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
- Show more detail
- View rights & permissions
| 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 |
Kutchko, B. G., Strazisar, B. R., Dzombak, D. A.. 2007. Degradation of Well Cement by CO2 under Geologic Sequestration Conditions. Environ. Sci. Technol. 41 (13):4787-4792. https://doi.org/10.1021/es062828c.
Lind, Y.B., Kabirova, A.R.2014. Artificial Neural Networks in Drilling Troubles Prediction. Presented at the SPE Russian Oil and Gas Exploration & Production Technical Conference and Exhibition, 14-16 October, Moscow, Russia. SPE-171274-MS. https://doi.org/10.2118/171274-MS.
Lovett, W. T. and Atkins, R. J. 1996. Development of the Cleveland, Tight Gas Sand, in the Texas Panhandle. Presented at the SPE Mid-Continent Gas Symposium, Amarillo, Texas, USA. 28-30 April. SPE-35262-MS. https://doi.org/10.2118/35262-MS.
Ma, F.Y. 2012. Chapter 7: Corrosive Effects of Chlorides on Metals. In Pitting Corrosion; Rijeka, Croatia,; pp. 140-178. DOI: 10.5772/32333.
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. SPE-193776-MS. https://doi.org/10.2118/193776-MS.
Noshi, C. I., S. F. Noynaert, Schubert, J.J. 2018a. Casing Failure Data Analytics: A Novel Data Mining Approach in Predicting Casing Failures for Improved Drilling Performance and Production Optimization. Presented at the SPE Annual Technical Conference and Exhibition, 24-26 September, Dallas, Texas, USA. SPE-191570-MS. https://doi.org/10.2118/191570-MS.
Noshi, C. I., S. F. Noynaert, Schubert, J.J. 2018b. Failure Predictive Analytics Using Data Mining: How to Predict Unforeseen Casing Failures? Presented at the Abu Dhabi International Petroleum Exhibition & Conference, 12-15 November, Abu Dhabi, UAE. SPE-193194-MS.https://doi.org/10.2118/193194-MS.
O'Brien, T.B. 1996. A Case Against Cementing Casing - Casing Annuli. Presented at the SPE/IADC Drilling Conference, New Orleans, Louisiana, 12 - 15 March. SPE-35106-MS. https://doi.org/10.2118/35106-MS.
Schremp, F. W. and Roberson, G. R. 1975. Effect of Supercritical Carbon Dioxide (CO2) on Construction Materials. SPE J. 15 (3): 227-233. SPE-4667-PA. https://doi.org/10.2118/4667-PA.
Watson T. L. and Bachu, S. 2008. Identification of Wells With High CO2-Leakage Potential in Mature Oil Fields Developed for CO2-Enhanced Oil Recovery. Presented at the SPE Symposium on Improved Oil Recovery, Tulsa, Oklahoma, USA. 20-23 April. SPE-112924-MS. https://doi.org/10.2118/112924-MS.
Watson, T. L., Getzlaf, D., and Griffith, J. E. 2002. Specialized Cement Design and Placement Procedures Prove Sucessful for Mitigating Casing Vent Flows-Case Histories. Presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, 30 April-2 May. SPE-76333-MS. https://doi.org/10.2118/76333-MS.
Yuan, Z., Schubert, J., Teodoriu, C.. 2012. HPHT Gas Well Cementing Complications and its Effect on Casing Collapse Resistance. Presented at the SPE Oil and Gas India Conference and Exhibition, Mumbai, India. 28-30 March. SPE-153986-MS. https://doi.org/10.2118/153986-MS.
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