In assembling this issue, we have studied a plethoric body of literature covering ‘digitalization’ and ‘digital transformation’ of the oil and gas industry. While the exact meaning of digital transformation is obscure (it could just mean ‘buy more of our stuff’) we examine some of the more coherent arguments for change as we a) try to pin down a definition and b) look behind the hype. But first, by way of an executive summary, here are our conclusions from this short trip through the latest thinking on the digital transformation.
While there are many possible interpretations, we consider that digital transformation is essentially about moving data to the cloud. The perception is that the cloud will be a better place to integrate data and make it amenable to artificial intelligence (AI). What is less clear is whether the move to the cloud is an inevitability to which all must face-up, or even a prerequisite for doing AI. We consider that digital transformation is part rehash of pre-existing solutions and platforms and part an attempt at a land grab by the IT and consulting community. The Accenture position paper is particularly revealing as it concludes that in the future, ‘[data science] expertise [will] triumph over industry experience.’ At another level, the hardball marketing extends into the IT department itself with, as a recent Forbes article has it , ‘CIOs must adapt their personal and professional skills to meet today’s demands. Those who don’t risk becoming irrelevant.’ You have been warned!
Paul Bonner, speaking at the 2018 Honeywell User Group (HUG) in San Antonio, made the case for differentiation between transformations that focus on information technology versus operations technology. In the IT camp, companies are ‘either engaged in or planning for DT’ driven primarily by IT. While ‘some success’ is reported in data integration and cloud usage, cyber security and data governance challenge the pace of progress. OT has perhaps a head start on IT with many pilot projects completed. The latter have shown that ‘process data is highly correlated*, general purpose big data tools and data scientists are not effective.’ Also, in the OT domain, streaming process data to the cloud requires a different toolset from IT. Bonner suggests building an OT transformation strategy by starting from an understanding of the existing digital footprint and capability. Then the required ETL tools and processes can be deployed to move data into a data lake in the cloud. A corporation’s readiness for such upheaval should be addressed with a digital maturity model such as those emanating from the Industry 4.0 movement, see for instance Science Direct (open access). Bonner sees the cloud from a different standpoint to some in the IT community. Good metadata is the key to cloud success. The cloud is not a suitable place for storing high volumes of raw process data which should be cleansed aggregated and compressed ‘at the edge.’ Various combinations of on-premises and hosted clouds can be deployed depending on governance and regulatory needs. All of these expose different and non-negligible cyber security issues. Which is where Honeywell’s enterprise secure cloud comes in, acting as a single conduit from plant to data lake. Honeywell advocates maintaining dual data stores in the cloud. An enterprise historian for operations and a data lake for analytics. These are linked by a ‘context model’ of plant equipment and data sources. Bonner concluded that the OT journey is not an easy one and there are no correct answers to tool selection - except to choose the right partner, Honeywell HCP of course!
* AI techniques often assume input variables are uncorrelated. Early attempts (from the 1960s!) at numerical taxonomy in biology and geoscience came unstuck as measures (length, breadth, weight) are generally correlated. Correlation is a good issue to raise with your pet data scientist.
A 16 page position paper from ABB explains “how digitalization enables oil and gas operators to transition to a new energy ecosystem” rather confusingly conflates the transition of oil and gas to a greener energy ecosystem with the digital transformation. Success during the energy transition requires a ‘robust digital strategy’ with board level support. ‘Boardrooms need to act decisively and embrace digital by (inter alia) accepting a flatter organization where decisions can be made by well-informed colleagues deeper in the organization who are receptive to new ideas and ways of working and by collaborating with the supply chain (i.e. ABB) and by ‘forming digitally-powered, multidisciplinary teams with the freedom to think differently.’ ABB’s copywriters extend themselves somewhat when they advocate ‘social media crowdsourcing’ which apparently ‘has already proved useful in reserve analysis, where seismic data and other information is put into the cloud for crowds to suggest analytical technique improvements.’ The approach is moreover said to ‘work well in the sharing economy that oil and gas operators are now entering.’
