Stanford Reservoir Simulation Project

Stanford Reservoir Simulation Project
Name	Stanford Reservoir Simulation Project
Established	20XX
Institution	Stanford University
Field	Reservoir simulation, hydrology, petroleum engineering
Director	[Redacted]
Location	Stanford, California

Stanford Reservoir Simulation Project is a multidisciplinary initiative based at Stanford University that develops computational models for subsurface fluid flow in reservoirs for applications across California, Gulf of Mexico, North Sea, and international basins. The project integrates methods from Department of Energy, Sandia National Laboratories, Lawrence Berkeley National Laboratory, and industrial partners such as ExxonMobil, Shell plc, Chevron Corporation to advance predictive capabilities for enhanced recovery, carbon storage, and groundwater management. Its outputs have informed work referenced by International Energy Agency, Intergovernmental Panel on Climate Change, and regulatory agencies.

History

The project originated from collaborations between the Stanford School of Earth, Energy & Environmental Sciences and the Stanford Center for Reservoir Forecasting following investments by the U.S. Department of Energy and grants from the National Science Foundation. Early milestones included adoption of techniques developed in the SPE community and integration of numerical schemes from research groups at MIT, University of Texas at Austin, and Imperial College London. Workshops and symposia were held with participants from Society of Petroleum Engineers, American Geophysical Union, and European Geosciences Union, while pilot studies partnered with field operators in basins such as Permian Basin, Sintef projects in North Sea, and research sites monitored by USGS.

Objectives and Scope

The project's stated objectives encompass improving forecasting for enhanced oil recovery, geologic carbon storage, and aquifer management using high-resolution simulations. Scope includes coupling of multiphase flow, geomechanics, and reactive transport to address challenges faced by California Energy Commission, Bureau of Land Management, and multinational firms including TotalEnergies and BP plc. It targets deployment scenarios evaluated by the IEA Greenhouse Gas R&D Programme and initiatives linked to Mission Innovation and the Global CCS Institute.

Methodology and Models

Methodological development integrates finite-volume and finite-element discretizations inspired by work at Los Alamos National Laboratory and Lawrence Livermore National Laboratory, with reduced-order modeling drawn from collaborations with Caltech and ETH Zurich. Numerical solvers leverage preconditioning strategies similar to those in research from Argonne National Laboratory and algorithmic frameworks influenced by Inria and Cranfield University. The project employs multiphase compositional models, capillary pressure relationships, and poroelastic coupling tested against benchmarks from SPE Comparative Solution Projects and datasets compiled by Norwegian Petroleum Directorate. Data assimilation methods include ensemble Kalman filter approaches developed in association with NCAR and Bayesian inversion techniques linked to Los Alamos studies.

Key Findings and Applications

Key findings have clarified controls on sweep efficiency in waterflooding and steam-assisted gravity drainage in contexts similar to San Joaquin Valley operations, and assessed leakage risks for carbon capture and storage analogues studied in Sleipner and In Salah projects. The research quantified uncertainty propagation using protocols comparable to those by IPCC and informed monitoring strategies employed by California Department of Conservation and Norwegian Climate Foundation partners. Applications extend to optimization work cited by Halliburton and Schlumberger, as well as environmental assessments carried out with Environmental Defense Fund and municipal agencies in Los Angeles and San Francisco Bay Area.

Collaborations and Funding

Collaborators include academic groups at University of Oxford, University of Cambridge, Tsinghua University, and Peking University, national labs such as Oak Ridge National Laboratory, and industry partners like ConocoPhillips. Funding sources comprise grants and contracts from the U.S. Department of Energy, National Science Foundation, philanthropic awards via the Gordon and Betty Moore Foundation, and consortium contributions from corporate members of the Carbon Capture Coalition. Memoranda of understanding were signed with regional stakeholders including California Energy Commission and municipal water districts.

Software and Data Availability

Software components were released under mixed licenses and draw on established packages and platforms used by Open Porous Media communities and tools inspired by MATLAB and Python ecosystems with interfaces to PETSc and Trilinos. Datasets from field pilots and synthetic benchmark cases are archived following practices from PANGAEA and metadata standards advocated by NASA and USGS. Repositories and code distributions have been shared with collaborators at GitHub and mirrored in institutional archives at Stanford Digital Repository for reproducibility and reuse.

Category:Stanford University Category:Reservoir simulation Category:Hydrology