In June, the annual ‘Semantic Days’ conference was held in Oslo, Norway. Semantic Days is billed as a ‘meeting place for industrial and public sector use of semantic web technologies.’ The conference included a special session on Norway’s richly-endowed Integrated Operations in the High North (IOHN—Oil ITJ March 2010) project, one of the industy’s most ambitious test beds for semweb technology.
Karl Johnny Hersvik traced Statoil’s long march on the interop road with earlier initiatives of an ‘integrated information platform,’ ‘global operation data integration’ (GODI) and now, IOHN, real time environmental monitoring and ‘open data for innovation.’
Statoil’s Vidar Hepsoe unveiled the ‘new collaboration model’ for environmental monitoring. Complex operations in the high north mandate a paradigm shift from ‘expeditionary’ offline sampling to continuous monitoring. Operators need to demonstrate that they are not harming sensitive cold water coral structures with real time monitoring of seawater chemistry and video. This means blending data from echo sounders (fish radar) cameras, sonar doppler current profilers, hydrophones and hydrocarbon sniffers. This involves a multi disciplinary, multi system activity with ‘disparate systems and non standard, point solutions.’ The solution? System integration based on semantic technologies, along with ontologies and modeling rules that ‘understand’ what ‘environmental impact’ means in different contexts. Semantics and ontologies promise shared understanding, improved processes and better collaboration.
Trond Solberg, solutions architect for plant integration with Statoil described data modeling in industrial domains. A tender following the GODI program resulted in the selection of IBM’s information integration core (IIC) solution. IIC provides ‘model-driven’ access to OPC connectors into the IBM Infosphere service bus. Statoil is currently piloting the technology at four assets. The idea is to have enterprise-wide access to plant and equipment related data, through standardized information models feeding data from diverse sources into end-user applications. Solberg advocates a ‘bottom up’ approach, focusing on relevant use-cases and assessing what equipment attributes are needed. ‘Decide what these are and what they should be called. Model them and nothing else. But keep in mind that you will have to expand the model later so don’t do anything that will restrict expansion.’ No single standard provides all the required functionality. Solberg concluded enigmatically that ‘an integration layer is not always the correct answer to a given problem.’
IBM’s Ron Montgomery compared the ‘walled garden’ of proprietary operational eco-systems with the open source movement. Open source may appear ‘chaotic’ with uncertain support. But it can be the key to unlocking the garden gate—as a ‘new industry solutions model where systems of systems interoperate in an industry eco-system based on open, supplier neutral standards.’ Enter IBM’s ‘safe technology roadmap’ a combined ‘reference and execution environment’ spanning OpenO&M, Mimosa and ISO 15926. The system is being tested at the Northwest Upgrading/Redwater Partnership tar sands project. Engineering consultant Assetricity provides a range of Open O&M tools to the initiative including a ‘model-based information transformation engine’ to map between OpenO&M, ISO15926 and the information models of historians and control systems. Ultimately use cases will include field maintenance, asset configuration updates and semi-automatic triggering of condition-based maintenance.
Project manager Frédéric Verhelst kicked off the IOHN session. IOHN is a four year, $15 million project that is to complete next year. IOHN turns on the ‘application of semantic models in ISO 15926’ for ‘proactive monitoring and management of production critical sub-systems in collaboration with external expert centers.’ Two proofs of concept are underway. One, sand control, leverages ‘data standardization and abstraction of domain knowledge using a semantic model.’ The other, erosion monitoring, applies ‘autonomous decision making using a knowledge model based on an ontology.’
Baard Henning Tvedt (EPSIS) walked through the process of building a topological model of an oil platform, a hub between a reference data library, with SQL links to production accounting and SPARQL access to other data sources. He concluded, perhaps unsurprisingly, that ‘the graphical (UML) modeling approach better included domain experts than the RDF triples,’ but that the Visio UML modeling tool was ‘unsuited to automated model transformation.’ Those with a strong constitution may like to checkout more ISO 15926 presentations on www.oilit.com/links/1109_9.
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