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Machine Learning can extract the information needed for modelling and data analysing from unstructured documentsNormal access

Authors: H. Blondelle, A. Juneja, J. Micaelli and P. Neri
Event name: 79th EAGE Conference and Exhibition 2017 - Workshops
Session: WS01 Data Science for Geosciences
Publication date: 12 June 2017
DOI: 10.3997/2214-4609.201701654
Organisations: EAGE
Language: English
Info: Abstract, PDF ( 527.94Kb )
Price: € 20

Summary:
Since its early days, the exploration and production industry has handled large volumes of data, mainly measurements, to build subsurface models used for strategic or technical decisions. More recently, data analytics technologies have emerged to complement the modelling tools, with notable successes in the domain of field monitoring. But the broader adoption of new analytical tools is made difficult due to the limited access to the large percentage of relevant data that is stored in unstructured formats. This issue is not new: modelling tools faced the same difficulty, but with a lower order of magnitude because each tool has a limited set of input data. Manual information extraction by skilled technicians from unstructured documents to feed sophisticated enterprise data models and modelling tools was acceptable even if it represented a poor use of a trained professional’s time. Despite these efforts, it is estimated that only 20% of the information available in our industry is stored in structured, searchable databases. Analytical tools require much more than that to perform adequately. With the emergence of new analytics tools, our industry now has a much greater appetite for data than it has ever had before. Is Machine Learning the means to satisfy it?


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