ERL Current Projects

Advanced Carbon Mineralization Initiative (ACMI)

Advanced Carbon Mineralization Initiative (ACMI)

To meet 1.5-2 degrees Celsius warming targets, society must develop carbon management technologies for the irreversible removal and storage of gigatons of CO2 from point sources and the atmosphere. The conversion of carbon dioxide into carbonate minerals, “CO2 mineralization,” can play a fundamental role in carbon management technologies, as it is thermodynamically favorable and scalable. However, the long timescales and poor predictability of CO2 mineralization impede widespread utilization. There is thus an urgent need to accelerate mineralization kinetics and capacity. We believe we can lay the foundations for enhanced mineralization technologies by developing and synthesizing mechanistic understanding from geology, chemistry, and biology. The Advanced Carbon Mineralization Initiative is a cross-disciplinary effort to establish the basic science, technology, and economic framework to realize the full potential of CO2 mineralization.

Sponsored by: Chan-Zuckerberg Initiative

ERL Personnel: Matěj Peč (lead), Bradford Hager, Oliver Jagoutz, Shuhei Ono, Tanja Bosak, Angelina Serafini, Manlin Xu, Andy Fong, Jonathan Simpson, Hoagy O’Ghaffari, Gage Coon

Additional collaborators: Yogesh Surendranath (lead), Martin Bazant, Emre Gencer, Admir Masic, Yuriy Roman, Shuguang Zhang (MIT), George Church (Harvard Medical School), Peter Keleman (Columbia Climate School), Loren Looger (University of California, San Diego)

CO2 Migration along Faults in CCS Projects: Field Validation and Stochastic Modeling of Fault Properties

CO2 Migration along Faults in CCS Projects: Field Validation and Stochastic Modeling of Fault Properties

A proper assessment of CO2 migration along and across faults is a key requirement for site selection, injection management, and design of a monitoring, mitigation, and verification (MMV) program. As part of this project, we have developed numerical capabilities aimed at addressing this technical challenge. The capabilities include (i) a novel algorithm to determine fault permeability and its distribution on two- and three-dimensional faults based on local stratigraphy and fault throw (PREDICT), (ii) a coupled reservoir-geomechanical modeling approach to simulate CO2 injection and migration along faults, and (iii) the application of this capability to case studies in offshore sedimentary formations. In this continuing collaboration, we are capitalizing on these forward modeling capabilities and tools to achieve two goals: (1) validate the PREDICT model with datasets of fault transmissibility in lightly-lithified siliciclastic clay-sand sequences; and (2) develop quantitative tools for end-to-end uncertainty quantification of CO2 storage security in realistic geologic settings. We anticipate that the outcomes of this project will be key for site selection, injection operations, monitoring design and, of particular importance, influence regulators and public acceptance.

Coupled flow-geomechanical models applied to assess earthquake triggering in tectonically active regions – The Los Angeles basin, CA

Earthquakes caused by human activities are a growing societal concern affecting hydrocarbon and geothermal energy production, gas storage, and subsurface carbon sequestration efforts in the Unites States and throughout the world. Distinguishing between tectonic and induced earthquakes in tectonically active regions is extremely challenging. Moreover, induced seismicity in these regions pose greater risks, as human activities may trigger larger and more destructive earthquakes. Our project is developing state-of-the-art, physics-based models to investigate how nearly a century of production and waste-water injection in hydrocarbon fields of the Los Angeles basin, California, have impacted the stability of faults in the area. These models consider stress changes on faults caused both by tectonic and anthropogenic processes, thus helping to distinguish between tectonic and induced events. The project will advance methodologies to investigate and manage triggered seismicity in Los Angeles and other tectonically active regions.

Sponsored by: NSF Geology and Geophysics

ERL Personnel: Ruben Juanes (PI), John Shaw (PI at Harvard), Lluis Salo-Salgado

Critical Minerals of the Laramide Porphyry Belt, Southwest USA.

Critical Minerals of the Laramide Porphyry Belt, Southwest USA.

