A Brief Survey of Text Mining Applications for the Oil and Gas Industry
- Christine Noshi (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
- Clustering,Classification, Information Retrieval, Text mining, Review, Information Extraction
- 4 in the last 30 days
- 262 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 7.00|
|SPE Non-Member Price:||USD 23.00|
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||3 MB||Number of Pages||13|
Arumugam, S., Rajan, S., and Gupta, S. 2017. Augmented Text Mining for Daily Drilling Reports using Topic Modeling and Ontology. Presented at the SPE Western Regional Meeting, 23 – 27 April, Bakersfield, California. SPE-185711-MS. https://doi.org/10.2118/185711-MS.
Anno, P.D., Pham, S., Ramsay, S.C. 2016. Big drilling data analytics engine. https://patents.google.com/patent/US20160333673A1/en, Google Patents.
Bateman, D.T., Phillips, A.E., Drennan, J.C., Langdon, W.H. 2016. Natural Language Processing for Extracting Conveyance Graphs. https://patents.google.com/patent/US9251139B2/en Google Patents.
Brestoff, N.E. 2017. Using classified text and deep learning algorithms to identify risk and provide early warning. https://patents.google.com/patent/US9552548B1 Google Patents.
Gantz, J., & Reinsel, D. 2012. The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Analyze the future: 1–16. https://www.emc.com/collateral/analyst-reports/idc-the-digital-universe-in-2020.pdf.
Noshi, C. and Schubert, J.J. 2018. The Role of Machine Learning in Drilling Operations; A Review. Presented at the SPE/AAPG Eastern Regional Meeting, 7–11 October, Pittsburgh, Pennsylvania, USA. SPE-191823-18ERM-MS. https://doi.org/10.2118/191823-18ERM-MS.
Noshi, C. I., Assem, A. I., Schubert, J. J. 2018. The Role of Big Data Analytics in Exploration and Production: A Review of Benefits and Applications. Presented at the SPE International Heavy Oil Conference and Exhibition, 10–12 December, Kuwait City, Kuwait. https://doi.org/10.2118/193776-MS. SPE-193776-MS.
Pennington, J., Socher, R., and Manning, C. 2014. Glove: Global Vectors for Word Representation. Presented at the Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, 25–29 October. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing: 1532–1543.
Sidahmed, M., Coley, C. J., and Shirzadi, S. 2015. Augmenting Operations Monitoring by Mining Unstructured Drilling Reports. Presented at the SPE Digital Energy Conference and Exhibition held in The Woodlands, Texas, USA, 3–5 March. SPE-173429-MS. https://doi.org/10.2118/173429-MS.
Wu, W., Lu, X., Cox, B.. 2014. Retrieving Information and Discovering Knowledge from Unstructured Data Using Big Data Mining Technique: Heavy Oil Fields Example. Presented at the International Petroleum Technology Conference, 10-12 December, Kuala Lumpur, Malaysia. IPTC-17805-MS. https://doi.org/10.2523/IPTC-17805-MS.
Vennelakanti, R., Dayal, U., Gupta, C. 2015. Oil and gas rig data aggregation and modeling system. https://patents.google.com/patent/US20150278407 Google Patents.