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AI Demos by industry

Carbon capture & storage AI Demos

Compare reservoir conditions, storage capacity, and injection pressure. Explore modeling workflows that keep geological assumptions and monitoring constraints visible. Browse interactive demonstrations relevant to this area, with methods, screenshots, and resource links.

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Carbon Capture and Storage

Carbon storage planning requires an understanding of both available reservoir capacity and the pressure response to injection. This synthetic example relates geological properties, well spacing, and monitoring constraints to a defined storage domain. Its model views provide a way to compare how input assumptions affect estimated storage behavior and the operational limits that accompany it.

  • Evaluates carbon-storage capacity and pressure change in a synthetic reservoir scenario.
  • Connects permeability, porosity, well separation, and monitoring limits to the chosen reservoir domain.
  • Uses field-generation and solver controls before storage outputs can be interpreted.
  • Supports comparison of input and output maps, storage capacity, and pressure constraints; the example is not a certified storage assessment.

Geo Modeling

The geological modeling example brings surface terrain and subsurface structures together in a synthetic three-dimensional field. Wells, horizons, faults, and temperature intervals can be examined in the same scene to understand their spatial relationships. This integrated perspective helps geoscience teams explain subsurface geometry and discuss how well information relates to a broader geological interpretation.

  • Combines wells, geological horizons, faults, terrain, and temperature intervals in an interactive 3D scene.
  • Relates well trajectories and depth-dependent temperatures to their surrounding geological context.
  • Provides layer controls, terrain opacity, vertical relief, and well inspection.
  • Provides access to our open-source and proprietary ML for geospatial analyses.
  • Uses deterministic synthetic geology for illustration; the scene is not a field-validated subsurface interpretation.

Model Diagnostics

A close match to observed data leaves important questions about a model's reliability and the information contained in its parameters. This demo combines calibration with held-back checking, sensitivity analysis, and an examination of whether individual inputs can be constrained. The resulting views help users distinguish influential parameters from weakly supported ones and identify where additional evidence could improve an analysis.

  • Combines model calibration with a held-back data check.
  • Examines which inputs influence predictions and which parameters are poorly constrained.
  • Includes an optional unused input to illustrate an identifiability warning.
  • Encourages joint review of fit, validation, sensitivity, and identifiability rather than judging a model by fit alone.
  • Applies our open-source code Mads.jl