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.

[1]  Amin Shokrollahi,et al.  State-of-the-Art Least Square Support Vector Machine Application for Accurate Determination of Natural Gas Viscosity , 2014 .

[2]  Alireza Nouri,et al.  Review of Sand Production Prediction Models , 2013 .

[3]  A Bazzani,et al.  An SVM classifier to separate false signals from microcalcifications in digital mammograms , 2001, Physics in medicine and biology.

[4]  Giovanni Luca Christian Masala,et al.  A comparative study of K-Nearest Neighbour, Support Vector Machine and Multi-Layer Perceptron for Thalassemia screening , 2003 .

[5]  Dirk Cattrysse,et al.  Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study , 2008 .

[6]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[7]  Amin Shokrollahi,et al.  Evolving an accurate model based on machine learning approach for prediction of dew-point pressure in gas condensate reservoirs , 2014 .

[8]  Farhad Gharagheizi,et al.  Gas Hydrate Phase Equilibrium in Porous Media: Mathematical Modeling and Correlation , 2012 .

[9]  Amir H. Mohammadi,et al.  Efficient estimation of natural gas compressibility factor using a rigorous method , 2014 .

[10]  Ridha Gharbi,et al.  Neural Network Model for Estimating The PVT Properties of Middle East Crude Oils , 1996 .

[11]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[12]  M. Dusseault,et al.  Sand-Production Simulation in Heavy-Oil Reservoirs , 2004 .

[13]  Alireza Bahadori,et al.  Prediction of natural gas flow through chokes using support vector machine algorithm , 2014 .

[14]  Michel Minoux,et al.  Mathematical Programming , 1986 .

[15]  Christopher D. Brown,et al.  Receiver operating characteristics curves and related decision measures: A tutorial , 2006 .

[16]  Davut Hanbay,et al.  Application of least square support vector machines in the prediction of aeration performance of plunging overfall jets from weirs , 2009, Expert Syst. Appl..

[17]  W. Poquioma,et al.  The Diagnosis, Well Damage Evaluation and Critical Drawdown Calculations of Sand Production Problems in the Ceuta Field, Lake Maracaibo, Venezuela , 1999 .

[18]  Tung-Shou Chen,et al.  A Novel Knowledge Protection Technique Base on Support Vector Machine Model for Anti-classification , 2011 .

[19]  A. Bahadori,et al.  Prediction of the aqueous solubility of BaSO4 using pitzer ion interaction model and LSSVM algorithm , 2014 .

[20]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[21]  Millaray Curilem,et al.  Neural Networks and Support Vector Machine Models Applied to Energy Consumption Optimization in Semiautogeneous Grinding , 2011 .

[22]  Amir H. Mohammadi,et al.  Predicting the Hydrate Stability Zones of Natural Gases Using Artificial Neural Networks , 2007 .

[23]  B. Matthews.  Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[24]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[25]  Kyung-shik Shin,et al.  An application of support vector machines in bankruptcy prediction model , 2005, Expert Syst. Appl..

[26]  F. J. Santarelli,et al.  Basin scale rock mechanics: Field observations of sand production , 1994 .

[27]  Farhad Gharagheizi,et al.  Intelligent model for prediction of CO2 – Reservoir oil minimum miscibility pressure , 2013 .

[28]  S. M. Willson,et al.  New Model for Predicting the Rate of Sand Production , 2002 .

[29]  Johan A. K. Suykens,et al.  LS-SVMlab : a MATLAB / C toolbox for Least Squares Support Vector Machines , 2007 .

[30]  Richard Wan,et al.  An integrated modular approach to modeling sand production and cavity growth with emphasis on the multiphase and 3D effects , 2006 .

[31]  Farhad Gharagheizi,et al.  Toward a predictive model for estimating dew point pressure in gas condensate systems , 2013 .

[32]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[33]  Elif Derya íbeyli.  Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals , 2010 .

[34]  Amir H. Mohammadi,et al.  Application of constrained multi-variable search methods for prediction of PVT properties of crude oil systems , 2014 .

[35]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[36]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[37]  J. Hanley,et al.  A method of comparing the areas under receiver operating characteristic curves derived from the same cases. , 1983, Radiology.

[38]  Ghasem Zargar,et al.  A New Approach to Sand Production Onset Prediction Using Artificial Neural Networks , 2011 .

[39]  Mazen Y. Kanj,et al.  Realistic Sanding Predictions: A Neural Approach , 1999 .

[40]  Reza Chamkalani,et al.  SOFT COMPUTING METHOD FOR PREDICTION OF CO2 CORROSION IN FLOW LINES BASED ON NEURAL NETWORK APPROACH , 2013 .

[41]  Nobuo Morita,et al.  Parametric study of sand production prediction: analytical approach. , 1989 .

[42]  Mahmood Amani,et al.  Implementation of SVM framework to estimate PVT properties of reservoir oil , 2013 .

[43]  Ali Naseri,et al.  Reservoir oil viscosity determination using a rigorous approach , 2014 .

[44]  Johan A. K. Suykens,et al.  Coupled Simulated Annealing , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[45]  A. Bahadori,et al.  Prediction of phase equilibrium of CO2/cyclic compound binary mixtures using a rigorous modeling approach , 2014 .

[46]  Nobuo Morita,et al.  Typical Sand Production Problems Case Studies and Strategies for Sand Control , 1991 .

[47]  P. M. Halleck,et al.  Mechanisms of Arch Instability and Sand Production in Two-Phase Saturated Poorly Consolidated Sandstones , 2001 .

[48]  F. J. Santarelli,et al.  Economical Management of Sand Production by a Methodology Validated on an Extensive Database of Field Data , 1995 .

[49]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..