Digitalisation: How can we mix the "new oil" and the "old oil? The role of IT Research
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The document discusses the integration of digitalization in the oil and gas industry, emphasizing the role of research and innovation in managing digital assets and enhancing operational efficiency. It highlights the potential benefits, challenges, and the need for interdisciplinary collaboration and robust data management practices. Additionally, it addresses the concerns surrounding machine learning, artificial intelligence, and the importance of an agile culture in adopting new technologies.
Digitalisation: How can we mix the "new oil" and the "old oil? The role of IT Research
- 1. Digitalization: How can we mix the ”new oil” and the old oil? The role of IT research David Cameron Centre Coordinator, SIRIUS Centre V November Conference, Rio de Janeiro, 7th November 2017
- 2. The SIRIUS Centre Eight years’ financing from RCN 11 Industrial Partners 3 Leading Academic Institutions Centre for Research-Based Innovation Funding for 20 Ph.D. students Innovation through prototypes and pilots 45 affiliated researchers
- 5. The business context for digitalization • Digital asset lifecycle management • Circular collaborative ecosystem • Beyond the barrel • Energizing new industries
- 6. Big rewards. Big challenges. • Digital Asset Management Lifecycle – Automation: USD 220(230) b benefit, 38000 jobs “displaced”. – Analytics: USD 425(525) b benefit – Connected worker: USD 100 b benefit, 76000 jobs ”displaced”. • How do we avoid the Integrated Operations experience? – OLF 2007 Promised USD 30 billion in 10-year potential – At oil price of USD 43! – Coincided with billions in increased cost. Source: WEF Report OLF presentation on IO, Nov 2007
- 7. Machines • Are machine learning and artificial intelligence oversold? • Are systems safe? Reliable? Resilient? • Do we have the data? Is the data good enough? • How do machines support better work practices? • How much of our decision-making is decided by science fiction?
- 8. Platforms • Whose platform? • Whose data? • Platforms as the new silos? • Sovereignty, ownership and integrity? • Security? • Design of platforms? • Interoperability?
- 9. Crowds • Capturing the contribution of each engineer, geologist or planner • Open supply chain to share data • Work with vendors in co-design • Expand access to start-ups and SMEs • Build an agile culture without anarchy • Self-service access to data and analytics
- 10. SOME CAUTIONS Digitalization in Oil & Gas
- 11. Rubbish in = Rubbish out Sensor Analog Transmission A/D Converter Data Processing Control System Historian Interface Local Bus Historian True Value OPC Data Processing Reported Value Data Warehouse ETL Sensor Surroundings True but unknown. Barrier between sensor and quantity measured. Calibrate and maintain. Configure properly. Configure properly.
- 12. The real world is a hard place • Dirty, wet, cold, hot • We haven’t done technology qualification just for fun. • Robustness, reliability and resilience is difficult and expensive • How many control loops do you have in manual?
- 13. Data lake or data swamp? • Integration • Security • Integrity • Maintenance • Search • Maintain Meaning and Context
- 14. Skills and domain knowledge • Data scientist: a statistician with attitude? The guru of the new age? • Ideal is an expert on: – Statistics – Computer science – Domain knowledge (physics, engineering, geology, business…) • Might an interdisciplinary team be more useful? • Might hybrid modelling be better than raw empiricism?
- 15. E&P is an inherently social process • Interpretation • Judgment • Experience • Dialogue • Mutual responsibility • Team work
- 16. ACADEMIC COMPUTER SCIENCE CAN HELP DIGITALIZATION Centres for Research-Based Innovation
- 17. Building bridges to fill gaps Publish! Sell! Pump Oil!
- 18. An interdisciplinary computer science approach Knowledge Representation Natural Language Databases Execution Modelling & Analysis Scalable Computing Work Practices Data Science
- 19. With friends with problems … and domains • Our partners • Geosciences at the University of Oslo • Earth Observation in Tromsø • Automation and computer-aided process engineering groups • EU Private-Public Partnerships – BDVA ⇒ EFFRA, AIOTI – A.SPIRE
- 20. From lab bench to products and services Laboratory Projects Innovation Projects Fundamental projects: Oxford, NTNU and Oslo Laboratory Prototyping Project Pilot Project Product or Service
- 21. Optique: Digitalization of Geoscience and NDRs Student in Petroleum Geoscience: ”I want all Gamma Ray logs from wells that penetrate Rotliegend deposits, with porosities larger than 25% between 3°E-12°E and 50°N-60°N” DISKOS – CDA – DINO – JUPITER – German NDR SEARCH
- 22. Operations: Planning, Requirements and Digital Twins Digital twins Requirements Planning
- 23. Planning for Commissioning, Logistics & Maintenance
- 26. Operations: Planning Knowledge Representation Semantic representation and manipulation of planning concepts and data (ILAP). Natural Language Analysis of free-text fields, manifests and work orders. Databases Execution Modelling & Analysis Analysis and optimization of plans and schedules. Simulation and optimisation. Verification of plans and re-planning. Scalable Computing HPC and cloud support for planning simulations and optimisations. Work Practices Effect of formal methods and simulation-based tools on planning work processes and practices. Data Science & Machine Learning Modelling of risk and adaption of plans and models to observed behaviour.
- 27. Operations: Digital Twins Knowledge Representation Semantic support for configuration, interfacing and integration of digital twins. Ontologies for twins. Natural Language Conversion from natural language documents to and from semantic requirements. Interpretation of logs. Databases Efficient triple-stores for use in digital twin implementations. Execution Modelling & Analysis Analysis and optimization of consistency, safety and correctness of systems and their twins. Handling of events. Scalable Computing HPC and cloud support for digital twins and supporting tools. Work Practices Specification and implementation of fit-for-purpose digital twins. Data Science & Machine Learning Streaming analytics and events. Reconciliation and alignment of data and models. Hybrid analytics.
- 28. Operations: Requirements Knowledge Representation Semantic representation of requirements for storage, sharing, transmission and reasoning. Natural Language Conversion from natural language documents to semantic requirements Databases Efficient triple-stores for requirement systems Execution Modelling & Analysis Analysis of execution of requirement fulfilment and implementation in design systems Scalable Computing HPC and cloud support for requirement reasoning systems Work Practices Collaboration across engineering supply chain Data Science & Machine Learning Monitoring and continuous improvement of engineering performance