and enhancement, medical imaging, and remote sensing. She has contributed to many projects across multiple disciplines, including engineering, psychology, biology and the creative arts. She has developed state-of-the-art methods for denoising long-range imagery distorted by atmospheric turbulence and for detecting ground deformation globally in noisy satellite imagery.

Introduction of the Lecture

Interferometric synthetic aperture radar (InSAR) can be used to measure ground displacement over large geographic areas. However, while detecting deformation using InSAR images is conceptually straightforward, it is difficult to automate, particularly in dense vegetation areas and where the signals are sparse. In this talk, a simple but efficient machine learning framework based on modern deep learning to detect ground deformation will be present. The model can be applied to individual interferograms for rapid deformations and can be tested on time-series for slow and sustained ones. The image processing techniques to deal with difficult, noisy, and sparse signals will also be addressed.  This talk will analyse based on two used cases: i) the system for globally monitoring volcanic unrest and ii) the system for detecting ground deformation in the built environment.