Subsurface Solutions
Primary Research Goals:
Leaders: Noami BonessTapan MukerjiTony Kovscek
- Underground molecule storage
- Natural hydrogen deposits
- Co-located energy production and CO2 storage
Faculty & Researchers








Students
Research Groups
Stanford University Petroleum Research Institute Well Test Interpretation (SUPRI-D)

SUPRI-D investigates and supports novel approaches to the interpretation of oil, gas, geothermal, and water well tests. Backed by a wealth of information thanks to modern computerization and Big Data, well test analysis and design have greatly increased the reliability of test results for far less cost.
Stress and Crustal Mechanics Group

The Stress and Crustal Mechanics Group uses knowledge of the state of stress in the Earth and the mechanical properties of Earth materials to investigate a variety of geophysical problems. These problems cover a variety of scales, ranging from pore-scale processes and the mechanical behavior of reservoir-scale to the strength of the lithosphere and the mechanics of major plate-bounding faults such as the San Andreas. Our group conducts basic and applied research in the areas of reservoir geomechanics, and the physics of friction and faulting. We treat the Earth's crust as a natural laboratory, using a combination of stress and strain data obtained from boreholes, GPS measurements, and earthquake focal mechanisms to test theories about the behavior of the lithosphere. Our group is heavily engaged in applying these methodologies toward optimization of production from gas shale research and CO2 sequestration.
Related Publications
- Zoback, M., & Smit, D. (2023). Meeting the challenges of large-scale carbon storage and hydrogen production. PNAS, 120(11). https://doi.org/doi: 10.1073/pnas.2202397120
- Shi, L., Mach, K., Suh, S., & Brandt, A. (2022). Functionality-based life cycle assessment framework: An information and communication technologies (ICT) product case study. Wiley, 19.
- Orsini, R., Brodrick, P., Brandt, A., & Durlofsky, L. (2021). Computational optimization of solar thermal generation with energy storage. Elsevier, 47, 101342.
- Nie, Y., Zamzam, A., & Brandt, A. (2021). Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks. Pergamon, 224, 341-354.
- Das, V., Pollack, A., Wollner, U., & Murkeji, T. (2020). Convolutional neural network for seismic impedance inversion.
- Rostami, E., Boness, N., & Zoback, M. (2020). Significance of Well Orientation on Cumulative Production From Wells in the Bakken Region.
- Al, J. E. (2020). Reduced-Order Modeling of Coupled Flow and Quasistatic Geomechanics.
- G., M., T., M., & J., D. (2020). The Rock Physics Handbook, 3rd Ed..
- K., F. R. M. E. M. L. R. (2019). Compact models for adaptive sampling in marine robotics.
- J., D. M. E. (2019). Value of information analysis for subsurface energy resources applications.
- A., W. K. H. T. (2019). Sequential-implicit Newton method for multiphysics simulation.
- G., A. I. K. M. M. (2019). Particula: A simulator tool for computational rock physics of granular media.
- Teichgraeber, H., & R.Brandt, A. (2019). Clustering methods to find representative periods for the optimization of energy systems: An initial framework and comparison. Applied Energy.
- R., K. K. (2019). The Effect of Voidage-Displacement Ratio on Critical Gas Saturation.
Subsurface Solutions Annual Report
Stanford Natural Gas Initiative seed funded projects require annual submissions of brief technical progress reports and interim report summaries for active projects. Closed projects require a technical report and final report summary one year after the award close date.
Read the most recent interim report (Coming soon)