Abstract
The situation of an off-center casing under non-uniform ground stress can occur in the process of drilling a salt-gypsum formation, and the related casing stress calculation has not yet been solved analytically. In addition, the experimental equipment in many cases cannot meet the actual conditions and the experimental cost is very high. These comprehensive factors cause the existing casing design to not meet the actual conditions and cause casing deformation, affecting the drilling operation in Tarim oil field. The finite element method is the only effective method to solve this problem at present, but the re-modelling process is time-consuming because of the changes in the parameters, such as the cement properties, casing centrality, and the casing size. In this article, an artificial intelligence method based on support vector machine (SVM) to predict the maximum stress of an off-center casing under non-uniform ground stress has been proposed. After a program based on a radial basis function (RBF)-support vector regression (SVR) (ε-SVR) model was established and validated, we constructed a data sample with a capacity of 120 by using the finite element method, which could meet the demand of the nine-factor ε-SVR model to predict the maximum stress of the casing. The results showed that the artificial intelligence prediction method proposed in this manuscript had satisfactory prediction accuracy and could be effectively used to predict the maximum stress of an off-center casing under complex downhole conditions.
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References
Clinedinst W O. A rational expression for the critical collapsing pressure of pipe under external pressure. In: Drilling and Production Practice. New York: American Petroleum Institute, 1939. 383–391
American Petroleum Institute. Bulletin on formulas and calculations for casing, tubing, drill pipe and line pipe properties. The API Bulletin No.5C3, 1992
Han J Z, Shi T H. Equations calculate collapse pressures for casing strings. Oil Gas J, 2001, 99: 44–47
Han J Z, Shi T H. Non uniform loading affects casing collapse resistance. Oil Gas J, 2001, 99: 45–48
Yin Y Q, Li P E. Computation of casing strength under non-uniform load. Acta Petrol Sin, 2007, 28: 138–141
Fang J, Wang Y, Gao D. On the collapse resistance of multilayer cemented casing in directional well under anisotropic formation. J Nat Gas Sci Eng, 2015, 26: 409–418
Chen Z, Zhu W, Di Q. Elasticity solution for the casing under linear crustal stress. Eng Failure Anal, 2018, 84: 185–195
Li J, Chen M, Zhang H, et al. Effects of cement sheath elastic modulus on casing external collapse load. J Univ Petrol, China, 2005, 29: 41–44
Chen S, Chen L, Luo H, et al. Enzymatic activity characterization of SARS coronavirus 3C-like protease by fluorescence resonance energy transfer technique1. Acta Pharmacol Sin, 2005, 26: 99–106
Taheri S R, Pak A, Shad S, et al. Investigation of rock salt layer creep and its effects on casing collapse. Int J Min Sci Tech, 2020, 30: 357–365
Wang X, Qu Z, Dou Y, et al. Loads of casing and cement sheath in the compressive viscoelastic salt rock. J Pet Sci Eng, 2015, 135: 146–151
Zhu H Y, Deng J G, Zhao J, et al. Cementing failure of the casing-cement-rock interfaces during hydraulic fracturing. Comput Concrete, 2014, 14: 91–107
Zhu H Y, Deng J G, Chen Z G, et al. Hydraulic fracture initiation and propagation of highly deviated well with oriented perforation completion technique. J Mines Metals Fuels, 2018, 66: 116–126
Song J S, Bowen J, Klementich F. The internal pressure capacity of crescent-shaped wear casing. In: Proceedings of the SPE/IADC Drilling Conference. New Orleans, 1992. SPE23902
Wang X Z, Dou Y H, Yang J H. An analysis for the stress of eccentric worn casing with bipolar coordinates. Oil Field Equip, 2006, 35: 42–45
Sun Y X, Lin Y H, Wang Z S, et al. A new OCTG strength equation for collapse under external load only. J Pressure Vessel Tech, 2011, 133: 011702
Lin Y, Deng K, Qi X, et al. A new crescent-shaped wear equation for calculating collapse strength of worn casing under uniform loading. J Pressure Vessel Tech, 2015, 137: 031201
Tamano T, Mimaki T, Yanagimoto S. A new empirical formula for collapse resistance of commercial casing. J Energy Res Techy, 1983, 43: 489–495
Chen Z, Zhu W, Di Q, et al. Numerical and theoretical analysis of burst pressures for casings with eccentric wear. J Pet Sci Eng, 2016, 145: 585–591