The U.S.–Mexico borderlands, spanning Arizona, New Mexico, Texas, Sonora, Chihuahua, and Sinaloa, make up one of the world’s most important copper regions. Alongside copper, these porphyry systems also contain a wide range of critical minerals such as gallium, indium, cobalt, tellurium, tungsten, and platinum-group elements—materials essential for clean energy, electronics, and advanced technologies.

Despite their importance, the distribution of these critical minerals is still poorly understood. Existing geochemical data are incomplete, inconsistent, and vary in quality, making it difficult to evaluate the full potential of these mineral systems. A clearer, more systematic picture is needed to guide future research and resource assessments. This project aims to fill that gap by compiling past data, collecting new samples, and applying modern analytical techniques to create the first standardized geochemical database of the region. The results will provide a better understanding of where critical minerals occur in porphyry copper systems and help identify areas with the greatest potential for future exploration and development.

Sponsored by: USGS Earth MRI (Award number: G23AC00054)

ERL personnel: Hervé Rezeau

Collaborators: Carson Richardson (Lead; Arizona Geological Survey), Victor Garcia (Arizona Geological Survey), Virginia T. McLemore (New Mexico Tech), Nels Iverson (New Mexico Tech)

Electric stimulation model development

Electric Stimulation Model Development

Conventional geothermal energy needs pre-existing fracture networks to conduct heat from rock to water and thence convect heat to surface turbines or other uses. Very few locations, less than 10% of Earth’s land area provide this. Enhanced geothermal systems (“hot dry rock”) can be created anywhere, but require new networks to be fractured. Hydrofracturing uses enormous amounts of pressurized water to fracture rock. Electrofracturing creates an electric plasma channel, essentially subterranean lightning, to fracture rock. Replacing most of the hydrofracturing by electrofracturing promises to save hundreds of millions of liters of water per typical well. This project models the plasma-channel effect by numerically simulating electric-current generation and heating, and nonlinear feedback of voltage and temperature on electric conductivity. It also models the advection of proppant in fracture liquids, and how proppant prevents fracture closure.

Sponsor: Eden GeoPower, Inc

ERL Personnel: Aimé Fournier (PI), Laurent Demanet

Collaborators: Davis Evans, Chunfang Meng, Adrian Moure, Paris Smalls (Eden); Robert Egert, Wencheng Jin, Vuong Van Pham, Ming Yang, Chunhui Zhao (Idaho National Laboratory)

Developing Adaptive Traffic Light Systems For Enhanced Geothermal Systems

Developing Adaptive Traffic Light Systems For Enhanced Geothermal Systems

Induced seismicity is both a valuable reservoir management tool and a significant challenge, particularly for enhanced geothermal system (EGS) projects. While carefully managed seismic activity can help create and maintain fluid pathways, large earthquakes—such as those that occurred at the Basel and Pohang EGS sites—have raised public concern and highlighted the risks of large-scale deployment.

Beyond avoiding damaging earthquakes, geothermal operators also aim to keep reservoir stimulation within a defined radius—typically a few hundred meters around the injection well—to maximize heat extraction from a controlled volume of rock. Effective seismic monitoring must therefore balance two key objectives: mitigating the risk of large events and optimizing reservoir performance.

Our research focuses on developing anadaptive traffic light system that combines seismicity forecasting, ground motion modeling, real-time observations, and machine-learning techniques. This integrated approach is designed to improve seismic hazard management. The system is being tested and calibrated using both natural seismicity and controlled experimental data.

Modeling Supershear Earthquakes to Forecast Induced Seismicity Hazard

While the potential for subsurface fluid injection and extraction to trigger earthquakes has long been recognized, the sharp increase in the extent and vigor of injection-induced seismicity calls for much deeper understanding than is currently available. One of the features of some highly destructive earthquakes is their supershear rupture propagation, with velocities faster than the shear wave speed that typically lead to large magnitude events. The intensity and the patterns of strong ground motion for supershear earthquakes have been shown to be inherently different from those of regular (sub-Rayleigh) ones, calling for a need to elucidate the controlling factors behind rupture speed to understand. Fluids fill the pore space of crustal rocks, and pore pressures are essential to understand the stability of geologic fault. However, poro-mechanical effects are often neglected in the analysis and interpretation of earthquake rupture speeds. Here we address the overarching question: what is the role of pore fluids in the generation of supershear earthquakes? We break this down into two fundamental questions: (1) How does pressurization rate from injection impact rupture speeds? (2) Can heterogeneity in frictional properties elicit a transition between rupture regimes (e.g., from slow-slip, to regular earthquake, to supershear earthquake)? We address these fundamental questions through a combination of high-resolution computational modeling and theoretical stability analyses that probe the regime transitions of the coupled fluid/porous solid system. We anticipate that our results will shed new light in our understanding of supershear earthquakes, paving the way for improved forecasting of earthquake hazards.

