INGENUITY
Next-Gen Nuclear Waste Disposal Internship 
Summer 2025

Machine learning (ML) can be a practical toolset to predict a range of multi-physics behavior in the subsurface energy geosciences. Previously, we used machine learning to identify multiscale geological features from Mt Terri URL (a 2022 Ingenuity project), to predict fault slip behavior induced by fluid injection in an experiment conducted in Mt Terri URL, and to predict wastewater injection induced seismic events in space and time in Oklahoma at the basin scale (an LBNL-CSUEB Intern project). We also made a first attempt to predict microearthquakes from hydraulic stimulations conducted at the EGS Collab (a 2024 Ingenuity project). 

In this project, we look for a skilled student to carry out machine learning analysis for understanding and predicting fractures and fault behavior from meso (m) to repository (km) scale. The goal is to quantify the fault and fracture responses to engineering loading such as hydraulic loading in geothermal reservoirs and thermal loading in nuclear waste repositories.  Detailed technical goals will be set based on the background and interest of the successful candidate.

• Desired Education Level: PhD student

• Mentors: Mengsu Hu (mengsuhu@lbl.gov) 

Gas flow through the Earth is critically important in subsurface engineering activities, such as nuclear waste disposal, carbon sequestration and hydrogen storage. Subsurface gas migration involves complex processes, including multiphase fluid flow with gas breakthrough, phase change expansion, pressure buildup, as well as potential gas fracturing with rapid gas release that could potentially be catastrophic. 

In this project, we look for a skilled student to conduct numerical simulations of deep subsurface gas flow in various types of host rocks. A detailed program will be designed together with the candidate depending on the candidate’s background and interest. 

• Desired Education Level: PhD student

• Mentor: Jonny Rutqvist (jrutqvist@lbl.gov)

Clayey materials are important in engineered barrier systems (EBS) for potential U.S. DOE nuclear waste repository sites. For this summer project, we are looking for a highly skilled and motivated student to conduct laboratory experiments to determine how water chemistry influences swelling pressure, microstructural and ion transport in compacted clay system. 

The student will be provided with an excellent opportunity to engage in the frontiers of research of geologic nuclear waste disposal, with a multidisciplinary team of geochemists, hydrologists, and computational geoscientists.

• Desired Education Level: Undergraduate, Masters or PhD student

• Mentor: Wenming Dong (wenmingdong@lbl.gov)

Bentonite pellet/powder mixtures have been applied as an Engineered Barrier System for high level radioactive waste underground repository. The two sets of bench-scale column tests at LBNL using bentonite powder vs. pellet/powder mixture have shown considerably different hydration and swelling processes. The observed faster hydration in the pellet/powder bentonite mixture indicates the impacts of heterogeneity and potential fast flow path developed during hydration. 

In this project, you will be investigating the heterogeneous hydration in bentonite pellet/power mixtures, by preparing column samples (5 cm diameter by 10 cm long), conducting experiments, and assisting with image data acquisition and analysis. You will be introduced to the fascinating X-ray CT scanner housed at LBNL, and work on CT images obtained for scientific research, rather than medical diagnosis you may have seen. Supported by LBNL scientists, you will also have the opportunities to access the science behind these images, dig deeper using dedicated software and develop your own code to help interpretations.

• Desired Education Level: PhD student

• Mentor: Chun Chang (chunchang@lbl.gov)

This project will expose interns to first principle calculations of the free energies of reactions in the gas and solution phases. We will use a range of quantum chemistry methods ranging from the density functional theory to post-Hartree-Fock methods. The intern will learn state-of-the-art computational tools and apply them to solve urgent environmental problems.

• Desired Education Level:  Community college, Undergraduate, Masters, or PhD student 

• Mentor: Piotr Zarzycki (ppzarzycki@lbl.gov)

The CI-D Experiment is a field test performed at the Mont Terri rock laboratory (Switzerland) to understand the migration of radionuclides in clay formations. In this project, we will extend our efforts on the CI-D experiment by taking a broader view of radionuclide migration. We will continue to focus on anions, which are arguably the biggest risk drivers for radionuclide migration from deep geological storage of nuclear waste.  We will work with the project intern to further these efforts, beginning with a publication of the CI-D modeling undertaken with Carl Steefel and Christophe Tournassat.

• Desired Education Level: PhD student

• Mentor: Carl Steefel (cisteefel@lbl.gov)

Cement is used as a liner in the emplacement tunnel of the geological repository for high-level radioactive waste. The interaction between cement and bentonite has a profound impact on the repository's long-term safety. 

In this project, the intern will conduct a reactive transport model with the mentors to study the alteration of bentonite and concrete. If time permits, migration of radionuclides can be added to the model to study the transport of radionuclides through bentonite and cement.  The intern will learn the geochemistry of cement and bentonite and LBNL in-house reactive transport code, TOUGHREACT, and contribute to the technical report and journal articles.

• Desired Education Level: Masters or PhD student 

• Mentors: Omotayo Omosebi  (oaomosebi@lbl.gov) & LianGe Zheng (lzheng@lbl.gov)