SubsurfaceAI 2024 Module List
SubsurfaceAI 2024 comprises 21 modules, all integrated within a unified database and visualization environment. Each of the 7 software packages includes relevant modules designed to enhance subsurface exploration and production.
The foundation of SubsurfaceAI, supporting multi-scale subsurface data from core plugs, seismic, and production.
Comprehensive visualization for subsurface data interpretation, integration, and modeling
Interactive well log calculation and data analysis with machine learning predictions.
AI-assisted lithofacies classification and petrophysical property prediction from core photos.
Interactive correlation of stratigraphic boundaries and facies from well logs.
Predict well formation properties using machine learning and geostatistical workflows.
Model lamina-scale heterogeneity to reconcile differences between core plugs, well logs, and static reservoir models.
Complete workflow solutions from well tie to domain conversion for both 2-D and 3-D seismic interpretation.
3-D volume interpretation of seismic data with advanced visualization techniques.
AI-assisted workflows for 2-D and 3-D seismic interpretation.
Interactive calculation and enhancement of over 100 seismic attributes.
Qualitative and quantitative analysis tools for seismic attributes in reservoir characterization.
Estimate probabilities, cross-correlations, and variograms for multiple properties on horizons, intervals, strata-grids, cores, well logs, and reservoir models.
Build rock physics templates based on elastic models for quantitative pre-stack inversion and facies classification.
Integrate subsurface data from cores, well logs, seismic attributes, completions, and production into maps or 3-D models for informed decision-making.
Robust geostatistical toolbox for integrating spatial data in the subsurface, including variogram modeling, collocated kriging, and conditional co-simulation.
Data-driven forecasting of well production and sweet spot mapping through machine learning workflows.
3-D and 4-D visualization and prediction with AI and machine learning to optimize hydraulic fracturing and thermal production.
Workflows for building static reservoir models during seismic interpretation.
Convert conceptual geologic models into digital models and reservoir properties to optimize performance forecasting.
Enhance interpretations with rule-based geologic models and synthetic seismic volumes for more accurate AI model training.