Advanced detection algorithms incorporate machine learning techniques to provide a new level of quantitative analysis and classification for geologists and petrophysicists, accelerating and improving interpretation of core bedding, bioturbation, facies, and lithology features from well log data.
• Data cleansing and processing
• Integrated viewer for well logs, images, and volumes
• Easy navigation from hundreds of feet of core down to millimeter-resolution features
• Machine learning with advanced detection algorithms
Learn more in our blog: Latest Features in Virtual Core 1.8
Cleanse & Correct
Clustering & Classification
Analysis & Exploration
Aligns data into a contiguous core, normalizes differences, and removes scan artifacts and coating effects to optimize data for advanced processing and viewing.
Processing provides quantitative automated feature detection, clustering, and classification tools for additional exploration.
The Virtual Core Viewer provides macro to microscopic co-visualization of CT and photo imagery with well log data for full core and well log analysis.
Virtual Core processing provides auto-detection and display of core features such as parting fractions, dip angles, lamination frequency, intensity, and more for quantitative, consistent data interpretation across wells. Cutting-edge machine learning algorithms enable automated and semi-automated methods to classify similar rocks or events on full cores.
Virtual Core incorporates full core CT, photo, and processed feature detection views (including microscopic-level data) with petrophysical well log data in a comprehensive and integrated visualization and analysis environment.
1 Integrated Viewer