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CGG GeoSoftware, part of CGG’s Geoscience Division, has released new versions of its cloud-ready reservoir characterisation and petrophysical interpretation software with features and functionality that enhance the user experience, improve capabilities, and offer greater flexibility

CGG GeoSoftware Jason DepthModCGG GeoSoftware Jason DepthMod. (Image source: CGG GeoSoftware)

All applications across the entire GeoSoftware portfolio now run on both Azure and AWS platforms, enabling easy access to products from wherever a client is working. The newest releases, Jason 10.1, HampsonRussell 10.5 and PowerLog 10.1, also offer advances in machine learning and artificial intelligence, as well as streamlined connections to Python ecosystem notebooks.

Jason 10.1 enhancements include further expansion of drag-and-drop functionality of files and viewers. The Jason Workbench now has a consolidated Progress Overview, an all-in-one window without distracting popups. Users can focus on their job, spend more time on science and less time clicking, and generally work more efficiently.

Time-to-depth conversion in Jason DepthMod is faster and now requires fewer steps. Velocity refinement can occur over all geologic layers simultaneously.

The Jason Python ecosystem makes Jason data available to data scientists for machine learning applications. It links to popular data analytics packages for greater efficiency and productivity. In addition, the EarthModel application now offers more efficient transfer of data and corner point grid (CPG) to and from Petrel.

HampsonRussell 10.5 offers multi-node processing (MNP) on Windows in addition to Linux, powerfully speeding up your processing projects. Initial tests of MNP Windows indicate 3.5 times faster project speeds when using four MNP nodes rather than a single node.

A new Python ecosystem provides the flexibility to design and code custom processes while taking advantage of the HampsonRussell project structure and data access. The ecosystem accesses log, horizon and seismic data. Additionally, improved QC plots in Deep Learning Network analysis provide ways to perform better parameterisation of neural networks, enabling users to customise parameters for a better neural network design.

PowerLog 10.1, the most user-friendly package for petrophysical interpretation, now offers Automatic Depth shifting. This artificial intelligence capability calculates depth shifts for a variety of well log data sets to a measurement that is known to be on depth, important for generating log correlations and valid petrophysical computations. Many of PowerLog’s usability enhancements are client-driven and enable users to easily generate interpretations and optimise well completions.