Real Time Stuck Pipe Prediction by Using a Combination of Physics-Based Model and Data Analytics Approach
- Feifei Zhang (Yangtze University) | Aminul Islam (Equinor ASA) | Hao Zeng (Sinopec) | Zengwei Chen (Sinopec) | Yijin Zeng (Sinopec) | Xi Wang (Yangtze University) | Siyang Li (Yangtze University)
- Document ID
- Society of Petroleum Engineers
- Abu Dhabi International Petroleum Exhibition & Conference, 11-14 November, Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- physics based model, data analytics, hybrid approach, real time monitoring
- 7 in the last 30 days
- 121 since 2007
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Nearly one third of the drilling time lost is caused by stuck drill pipe. In many cases, stuck pipe is preventable if early signs are detected and timely measures are taken, particularly for stuck pipe events caused by solids (primarily drilled cuttings) in the wellbore. This paper presents a physics-based model and data analytics combined approach to predict stuck pipe caused during drilling.
The new proposed method combines the physics-based first principle models, including transient solid transport model, drill string model (torque and drag model) and the data-driven models. The proposed models will be worked based on the analysis of both field and experimental data. The physics-based models capture the basic rules of fluid mechanics, drill string mechanics, and multiphase flow during the drilling operations.
By analyzing field data from historical wells and experimental data, an EnKF based data-driven model is applied to provide parameters and coefficients needed by the physics-based models. The data-driven model improves the reliability of the results predicted by the first-principle based model and allows it to continuously improve itself.
Based on a transient approach, this development can use real-time drilling operational data as inputs, predict the stuck pipe risks, and provide warnings when a high risk for stuck pipe scenarios are encountered. Comparing to existing stuck pipe prediction approaches, the new proposed approach can distinguish the hole cleaning related stuck pipe risk and other reasons to create stuck pipe. This hybrid method in build technology will use as a supporting tool in decision make. Resulting to bring an opportunity for the drillers to avoid the potential stuck pipe incidents by taking a proper action in time.
|File Size||1 MB||Number of Pages||16|
Belaskie, J. P., McCann, D. P., & Leshikar, J. F. (1994, January 1). A Practical Method To Minimize Stuck Pipe Integrating Surface and MWD Measurements. Society of Petroleum Engineers. doi:10.2118/27494-MS
Biegler, M. W., & Kuhn, G. R. (1994, January 1). Advances in Prediction of Stuck Pipe Using Multivariate Statistical Analysis. Society of Petroleum Engineers. doi:10.2118/27529-MS
Bradley, W. B., Jarman, D., Plott, R. S. 1991. A Task Force Approach to Reducing Stuck Pipe Costs. Presented at the SPE/IADC Drilling Conference, Amsterdam, 11–14 March. SPE-21999-MS. https://doi.org/10.2118/21999-MS.
Evensen, G., 2009, Data Assimilation-The Ensemble Kalman Filter, Springer, DOI 10.1007/978-3-642-03711-5
Guzman, J. M., Khalil, M. E., Orban, N. 2012. Stuck-Pipe Prevention Solutions in Deep Gas Drilling; New Approaches. Presented at SPE Saudi Arabia Section Technical Symposium and Exhibition, Al-Khobar, Saudi Arabia, 8–11 April. SPE-160875-MS. https://doi.org/10.2118/160875-MS.
Massie, G.W., Castle-Smith, J., Lee, J.W., and Ramsey, M.S. 1995. Amoco's Training Initiative Reduces Wellsite Drilling Problems. Petroleum Engineer International 67(3). http://www.osti.gov/scitech/biblio/39900.
Murillo, A., Neuman, J., & Samuel, R. (2009, January 1). Pipe Sticking Prediction and Avoidance Using Adaptive Fuzzy Logic Modeling. Society of Petroleum Engineers. doi:10.2118/120128-MS
Hempkins, W. B., Kingsborough, R. H., Lohec, W. E., & Nini, C. J. (1987, September 1). Multivariate Statistical Analysis of Stuck Drillpipe Situations. Society of Petroleum Engineers. doi:10.2118/14181-PA
Howard, J. A., & Glover, S. B. (1994, January 1). Tracking Stuck Pipe Probability While Drilling. Society of Petroleum Engineers. doi:10.2118/27528-MS
Salminen, K., Cheatham, C., Smith, M., & Valiullin, K. (2017, September 1). Stuck-Pipe Prediction by Use of Automated Real-Time Modeling and Data Analysis. Society of Petroleum Engineers. doi:10.2118/178888-PA
Siruvuri, C., Nagarakanti, S., & Samuel, R. (2006, January 1). Stuck Pipe Prediction and Avoidance: A Convolutional Neural Network Approach. Society of Petroleum Engineers. doi:10.2118/98378-MS
Shumway, R.H., Stoffer, D.S., 2000, Time Series Analysis and Its Applications: With R Examples, Spring, Doi 10.1007/978-1-4419-7865-3
Tang, H., Zhang, S., Zhang, F.*, Venugopal, S., 2019, Time Series Data Analysis for Automatic Flow Influx Detection during Drilling, Journal of Petroleum Science and Engineering, Vol(172), pages 1103–1111, https://doi.org/10.1016/j.petrol.2018.09.018.
Weakley, R. R. (1990, January 1). Use of Stuck Pipe Statistics To Reduce the Occurrence of Stuck Pipe. Society of Petroleum Engineers. doi:10.2118/20410-MS
Wisnie, A. P., & Zhu, Z. (1994, January 1). Quantifying Stuck Pipe Risk in Gulf of Mexico Oil and Gas Drilling. Society of Petroleum Engineers. doi:10.2118/28298-MS