CGG GeoSoftware Increases E&P Efficiency and Effectiveness with Machine Learning and Cloud-Ready Applications
At the 2018 EAGE convention in Copenhagen, CGG GeoSoftware is showcasing its latest developments aimed at harnessing the powerful capabilities of machine learning and cloud computing to enhance the performance and value of its geoscience software. E&P industry users can benefit from these new advances and transform their work practices, processes and workflows to ultimately streamline and improve operations.
With the latest 9.7.2 release of PowerLog petrophysical analysis software, users can now solve complex petrophysical and reservoir engineering challenges by accessing native machine learning and deep learning Python utilities, opening up vast possibilities for leveraging open-source technology and solutions, and designing bespoke workflows.
This year’s release of version 10.4 of GeoSoftware’s HampsonRussell reservoir characterization software will deliver substantial new machine learning technology in its Emerge attribute prediction module with advanced Deep Feed Forward Neural Network technology. Early investigations show promise for estimating density which is typically challenging to achieve through inversion.
Sophie Zurquiyah, CEO, CGG, said: “As an integrated geoscience company, CGG is aware of the challenges of digitalization for the E&P industry as well as the significant rewards it can bring to create new value and optimize decision-making. GeoSoftware is taking the lead to develop and demonstrate new workflows and capabilities so its software users can leverage the full potential of these new technologies and trends. These first results from our technology roadmap are just the beginning and we anticipate many more exciting developments in the future.”
