AI Demos by industry

Oil & gas AI Demos

Examine reservoir geology, production scenarios, and subsurface uncertainty. Explore model diagnostics and geological workflows relevant to oil and gas decisions. Browse interactive demonstrations relevant to this area, with methods, screenshots, and resource links.

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Energy Production/Storage

Subsurface energy decisions depend on how reservoir properties and well arrangements affect flow, heat transfer, and pressure. This synthetic example connects a chosen reservoir domain and physical inputs with field generation and a flow-and-thermal calculation. Comparing the resulting maps and summaries helps users investigate the consequences of an energy production or storage scenario.

Estimates energy production and pressure change for a synthetic reservoir and well arrangement.
Configures a reservoir polygon, resolution, permeability, rock and fluid properties, and operating inputs.
Generates a permeability field and runs the flow and thermal model through separate controls.
Relates output maps and energy or pressure summaries to the selected physical inputs; configuration alone is not a solved scenario.

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

Well Log Gap Filling

A geological well log records changes along depth, but gaps can interrupt the patterns needed for interpretation. This synthetic example uses neighboring measurements to estimate missing values while retaining the ordered depth structure of the record. Comparing the reconstructed log with held-back measurements helps users assess the behavior of the gap-filling method before considering a similar workflow for their own data.

Fills intentionally missing measurements along an ordered synthetic geological well log.
Uses our proprietary bounded RobustML neighbor method, with quick and stability-check configurations.
Compares observed and filled values using held-back RMSE and MAE.
Distinguishes the labeled precomputed example from a registered live workspace analysis.