A Brief Survey of Text Mining Applications for the Oil and Gas Industry
- Christine Noshi (Texas A&M University) | Jerome Schubert (Texas A&M University)
- Document ID
- International Petroleum Technology Conference
- International Petroleum Technology Conference, 26-28 March, Beijing, China
- Publication Date
- Document Type
- Conference Paper
- 2019. International Petroleum Technology Conference
- Information Retrieval, Information Extraction, Clustering,Classification, Review, Text mining
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- 107 since 2007
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Data bases of numerous oil and gas companies embrace very promising potential for more informed decision-making processes. Furthermore, there is an exponential growth in the influx of generated data from an escalating parade of systems encompassing Enterprise Resource Planning (ERP), machine instrumentation, sensory networks, and escalating mixed-media and different unlabeled data. Despite that, extracting meaningful value from zettabyte-sized datasets remains problematic given the uncontrollable wealth of data and its subsequent noise caveats. Amongst those data warehouses, are a multitude of textual information. Accordingly, Text mining has garnered worldwide interest, as it is a crucial phase in the process of knowledge discovery automatically extracting unstructured to semi-structured information. The following survey covers Text Mining methods and approaches to explain their effectiveness in information retrieval from textual databases from various sources. Moreover, the situational types where each technique may be beneficial are explored.
|File Size||874 KB||Number of Pages||13|
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