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Data Mining Approaches for Casing Failure Prediction and Prevention

Authors
Christine Noshi (Texas A&M University) | Samuel Noynaert (Texas A&M University) | Jerome Schubert (Texas A&M University)
DOI
https://doi.org/10.2523/IPTC-19311-MS
Document ID
IPTC-19311-MS
Publisher
International Petroleum Technology Conference
Source
International Petroleum Technology Conference, 26-28 March, Beijing, China
Publication Date
2019
Document Type
Conference Paper
Language
English
ISBN
978-1-61399-619-5
Copyright
2019. International Petroleum Technology Conference
Disciplines
7.6.6 Artificial Intelligence, 7 Management and Information, 7.6 Information Management and Systems, 7.6.4 Data Mining, 1.6 Drilling Operations
Keywords
casing failure, Failure prediction, Data Mining, Machine learning, Supervised Algorithms
Downloads
32 in the last 30 days
340 since 2007
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SPE Member Price: USD 7.00
SPE Non-Member Price: USD 23.00

Recent casing failures in the Granite Wash play in the western Anadarko Basin have sparked deep concerns to operators in North Texas and Oklahoma. Hydrostatic tests made in the field show that present API standards do not assure adequate joint and bursting strength to meet deep-well requirements. Past and present literature has been infested with numerous casing failures incidents. Despite the extensive documentation and recommendations, a mounting trend of failure is still on the rise. In an attempt to find possible solutions for these failures, this study is a continuation of an on-going effort to minimize the likelihood of failure using Data Mining and Machine Learning (ML) algorithms.

The study applied both descriptive visual representations such as Mosaic and Box Plots and predictive algorithms including Artificial Neural Networks (ANN) and Boosted Ensemble trees on eighty land-based wells, of which twenty possessed casing and tubing failures. The study used a predictive analytics software and python coding to evaluate twenty-six different features compiled from drilling, fracturing, and geologic data.

This work attempts to shed light on operational problems and implement a Data Analytic approach to find out the possible factors contributing to casing failures using both descriptive and supervised ML algorithms.

File Size  2 MBNumber of Pages   23

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