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Abstract

Summary

There is a large amount of historic and valuable well information available stored either on paper and more recently as digital documents and reports in the oil and gas industry especially by national data management systems and oil companies. These technical documents contain valuable information from disciplines like geoscience and engineering and are in general stored in a unstructured format. To extract and utilize all this well data, a machine learning-enabled platform, consisting of a carefully selected sequence of algorithms, has been developed as a hybrid cloud container that automatically reads and understands the technical documents with little human supervision. The user can upload raw data to the platform, which are stored on a private local server. The machine learning algorithms are activated and implement the necessary processing and workflows. Structured data is generated as output, which are pushed through to a search engine that is accessible to the user in the cloud. The aim of the platform is to ease the identification of important parts of the technical documents, automatically extract relevant information and visualize it for the user, so they can easily do further analysis, share it with colleagues or agnostically port it to other platforms as input.

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/content/papers/10.3997/2214-4609.201803031
2018-11-30
2020-04-01
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References

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    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201803031
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