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.
In this video, Brendon Hall, Energy Solutions Group Director, summarizes the Virtual Core visualization and analysis application, demonstrating key features
It is often said that data cleansing and preparation can take up to 80% of the time in data-intensive workflows. The Virtual Core application provides the automation tools geoscientists need to collect disparate file types, remove artifacts, and align measurements in depth so that geoscientists can focus on analysis and interpretation.
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.
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.