The Origin and Timing of Silver Enrichment at the world-class Filo Del Sol Cu-Au-Ag deposit

As the world transitions to renewable energy, the demand for metals like silver is soaring. Silver is essential for technologies like solar panels and batteries, securing its place among critical minerals. High-sulfidation epithermal deposits are key sources of silver, gold, and copper, but understanding where and how silver-rich zones form remains a scientific challenge. At the newly discovered Filo Del Sol deposit in Argentina, researchers are using cutting-edge techniques to analyze the chemistry and age of minerals to uncover the origins of silver. This is the first time such methods are being applied together at this scale in these types of deposits aiming to characterize the precise conditions that drive silver enrichment. Insights from this work will help design appropriate mineral exploration strategies globally, including in the U.S. The project includes hands-on training for students in advanced geoscience methods, helping prepare the next generation of experts for careers in clean energy and mining.

Sponsored by: NSF Petrology and Geochemistry

ERL personnel: Hervé Rezeau (Lead)

Collaborators: Brian Jicha (University of Wisconsin-Madison), Michael Pribil (US Geological Survey), Camila Sojo (PhD student, University of Arizona), Vicuña Corp. (joint arrangement between Lundin Mining and BHP)

Seismic Imaging, Inversion and Uncertainty Quantification

Seismic Imaging, Inversion and Uncertainty Quantification

The algorithmic workhorse for creating a map of the subsurface from seismic surveys is full waveform inversion. Many fundamental questions remain wide open about it, including how to deal with the lack of convexity in optimization, how to make use of AI to speed it up or supersede it, and how to quantify the uncertainty inherent in its predictions. Among the various projects that the Imaging and Computing group has led over the years, one current topic of great interest is the convergence of AI and uncertainty quantification. We provide the first method for sampling the “epistemic” posterior of a neural network that is consistent with the Bayesian setting. The upshot is for the practitioner to have correct, not arbitrary, error bars on the seismic image.

Through-Casing Permeability Estimation: Feasibility in Simulations, Field Data and Lab Experiments

There are hundreds of thousands of inactive oil & gas wells in the USA, millions in the world that all need to be plugged and abandoned. In that process, environmental regulators require operators to certify that no well will emit methane gas or other harmful fluids. Key emission-risk factors include the Darcy permeability of rock-formation layers, the contact quality of the steel well casing with the formation, and the presence of cracks in the formation. It is prohibitively expensive to inspect the formation directly by perforating the casing, injecting fluids etc. Measurement tools in the well can transmit acoustic, electromagnetic, and other energy forms into the formation and receive signals back, but with strong attenuation by the casing. This project uses mathematical analysis, numerical simulation, lab experiments, and well-log data to study how to infer permeability and other risk factors (and quantify their uncertainty) from well logs.

Understanding the geological conditions for hydrogen production during seafloor serpentinization in a fossil hyperextended margin

This project focuses on understanding the geological conditions for the generation of hydrogen gas during mineral transformations in ultramafic rocks situated in hyperextended passive continental margins. We sample the fossil Ocean-Continent Transition Zone of the Jurassic Tethys Ocean exposed in Italy and Switzerland, to study the role of geological structures, especially oceanic detachment faults, and petrologic variability of protolith rocks, on oxygen fugacity and hydrogen content. This will provide insights into the distribution and budget of hydrogen in a natural setting. We also conduct high-pressure high-temperature experiments aimed at simulating the hydrogen-producing reactions in the laboratory under well constrained boundary conditions.

Sponsor: MITEI Geologic Hydrogen Consortium

ERL Personnel: Oliver Jagoutz, Matej Pec, Dominic Hildebrand, Gage Coon