I am excited to announce that we are setting up an OSDU Machine Learning consortium. E&P data are typically found stored locally in multiple, incomplete and mostly unconnected d...
I am excited to announce that we are setting up an OSDU Machine Learning consortium.
E&P data are typically found stored locally in multiple, incomplete and mostly unconnected data silos. This results in unnecessary and costly duplication of data that is not accessible or suitable for modern Machine Learning applications.
Our goal is to demonstrate in a series of proprietary case studies what the future of E&P data management looks like and how Machine Learning models can add value to a standardized, cleaned-up datastore containing well and seismic data.
For each consortium member we update a proprietary database containing wells and seismic data. The received data is cleaned up and uploaded using the guidelines and technical standards that have been developed by The Open Group OSDUTM Forum (OSDUTM is an Open Source, Cloud Native, Subsurface Data Platform). Next, we add value to the data by applying modern Machine Learning algorithms. All work is performed in OpendTect Machine Learning software that will enable OSDUTM data handling.
Interested in learning more? Check out our flyer: https://lnkd.in/djtkNUQ
Or send me an email: info@dgbes.com
#machinelearning #OpendTect