Chen Z, Yan S, Ye H, et al. Effect of the Y/T on the burst pressure for corroded pipelines with high strength. J Pet Sci Eng, 2017, 157: 760–766
Huang X, Mihsein M, Kibble K, et al. Collapse strength analysis of casing design using finite element method. Int J Pressure Vessels Piping, 2000, 77: 359–367
Wang T, Yan X, Wang J, et al. Investigation of the ultimate residual strengthen of a worn casing by using the arc-length algorithm. Eng Failure Anal, 2013, 28: 1–15
Rodriguez W J, Fleckenstein W W, Eustes A W. Simulation of collapse loads on cemented casing using finite element analysis. In: Proceedings of the SPE Annual Technical Conference and Exhibition. Denver, 2003. SPE84566
Mueller D T, GoBoncan V, Dillenbeck R L, et al. Characterizing casing-cement-formation interactions under stress conditions: Impact on long-term zonal isolation. In: Proceedings of the SPE Annual Technical Conference and Exhibition. Houston, 2004. SPE90450
Pattillo P D, Last N C, Asbill W T. Effect of nonuniform loading on conventional casing collapse resistance. SPE Drilling Completion, 2004, 19: 156–163
Nabipour A, Joodi B, Sarmadivaleh M. Finite element simulation of downhole stresses in deep gas wells cements. In: Proceedings of the SPE Deep Gas Conference and Exhibition. Manama, 2010. SPE132156
Chen Z F, Zhu W P, Di Q F. Effects of eccentricity of casing on collapse resistance in non-uniform in-situ stresses. J Shanghai Univ (Nat Sci Ed), 2012, 18: 83–86
Dou Y H. Analysis of confining pressure of casing in different viscosity of casing and wellbore in viscoelastic surrounding rock. Oil Drill Product Tech, 1989, 11: 1–6
Silver D, Schrittwieser J, Simonyan K, et al. Mastering the game of Go without human knowledge. Nature, 2017, 550: 354–359
Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer, 1995
Vapnik V N. Statistical Learning Theory. New York: Wiley, 1998
Cortes C, Vapnik V N. Support-vector networks. Machine Learning, 1995, 20: 273–297
Zhao B, Zhou H, Li X, et al. Water saturation estimation using support vector machine. In: Proceedings of the Society of Exploration Geophysicists International Exposition and 76th Annual Meeting. New Orleans, 2006
Anifowose F A, Ewenla A A, Eludiora S I. Prediction of oil and gas reservoir properties using support vector machines. In: Proceedings of the International Petroleum Technology Conference. Bangkok, 2011
Boser B E, Guyon I M, Vapnik V N. A training algorithm for optimal margin classifiers. In: Proceedings of the 5th Annual Workshop on Computational Learning Theory. Pittsburgh, 1992
Lian Z, Zhang Q, Lin T, et al. Experimental study and prediction model of casing wear in oil and gas wells. J Pressure Vessel Tech, 2016, 138: 1404–1410
Samui P, Sitharam T G. A comparative study of ordinary kriging and support vector machine models for the spatial variability of rock depth in Bangalore. In: Geocongress: Characterization, Monitoring and Modeling of Geosystems. New Orleans: The American Society of Civil Engineers, 2008
Chen W, Di Q, Ye F, et al. Flowing bottomhole pressure prediction for gas wells based on support vector machine and random samples selection. Int J Hydrogen Energy, 2017, 42: 18333–18342
Zhao D A, Chen Z M, Cai X L, et al. Analysis of distribution rule of geo stress in China. Chin J Rock Mech Eng, 2007, 6: 1265–1271
Cheng W R. Concrete Structure Design Principle (in Chinese). Beijing: China Building Industry Press, 2005
Xiao M H, Wang Y H, Zhang Y. Groundwork and Foundation (in Chinese). Beijing: Peking University Press, 2009
Huang J Q, Di Q F, Liu H L, et al. The designing and checking method of casing string in salt-gypsum formation based on actual well path. Drill Product Tech, 2009, 32: 61–64
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This work was supported by the National Natural Science Foundation of China (Grant Nos. U1663205, 51704191 and 51804194), the Shanghai Leading Academic Discipline Project (Grant No. S30106), the Shanghai Municipal Education Commission (Peak Discipline Construction Program), and the Shanghai Sailing Program (Grant No. 17YF1428000).
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Di, Q., Wu, Z., Chen, T. et al. Artificial intelligence method for predicting the maximum stress of an off-center casing under non-uniform ground stress with support vector machine. Sci. China Technol. Sci. 63, 2553–2561 (2020). https://doi.org/10.1007/s11431-019-1694-4
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DOI: https://doi.org/10.1007/s11431-019-1694-4