Module 21

In the AI application for subsurface interpretation and modeling, one of the substantial challenges is the scarcity of sufficient training labels, which hampers the application of artificial intelligence in various subsurface workflows. Module 21 of SubsurfaceAI addresses this challenge by facilitating the rapid creation of geologic models and their corresponding seismic volumes. These models and volumes are crucial for training AI algorithms, particularly for fault and channel detection.

Module 21 introduces a suite of tools designed to streamline the generation of training data, which is vital for the effective application of AI in subsurface exploration and production.

Key Features

Simulation of Faulted Geologic Models:

User-Defined Fault Populations: Users can specify the distribution of fault characteristics such as length, width, and displacement. This flexibility allows for a tailored approach to modeling based on specific geological scenarios.

Near-Field Displacement Vector Fields: These 3D vector fields are calculated to represent the displacement caused by faults. They are used to adjust both static reservoir models and seismic volumes, ensuring that the models reflect realistic geological conditions.

Separate Fault Labels: Fault labels are generated as “Ground Truth” data, providing a reliable basis for training AI to recognize and interpret fault structures accurately.

Synthetic Seismic Volume Generation:

Faulted Geologic Models: The module generates 3D synthetic seismic volumes based on the faulted geologic models, providing a rich dataset for training AI systems.

Integration with RBM Module: Synthetic seismic models can also be derived from stratigraphic models created using the RBM methodology from Module 20. This integration ensures consistency and enhances the quality of training datasets by using geologically realistic models as a foundation.

High-Efficiency Model Production:

Scalable Generation of Training Data: Module 21 is designed to efficiently produce thousands of training models along with their seismic volumes. This capability is critical for training robust AI models that can handle diverse geological scenarios and improve fault and channel detection in various subsurface environments.

Module 21 revolutionizes the preparation of training data for AI applications in subsurface workflows. The  module ensures that AI systems have access to high-quality, realistic datasets for training. This not only enhances the precision of fault and channel detection but also paves the way for more advanced AI applications in the field of geoscience.