Prediction of sand production onset in petroleum reservoirs using a reliable classification approach

Abstract

Controlling sand production in the petroleum industry has been a long-standing problem for more than 70 years. To provide technical support for sand control strategy, it is necessary to predict the conditions at which sanding occurs. To this end, for the first time, least square support machine (LSSVM) classification approach, as a novel technique, is applied to identify the conditions under which sand production occurs. The model presented in this communication takes into account different parameters that may play a role in sanding. The performance of proposed LSSVM model is examined using field data reported in open literature.

It is shown that the developed model can accurately predict the sand production in a real field. The results of this study indicates that implementation of LSSVM modeling can effectively help completion designers to make an on time sand control plan with least deterioration of production.

Keywords

Sand production / Least square SVM / ROC graph / Classification description / Modeling / Sanding onset

References

[1] References [1 S.R. Amendolia, et al., A comparative study of K-nearest neighbour, support vector machine and multi-layer perceptron for thalassemia screening, Chemom. Intell. Lab. Syst. 69 (1-2) (2003) 13-20.

[2] HARV, et al., The diagnosis, well damage evaluation and critical drawdown calculations of sand production problems in the Ceuta Field, Lake Maracaibo, Venezuela, in: Latin American and Caribbean Petroleum Engineering Conference, Caracas, Venezuela, 1999.

[3] H. Rahmati, et al., Review of sand production prediction models, J. Petrol. Eng. 2013 (2013) 16.

[4] G. Servant, P. Marchina, J.-F. Nauroy, Near-wellbore modeling: sand production issues,in:SPE Annual Technical Conference and Exhibition, Anaheim, California, U.S.A, 2007.

[5] D. Tiab, S.A. Rbeawi, The impact of sand and asphaltic production problems on pressure behavior and flow regimes, in: 2012 SPE Kuwait International Petroleum Conference and Exhibition, Kuwait City, Kuwait, 2012.

[6] S.M. Willson, Z.A. Moschovidis, J.R. Cameron, I.D. Palmer, New model for predicting the rate of sand production, in: SPE/ISRM Rock Mechanics Conference, Irving, Texas, 2002.

[7] J. Wang, A. Settari, D. Walters, R. Wan, An Integrated Modular Approach to Modeling Sand Production and Cavity Growth with Emphasis on the Multiphase and 3D Effects, 41st U.S. Symposium on Rock Mechanics, American Rock Mechanics Association, Colorado, 2006.

[8] L. Zhang, M.B. Dusseault, Sand-production simulation in heavy-oil reservoirs, SPE Reserv. Eval. Eng. 7 (6) (2004) 399-407.

[9] L.C.B. Bianco, P.M. Halleck, Mechanisms of arch instability and sand production in two-phase saturated poorly consolidated sandstones, in: SPE European Formation Damage Conference, the Hague, Netherlands, 2001.

[10] G. Moricca, G. Ripa, F. Sanfilippo, F.J. Santarelli, Basin Scale Rock Mechanics: Field Observations of Sand Production, Rock Mechanics in Petroleum Engineering, Delft, Netherlands, 1994.

[11] F. Sanfilippo, G. Ripa, M. Brignoli, F.J. Santarelli, Economical management of sand production by a methodology validated on an extensive database of field data, in: SPE Annual Technical Conference and Exhibition, Dallas, Texas, 1995.

[12] N. Morita, D. Whitfill, O. Fedde, T. Levik, Parametric study of sand-production prediction: analytical approach, SPE Prod. Eng. 4 (1) (1989) 25-33.

[13] N. Morita, P.A. Boyd, Typical sand production problems case studies and strategies for sand control, in: SPE Annual Technical Conference and Exhibition, Dallas, Texas, 1991.

[14] Q.T. Doan, L.T. Doan, S.M.F. Ali, M. Oguztoreli, Sand deposition inside a horizontal well-a simulation approach, J. Can. Petrol. Technol. 39 (10) (2000).

[15] M. Kanj, Y. Abousleiman, Realistic sanding predictions: a neural approach,in:SPE Annual Technical Conference and Exhibition, Houston, Texas, 1999.

[16] M. Azad, G. Zargar, R. Arabjamaloei, A. Hamzei, M. Ekramzadeh, A new approach to sand production onset prediction using artificial neural networks, Petrol. Sci. Technol. 29 (19) (2011) 1975-1983.

[17] A. Chamkalani, et al., Soft computing method for prediction of CO₂ corrosion in flow lines based on neural network approach, Chem. Eng. Commun. 200 (6) (2013) 731-747.

[18] A. Chapoy, A.-H. Mohammadi, D. Richon, Predicting the hydrate stability zones of natural gases using artificial neural networks, Oil Gas Sci. Technol. Revue de l'IFP 62 (5) (2007) 701-706.

[19] R. Gharbi, A. Elsharkawy, Neural network model for estimating the PVT properties of Middle East crude oils, SPE Reserv. Eval. Eng. 2 (3) (1999) 255-265.

[20] S.M.J. Majidi, A. Shokrollahi, M. Arabloo, R. Mahdikhani-Soleymanloo, M. Masihi, Evolving an accurate model based on machine learning approach for prediction of dew-point pressure in gas condensate reservoirs, Chem. Eng. Res. Des. 92 (5) (2014) 891-902.

[21] M. Arabloo, M.-A. Amooie, A. Hemmati-Sarapardeh, M.-H. Ghazanfari, A.H. Mohammadi, Application of constrained multi-variable search methods for prediction of PVT properties of crude oil systems, Fluid Phase Equilibr. 363 (2014) 121-130.

[22] K.-S. Shin, T.S. Lee, H.-j. Kim, An application of support vector machines in bankruptcy prediction model, Expert Syst. Appl. 28 (1) (2005) 127-135.

