Data assimilation for subsurface flow using latent diffusion model parameterization: performance of ensemble-Kalman and Monte Carlo techniques
arXiv:2606.11140v1 Announce Type: cross Abstract: Data assimilation (DA) in subsurface flow entails calibrating model parameters to match observed data, typically at wells, while preserving geological realism. Latent diffusion models (LDMs) provide efficient mappings from high-dimensional geological model space to a low-dimensional latent variable, reducing the dimensionality of the inverse problem while maintaining plausibility in posterior geomodels. However, the high nonlinearity in the LDM mapping may degrade the performance of Kalman-gain-based ensemble updates. We present a systematic co
The increasing availability of high-fidelity geological data and advances in machine learning, particularly diffusion models, are enabling more sophisticated approaches to subsurface modeling and resource management.
Improved data assimilation techniques for subsurface flow can lead to more accurate resource estimation, optimized extraction processes for critical materials like oil, gas, and groundwater, and better management of underground storage.
The application of latent diffusion models could significantly enhance the realism and efficiency of calibrating subsurface geological models, moving beyond traditional, potentially less accurate, methods.
- · Oil and Gas Industry
- · Water Management Agencies
- · Geothermal Energy Developers
- · Environmental Remediation Companies
- · Companies relying on less efficient or accurate traditional subsurface modeling
More precise reservoir characterization and production forecasting for hydrocarbon and geothermal resources.
Reduced operational costs and environmental impact through optimized resource extraction and subsurface storage strategies.
Enhanced energy security and improved water resource management globally due to better predictive capabilities.
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