CGG announces that GeoSoftware, part of its Geoscience Division, has launched a new generation of cloud-ready reservoir characterisation solutions. Jason 10.0, HampsonRussell 10.4 and PowerLog 10.0 also feature advanced machine learning capabilities and greater cross-product integration, improving E&P project performance and providing a better understanding of reservoir properties.
The three new releases already run seamlessly on Microsoft Azure’s Cloud Environment and will soon be available on other major Cloud platforms. Geoscientists can now implement compute-intensive workflows and run very large projects, to process thousands of wells, faster than ever before.
The benefits of machine learning continue to drive new capabilities to address complex geological challenges. HampsonRussell Emerge now delivers deep learning in the form of Deep Feed Forward Neural Networks for better prediction of reservoir properties. An open Python ecosystem in PowerLog enables the routine use of machine and deep learning in workflows to increase automation and achieve more accurate facies predictions.
The new releases feature integration advancements, such as the ability to “load once, use everywhere” to streamline cross-product workflows, as well as other user-driven improvements. HampsonRussell 10.4 updates Advanced Seismic Conditioning to improve seismic data quality for better inversion outcomes and AVO Modeling now offers a wider range of tools for investigating the seismic response of pre-stack data. Jason 10.0 makes it easy to design or vet facies classifications from petrophysical logs and immediately see the effects in the elastic inversion domain. It also has improved velocity calibration for time-to-depth conversion and depth inversion. PowerLog 10.0 advancements enable users to efficiently interpret groups of wells and apply machine learning to solve petrophysical challenges using the PowerLog Ecosystem.
Kamal al-Yahya, Senior Vice President, GeoSoftware & Smart Data Solutions, said: “As always, we listen to our clients to ensure our new software releases bring innovations that really help them increase efficiency. Greater product integration is key for inter-disciplinary workflows as links between geophysics, geology and petrophysics grow ever stronger. This new set of releases is also the first to deliver real benefits from our digitalisation roadmap, offering a high-quality experience with the integration of Machine Learning and Cloud-ready applications.”
Read the article online at: https://www.oilfieldtechnology.com/digital-oilfield/21022019/cgg-announces-new-geosoftware-releases-with-cloud-ready-machine-learning-capabilities/