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
- 7.6.4 Data Mining, 7.6 Information Management and Systems, 7 Management and Information, 1.6 Drilling Operations, 7.6.6 Artificial Intelligence
- Keywords
- Machine learning, Supervised Algorithms, Failure prediction, Data Mining, casing failure
- Downloads
- 55 in the last 30 days
- 251 since 2007
- Show more detail
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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 MB | Number of Pages | 23 |
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