[23] S.R. Taghanaki, et al., Implementation of SVM framework to estimate PVT properties of reservoir oil, Fluid Phase Equilibr. 346 (2013) 25-32.

[24] F.E.H. Tay, L. Cao, Application of support vector machines in financial time series forecasting, Omega 29 (4) (2001) 309-317.

[25] B. Verlinden, J.R. Duflou, P. Collin, D. Cattrysse, Cost estimation for sheet metal parts using multiple regression and artificial neural networks: a case study, Int. J. Prod. Econ. 111 (2) (2008) 484-492.

[26] A. Baylar, D. Hanbay, M. Batan, Application of least square support vector machines in the prediction of aeration performance of plunging overfall jets from weirs, Expert Syst. Appl. 36 (4) (2009) 8368-8374.

[27] T.-S. Chen, et al., A novel knowledge protection technique base on support vector machine model for anti-classification,in: M. Zhu (Ed.), Electrical Engineering and Control. Lecture Notes in Electrical Engineering, Springer Berlin Heidelberg, 2011, pp. 517-524.

[28] A. Fayazi, M. Arabloo, A.H. Mohammadi, Efficient estimation of natural gas compressibility factor using a rigorous method, J. Nat. Gas Sci. Eng. 16 (2014) 8-17.

[29] A. Fayazi, M. Arabloo, A. Shokrollahi, M.H. Zargari, M.H. Ghazanfari, Stateof-the-Art least square support vector machine application for accurate determination of natural gas viscosity, Ind. Eng. Chem. Res. 53 (2) (2013) 945-958.

[30] M. Mesbah, E. Soroush, A. Shokrollahi, A. Bahadori, Prediction of phase equilibrium of CO2/Cyclic compound binary mixtures using a rigorous modeling approach, J. Supercrit. Fluids 90 (2014) 110-125.

[31] I. Nejatian, M. Kanani, M. Arabloo, A. Bahadori, S. Zendehboudi, Prediction of natural gas flow through chokes using support vector machine algorithm, J. Nat. Gas Sci. Eng. 18 (2014) 155-163.

[32] H. Safari, et al., Prediction of the aqueous solubility of BaSO 4 using pitzer ion interaction model and LSSVM algorithm, Fluid Phase Equilibr. 374 (2014) 48-62.

[33] A. Shokrollahi, M. Arabloo, F. Gharagheizi, A.H. Mohammadi, Intelligent model for prediction of CO₂ e reservoir oil minimum miscibility pressure, Fuel 112 (2013) 375-384.

[34] E.D. Übeyli, Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals, Expert Syst. Appl. 37 (1) (2010) 233-239.

[35] C. Cortes, V. Vapnik, Support-vector networks, Mach. Learn 20 (3) (1995) 273-297.

[36] M. Curilem, G. Acu-na, F. Cubillos, E. Vyhmeister, Neural networks and support vector machine models applied to energy consumption optimization in semiautogeneous grinding, Chem. Eng. Trans. 25 (2011) 761-766.

[37] K. Pelckmans, et al., LS-SVMlab: a Matlab/c Toolbox for Least Squares Support Vector Machines. Tutorial, KULeuven-ESAT, Leuven, Belgium, 2002.

[38] J.A. Suykens, J. Vandewalle, Least squares support vector machine classifiers, Neural Process. Lett. 9 (3) (1999) 293-300.

[39] A. Bazzani, et al., An SVM classifier to separate false signals from microcalcifications in digital mammograms, Phys. Med. Biol. 46 (6) (2001) 1651.

[40] J.A.K. Suykens, T.V. Gestel, J.D. Brabanter, B.D. Moor, J. Vandewalle, Least Squares Support Vector Machines, World Scientific Pub. Co., Singapor, 2002.

[41] M. Arabloo, A. Shokrollahi, F. Gharagheizi, A.H. Mohammadi, Toward a predictive model for estimating dew point pressure in gas condensate systems, Fuel Process. Technol. 116 (2013) 317-324.

[42] A. Hemmati-Sarapardeh, et al., Reservoir oil viscosity determination using a rigorous approach, Fuel 116 (2014) 39-48.

[43] A.H. Mohammadi, et al., Gas hydrate phase equilibrium in porous media: mathematical modeling and correlation, Ind. Eng. Chem. Res. 51 (2) (2011) 1062-1072.

[44] M. Minoux, Mathematical Programming:Theory and Algorithms, John Wiley and Sons, 1986.

[45] R. Fletcher, Practical Methods of Optimization, John Wiley & Sons, 2013.

[46] S. Xavier de Souza, J.A.K. Suykens, J. Vandewalle, D. Bollé, Coupled simulated annealing, IEEE Trans. Syst. Man Cybern. Part B 40 (2) (2010) 320-335.

[47] L. Breiman, J. Friedman, R. Olshen, C. Stone, Classification and Regression Trees, Wadsworth International Group, Wadsworth International Group, Belmont, CA, 1984.

[48] C.D. Brown, H.T. Davis, Receiver operating characteristics curves and related decision measures: a tutorial, Chemom. Intell. Lab. Syst. 80 (1) (2006) 24-38.

[49] T. Fawcett, An introduction to ROC analysis, Pattern Recognit. Lett. 27 (8) (2006) 861-874.

[50] J.A. Hanley, Characteristic (ROC) curvel, Radiology 743 (1982) 29-36.

[51] J.A. Hanley, B.J. McNeil, A method of comparing the areas under receiver operating characteristic curves derived from the same cases, Radiology 148 (3) (1983) 839-843.

[52] B.W. Matthews, Comparison of the predicted and observed secondary structure of T4 phage lysozyme, Biochim. Biophys. acta 405 (2) (1975) 442.