Core analysis is challenging due to the massive amounts of disparate data located in multiple silos within organizations. The Virtual Core application enables geoscientists to gather, cleanse, prepare, and visualize all core data as well as integrate log data all in one application.
Virtual Core’s advanced workflows enable geoscientists to use AI and machine learning to remove the drudgery associated with core analysis while improving the accuracy and speed of their characterization.
Brendon Hall, PhD, Director Energy Solutions, summarizes the Virtual Core integrated geoscience visualization and analysis application, highlighting key features.
This deep dive demonstrates Enthought’s capabilities in preparing data for visualization and analysis, for example cleaning, scaling, depth matching and integrating with wireline logs.
CT Scans and core photograph images are often underutilized due to the difficulties in cleaning, preparing and integrating with other data. Enthought provides a service to correct CT data for environmental factors from recovery, and integrate with core photographs in a folder structure enabling further integration with all other data in the Virtual Core application. Legacy CT data can now be re-evaluated and used in context of more frequently analyzed data.
By its nature, geoscience is a visual discipline, and the Virtual Core application enables geoscientists
to view all of their data in one place so they can collaborate on the same data sets, and draw new conclusions.
Visualize all your core data in a single place including log and core data
and images depth matched, at any scale, overlaying any curves.
This deep dive demonstrates the Virtual Core application capabilities in visualizing integrated data sets across multiple scales to create a system that is intuitive and enables developing insights, in particular for subsequent AI and machine learning.
This deep dive demonstrates the Virtual Core application capabilities in AI and machine learning techniques for extracting features, classification, and eliminating the drudgery of core analysis while providing a deeper understanding.
The classification tool within Virtual Core enables geoscientists to train machine learning algorithms to classify facies over entire intervals with a limited training set, then retrain locally to improve.
Geoscientists can use the powerful AI and machine workflows within the Virtual Core application or build their own custom workflows to drastically increase their productivity and reduce the drudgery associated with characterizing large amounts of core.
Advanced AI and machine learning workflows include:
Enthought’s Ryan Swindeman presented a poster titled “Permeability Prediction Using Machine Learning to Upscale Core Measurements” at the SPE Data Science Convention held on April 4, 2019. The Virtual Core application’s advanced workflows enabled permeability prediction along the entire core using AI / ML algorithms.