DNV GL © SAFER, SMARTER, GREENER DNV GL © Tore Hartvigsen OIL & GAS Optique Project Dissemination 1 Summary

DNV GL © Agenda - SUMMARY 1)Where do we stand relative to our vision 2)Challenges 3)Opportunities 4)What are the next steps? 2

DNV GL © Where do we come from? ISLANDS OF INFORMATION!

DNV GL © Where do we come from? ISLANDS OF INFORMATION! Data integration and exchange are major challenges!!

DNV GL © The ISLANDS are now growing into SILOS ! VOLUME increase

DNV GL © The ISLANDS are now growing into SILOS ! As volumes increase data integration becomes even more complex and critical!

DNV GL © 2014 CONFIDENTIAL 03 February 2016 The VARIETY of Information Sources Increases 7 PFD MEL P&ID Equipm. indexes Weight Loop/ term drawings 3D Models 2D schematics Documents Vendor Documents TR Vendor Drawings Certificates Data mapping schemes ontology Standard ontologies Cable routing Data sheets MTO Project control Cost control MC PC&C Risk Transmittals Tech. Anal. Enterprise Data Simulations

DNV GL © The VELOCITY of Data Generation and Change 8 (Copied from Wikipedia)

DNV GL © Veracity  Trustworthiness - reliability  Security  Data Quality Ulrich Schniedermeier: 9

DNV GL © How Can Semantic Technologies help us? The AAA slogan from the World Wide Web – A nybody can say A nything about A ny topic. Prerequisites:  Focus on information not on data formats  A data model where information about each single item can be published  A data model where data resources can be associated to each other  Data must be converted to RDF (Resource Definition Format)  Data must have unique identifiers 10

DNV GL © 2014 CONFIDENTIAL 03 February 2016 Prepared for Future Development Steps! 11 Project x2 x1 Project x1 Vendor information Client requirements Experience data User Ontology Project xn x1 Linked Data (relevant external sources) Standard ontologies

DNV GL © 2014 Ungraded 03 February 2016 OPTIQUE 12

DNV GL © Where do we stand today?  We can demonstrate capabilities within: – A full functioning Optique system – OBDA (Ontology Based Data Access) – Query transformation (SQL -> Sparql) – Processing of real time streams – We can offer a training program  We are still not clever in: – Analytics (built in analytic tools) – Variety (Integrating data from several different sources)  We can do better in Veracity: – Data Quality measures – Data security measures 13

DNV GL © What are THE NEXT STEPS?  Finalize the OPTIQUE project  Proposal for a continuation project delivered : PanOptique  Encourage additional industry take-up projects  SIRIUS center 14

DNV GL © panOptique 15

DNV GL © SAFER, SMARTER, GREENER Semantic Technologies 16 Tore Hartvigsen