A barrier to sharing (of data) across the supply chain is the lack of standardization of sensor data. Other issues involve uncertain ownership of and access to data from suppliers, operators and contractors. ‘There is a lack of standardization and even when data is accessible, it is often too complex or large, obscuring any clear insights.’ ABB cites The Open Group’s work with ExxonMobil and Lockheed Martin as showing promise in this context but observes that where standards are ‘ambiguous or too general’, ABB develops its own.
At the Upstream Intelligence Data-Driven Drilling and Production conference in Houston earlier this year, Jan Pawlewitz presented Siemens’ Digital Hydrocarbon offering which is based on its ‘MindSphere’ cloud-based, ‘open’ Internet of Things operating system. MindSphere promises out-of-the-box IoT connectivity along with ‘MindApps’ for end user applications. For Siemens, the next stage in oil and gas ‘competitiveness and efficient operations’ will come from digital and advanced analytics. Siemens uses the EU-backed Industry 4.0 paradigm.
Whereas ‘Topsides 4.0’ is arguably inside Siemens purview, a cryptic reference to SIFeld 4.0, an ‘integrated digital twin of reservoir and facilities’ would appear to be a new branding of the work performed for AkerBP a.k.a. the ‘MindTwin for remote offshore operations’ on the Ivar Aasen Field. Another Siemens reference is Bahrein Petroleum, Bapco, which deployed Siemens XHQ operations intelligence software in 2012. This leads to the question as to how new and novel all the digital twin stuff really is and the extent to which it is a repurposing of existing ‘solutions.’ Oil IT Journal’s earliest XHQ reference dates back to 2003!
TechTonic 2018, a gathering of Shell’s technologists hosted at its Bengaluru, India IT hub heard Jay Crotts (executive VP and group CIO) and Nitin Prasad, (chairman of Shell Companies in India) expound on ‘digitalization’ and Shell’s future vision. Prasad put digitalization ‘at the heart of the energy transition.’ Shell unveiled ‘Agile’ - a new project management tool to manage workstreams and information flow alongside its integration of SAP in its digitalization journey and new cloud computing capabilities. Gartner’s Rich McAvey led a masterclass on digital transformation and the future of work in oil and gas with emphasis on how ‘CIOs must change IT to remain relevant and impactful.’ T-Systems’ CTO Jo Campbell led another class on IoT/edge technologies that are to ‘transform the landscape of the energy industry and refineries.’ Crotts cited Shell’s increased focus in expanding in-house IT expertise as testimony to the importance of digital ecosystems for driving progress in the energy world. ‘This digital agenda has been at the forefront of our thinking for years- from automation of oil field [production] to the engagement with our end-customers. I believe the digital agenda gives us a platform for standards that allow us to execute business processes cheaper than we have ever done before.’
The title of Accenture’s latest white paper (and full report here) ‘Oil and Gas: How do you compete with free?’ might make you think of open source software. Not at all. The threat to oil and gas is free energy! Oil demand is expected to peak in the next 20 years and its share of the energy mix is expected to fall from 80% to near 50% by 2060. This means that leading oil and gas companies are on high alert. 54% believe their growth strategies are at risk. The rest are ‘less concerned,’ in part because they believe their digital investments will protect them. Enter the ‘tremendous opportunity’ of data that is ‘just waiting to be turned into actionable insights that can reduce the cost of supply, increase operational responsiveness, and open the doors to new and profitable business models’.
Accenture’s analysis suggests that applied intelligence, driven by analytics, has the potential to shift the P&L equation with double-digit gains in efficiency, productivity and cost savings. The shift to an AI-driven world means changes to the oil and gas job market. According to Accenture, geoscience professionals’ jobs will ‘increasingly be filled by image processing experts from other industries such as high tech or health care*.’ Oils need to realize that ‘expertise triumphs over industry experience.’ ‘To compete with free, companies need to shift focus to providing a service, not selling a commodity at the wellhead. To enable this transition, the entire ecosystem must evolve to ensure molecules are dispatched to end users that exhibit the greatest demand. Expanding collaboration and risk-sharing across ecosystem partners, all the way to the customer, will be key.’
* This claim merits a health warning! Geoscientists are already to a large extend ‘image processing experts’ witness the huge seismic imaging market and the use of image processing tools (Aviso, Geoteric and others). This is a typical ‘grass is greener on the other side of the fence’ claim, assuming that the reader has a poor knowledge of the state of the art in the other (healthcare) field.
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