CGG GeoSoftware develops machine learning ecosystem
Published by Naomi Holliman,
Digital Editorial Assistant
At industry events over the last couple of years, digitalisation has become a major point of interest with dedicated technical sessions and exhibition feature areas to explore this growing topic. Perhaps the biggest area of interest in the industry right now is around machine learning and the opportunities it offers to revolutionise geoscience workflows.
To help geoscientists take advantage of machine learning and deep learning technology, CGG GeoSoftware has developed a machine learning ecosystem. It provides open access to data within its geophysical and petrophysical applications. Python-scripted machine learning lets users get their hands dirty if they like to tinker under the hood, or users can select pre-built recipes. Many tasks can now be completed more quickly and with more detailed results; for example, well log editing, petrophysical analysis, facies classification, and reservoir property prediction. Meanwhile, deep neural networks provide benefits for tasks as varied as reservoir quality assessment and near-surface characterisation.
Even if geoscientists are not using machine learning personally, it is increasingly involved across various aspects of geoscience projects and workflows around them.
Before the industry gets to the point where it can truly benefit from big data analytics and take full advantage of machine learning, there is a need to reach a minimum common denominator in terms of the data itself. Recent efforts have seen the liberation of huge volumes of data from legacy formats, migrating to new data management platforms, including an increasing mix of cloud storage. CGG Smart Data Solutions help to ease this digital transition with end-to-end services, from expert upcycling of legacy data into the cloud to the deployment of their modern and flexible GeoTrove data management platform.
Integration and interoperability of geoscience data becomes important to really take advantage of data analytics and machine learning applications. CGG has spent the last few years gaining valuable experience while taking its geological library into the digital realm, using a proprietary taxonomy and ontology to create a unique framework for its GeoVerse data set. Meanwhile, its multi-client seismic library is now assessable through its new GeoStore portal, with controlled access to historical client entitlement data. Upload to the cloud is underway for the entire multi-client seismic library.
The cloud offers more than just data storage – cloud computing provides scalable and flexible solutions to compute-intensive reservoir characterisation workflows and very large projects. Through its technical collaboration with Microsoft, CGG’s latest GeoSoftware releases run seamlessly in the Microsoft Azure Cloud Environment, with other major cloud platforms soon to follow.
This ResPack Fast porosity volume, predicted by HampsonRussell Emerge from seismic data using a neural network trained on well log data, highlights higher porosity targets in the SCOOP & STACK plays of Oklahoma (image courtesy of CGG Multi-Client & New Ventures).
CGG will be at booth 720 at the 81st EAGE Annual Conference and Exhibition 2019, London, from 3 - 6 June 2019.
For information about all of CGG’s activity at EAGE 2019, please click here.
Read the article online at: https://www.oilfieldtechnology.com/digital-oilfield/06062019/cgg-geosoftware-develops-machine-learning-ecosystem/
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