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Industry 4.0 for the Oil and Gas Sector Research and Innovation Challenges and Mechanisms David Cameron CCFN Digital Transformation, Oslo, 13th October 2016 vices to be provided through smart netd computing). In conjunction with ever aturisation and the unstoppable march of this trend is ushering in a world where omputing is becoming a reality. Industrialisation began with the introduction of mechanical manufacturing equipment at the end of the 18th century, when machines like the mechanical loom revolutionised the way goods were made. This first industrial revolution was followed by a second one that Industry 4.0 of volution First programmable logic controller (PLC), Modicon 084 1969 First mechanical loom 1784 3. industrial revolution uses electronics and IT to achieve further automation of manufacturing complexity First production line, Cincinnati slaughterhouses 1870 4. industrial revolution based on Cyber-Physical Systemss 2. industrial revolution follows introduction of electrically-powered mass production based on the division of labour 1. industrial revolution follows introduction of water- and steam-powered mechanical manufacturing facilities End of Start of 18th century 20th century time Start of 1970s today Source: DFKI 2011 Industrie 4.0 Concepts: • Cyber-Physical Systems • Vertical Integration • Horizontal Integration • Internet of Things • Artificial Intelligence • Analytics Not a new paradigm. Rather the maturing of the 3rd industrial revolution. Automation finally works! It takes around 50 years for technical step-changes to result in measurable improvements in productivity. 13 Challenges to oil and gas • Digital end-to-end engineering – For the whole life of the facility • Inter-company value chains – Cutting the cost of EPC and M&M • Standardisation, products and services – Instead of tailor-made and owned • Robotics, minimum-manning and autonomy – In difficult and challenging places • Vertical integration: getting data to the decision maker – Commercial and technical decision makers need to be first-class digital employees Siemens Power Generation Use-case Uniform solutions for equipment monitoring BSX-TC3562-XE01 BSX-TMP12A-XE01 Sensor types, BSX-TICCFB1-XE01 turbine structure, measurable BSX-TC3562-XE01 quantities, Semantic mapping “Ignitor on” Domain ontology Processes BSX-TC3562-XE01 CRR-M8393-9272 Query site configurations, MS-XC255-X12 MRR-T8901-8462 Normal start? Analytics Unrestricted © Siemens AG 2016 Page 3 Dr. Sebastian-Philipp Brandt, Siemens CT RDA BAM SMR-DE, Corporate Technology * http://optique-project.eu/ Who will deliver the solutions? • • • • • • Platform Companies? IT System Integrators? ERP? Automation? Equipment Manufacturers? Analytics Providers? Every vendor is offering their own cloud. How do we get these clouds to overlap, work together and work with our legacy? Research issues to be solved • • • • • • • • • • Specifying and maintaining useful semantic models about real things Good, fast, effective databases – in memory and in place Use of natural language – in data and interaction Efficient, predictable access to data spread across the cloud Secure, role-based access to data High-performance computing to access data, reason and calculate Modelling, optimization and reasoning – analytics – not just statistics Sensitive and effective transformation of work practices Development of friendly, usable user services i.e. industrial informatics. We are building an innovation cluster • Operators, EPC and service companies to provide the hard business problems • Research providers to bring the experiments into prototypes and pilots • Integrators, both large and small, to deliver the products and services SIRIUS: Centre for Scalable Data Access in the Oil and Gas Domain