Casing Failure Data Analytics: A Novel Data Mining Approach in Predicting Casing Failures for Improved Drilling Performance and Production Optimization
- C. I. Noshi (Texas A&M University) | S. F. Noynaert (Texas A&M University) | J. J. Schubert (Texas A&M University)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 24-26 September, Dallas, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 2.4 Hydraulic Fracturing, 7.6.4 Data Mining, 3.2.7 Lifecycle Management and Planning, 1.6 Drilling Operations, 4 Facilities Design, Construction and Operation, 7 Management and Information, 3 Production and Well Operations, 3 Production and Well Operations, 2.5.2 Fracturing Materials (Fluids, Proppant), 4.1 Processing Systems and Design, 1.6.6 Directional Drilling, 4.1.2 Separation and Treating, 7.6 Information Management and Systems, 3 Production and Well Operations, 2 Well completion, 3.2 Well Operations and Optimization
- Supervised and unsupervised Algorithms, Casing Failure, Predictive Analytics, Data Mining, Data Analytics
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The last decade has spotted a tremendous upsurge in casing failures. The aftermaths of casing failure can include the possibility of blowouts, environmental pollution, injuries/fatalities, and loss of the entire well to name a few. The motivation behind this work is to present findings from a predictive analytics investigation of casing failure data using supervised and unsupervised data mining algorithms. Scientists and researchers have speculated the potential underlying causes of failure but to date this type of work remains unpublished and unavailable in the public domain literature.
The study assembled comprehensive data from eighty land-based wells during drilling, fracturing, workover, and production operations. Twenty wells suffered from casing failure while the remaining sixty offset wells were compiled from well reports, fracturing treatment data, drilling records, and recovered casing data. The failures were unsystemic but included fatigue failure, bending stresses from excessive dogleg, buckling, high hoop stress on connections, and split coupling. The failures were detected at various depths, both in cemented and uncemented hole sections. Failures were spotted at the upper and lower production casing. Using a predictive analytics software from SAS, twenty-six variables were evaluated through the application of various data mining techniques on the failed casing data points. The missing data was accounted for using multivariate normal imputation. The study outcome addressed common casing sizes and couplings involved with each failure, failure location, hydraulic fracturing stages, cement impairment, dogleg severity, thermal and tensile loads, production-induced shearing, and DLS. The predictive algorithms used in this study included Logistic Regression, supervised Hierarchal Clustering, and Decision Trees. While the descriptive analytics manifested in visual representations included Scatterplot Matrices and PivotTables. A combination of the causes of failure were identified. A total of five statistical techniques using the aforementioned algorithms were developed to evaluate the concurrent effect of the interplay of these variables. Nineteen variables were believed to possess a high contribution to failure. Scatterplot matrix suggested a complex correlation between the total base water used in fracturing simulation and casing thickness. Logistic Regression suggested nine variables were significant including: TVD, operator, frac start month, MD of most severe DL, heel TVD, hole size, BHT, total proppant mass, cumulative DLS in lateral and build sections variables as significant failure contributors. PivotTables showed that the rate of casing failure was highest during the winter season.
This investigation is aimed to develop a thorough understanding of casing failures and the myriad of contributing factors to develop comprehensive predictive models for future failure prediction via the application of data mining algorithms. These models intend to provide a theoretical and statistical basis for cost-effective, safe, and better drilling practices.
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