Artificial Intelligence in Reservoir Characterization from CGG
CGG GeoSoftware cordially invites you to the “Artificial Intelligence in Reservoir Characterization” forum. The event will be held Tuesday, April 2, 2019 in London. Participation is free and the number of seats is limited. The program of the forum will include the latest achievements of geoscience – the principles of artificial intelligence in seismic reservoir characterization.
Bigger and Better Facies Classification
Petrophysical analysis and facies classification is seeing significant gains from the application of machine learning. Facies clusters defined for a play using unsupervised analysis on a few selected wells can be applied rapidly via supervised classification to large numbers wells across the play bringing a new scale and accuracy. PowerLog® native Python extensions puts these machine learning tools at your fingertips.
More Accurate Reservoir Properties, Faster
The estimation of reservoir properties is normally undertaken using seismic inversion results. However, neural networks are providing opportunities to take an alternative and direct data-driven approach. HampsonRussell™ first introduced neural networks into their software 20 years ago. Today they have an increasingly advanced toolkit featuring techniques such as probabilistic and deep feed-forward networks which can be deployed on your reservoir characterization projects.
GeoSoftware New Releases
Hear about our new generation of cloud-ready reservoir characterization solutions. Jason™ 10.0, HampsonRussell™ 10.4 and PowerLog™ 10.0 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.
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.
