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It is urgently needed to build competence and tools for the exploration data wrangling. An exploration data wrangler has competence in both geoscience and digital technologies. This competence is crucial to integrate the workflows of geoscientists, PDMs and CDMs and plays an important
role in enabling digital transformation in exploration work practices. Along with supporting exploration teams with the routine data access tasks, the data wranglers will efficiently exploit opportunities brought by new IT technologies. This includes efficient handling of critical tasks such as identifying relevant data sources, developing complex ad-hoc queries over federated databases, and retrieving information from reports stored as text documents. In these ways, the data wrangler can bring data much closer to the project teams and give geoscientists a radically better possibility of extracting data and information with the exact specification (in terms of complex geological and petrophysical attributes) they need for their subsurface evaluation.
For the data wrangler to be less dependent on the CDMs and PDMs than the geoscientists are today we need to capture the special knowledge of the CDMs and PDMs and buildt his into data wrangling tools. Asuccessful attempt in this direction was Optique, a 14M Euro EU project
that finished in 2016. Optique showed that geoscience knowledge could be reliably captured in a knowledge graph (or an ontology) and reusable mappings from CDMs could efficiently connect this knowledge graph to data in data- bases. Optique then demonstrated that complex queries over several federated data sources (including EPDS, NPD FactPages, Open Works installations, GeoChemDB, CoreDB and DDR) could be easily written and efficiently executed. Since the process was fully automated, tasks that normally would take several days could, with the Optique platform, be performed in minutes.
Optique showed the potential to transform the way data is gathered and analyzed by streamlining the workflow and making it more user-friendly. However, Optique has also revealed shortcomings that impede the realization of its full potential: (i) limitation to relational databases, (ii) lack
of built-in support for quantitative analytics, (iii) lack of access to unstructured data, and (iv) limited tool support for constructing and maintaining the necessary ontology and mappings.
48 | SIRIUS ANNUAL REPORT 2021
cross-component optimization