Enthought is pleased to announce Virtual Core 1.8. Virtual Core automates aspects of core description for geologists, drastically reducing the time and effort required for core description, and its unified visualization interface displays cleansed whole-core CT data alongside core photographs and well logs. It provides tools for geoscientists to analyze core data and extract features from sub-millimeter scale to the entire core. This release introduces multiple new features, including rotational core alignment, import & export of DLIS files and updated classification tools using advanced machine learning algorithms.
NEW VIRTUAL CORE 1.8 FEATURE: Rotational Alignment on Core CT Sections
Virtual Core 1.8 introduces the ability to perform rotational alignment on core CT sections. Core sections can become misaligned during extraction and data acquisition. The alignment tool allows manual realignment of the individual core sections. Wellbore image logs (like FMI) can be imported and used as a reference when aligning core sections. The Digital Log Interchange Standard (DLIS) is now fully supported, and can be used to import and export data.
Whole-core CT scans are routinely performed on extracted well cores. The data produced from these scans is typically presented as static 2D images of cross sections and video scans. Images are limited to those provided by the vendor, and the raw data, if supplied, is difficult to analyze. However, the CT volume is a rich 3D dataset of compositional and textural information that can be incorporated into core description and analysis workflows.
Enthought’s proprietary Clear Core technology is used to process the raw CT data, which is notoriously difficult to analyze. Raw CT data is stored in 3 foot sections, with each section consisting of many thousands of individual slice images which are approximately .2 mm thick. This data is first combined to create a contiguous volume of the entire core. The volume is then analyzed to remove the core barrel and mud as well as correcting for scanning artifacts such as beam hardening. The image below shows data before and after Clear Core processing.
Clear Core processing prepares CT data for additional analysis.
Automated feature detection is performed during processing to identify bed boundaries, lamination, dip angle and textural features of the core. A number of advanced machine learning algorithms and image analysis techniques are used during this step. It is also possible to perform feature detection on core photographs.
Virtual Core provides an integrated environment for the co-visualization of the CT data along with high resolution core photographs (white light and UV) and well logs. Data can be imported using a variety of industry standard formats, such as LAS and DLIS. Thin section images, plug data and custom annotations can be added and viewed at specific depths along with the core data. A CT volume viewer provides a full 3D rendering of the interior of the core to investigate bioturbation and sedimentary structures.
NEW VIRTUAL CORE 1.8 FEATURE: MACHINE LEARNING AND CLASSIFICATION TOOL
Virtual Core 1.8 also includes an updated machine learning and classification tool. This feature provides an interface for a user to identify a lithology class of interest, and then automatically determines whether other regions in the entire core belong to the class or not. This can be used to rapidly identify intervals that have certain features in common, such as bedding structures or density composition.
Stay tuned in the coming weeks for more details on the specific capabilities and features of Virtual Core. If you would like more information please get in touch with us. We’d be happy to schedule a demonstration and discuss how Virtual Core can help you unlock your core CT data.