The Application of Least Square Support Vector Machine as a Mathematical Algorithm for Diagnosing Drilling Effectivity in Shaly Formations

Document Type : Research Paper

Authors

1 Institute of petroleum engineering

2 Petroleum University of Technology, Ahwaz, Iran

Abstract

The problem of slow drilling in deep shale formations occurs worldwide causing significant expenses to the oil industry. Bit balling which is widely considered as the main cause of poor bit performance in shales, especially deep shales, is being drilled with water-based mud. Therefore, efforts have been made to develop a model to diagnose drilling effectivity. Hence, we arrived at graphical correlations which utilized the rate of penetration, depth of cut, specific energy, and cation exchange capacity in order to provide a tool for the prediction of drilling classes.
This paper describes a robust support vector regression (SVR) methodology that offers superior performance for important drilling engineering problems. Using the amount of cation exchange capacity of the shaly formations and correlating them to drilling parameters such as the normalized rate of penetration, depth of cut, and specific energy, the model was developed. The method incorporates hybrid least square support vector regression into the coupled simulated annealing (CSA) optimization technique (LSSVM-CSA) for the efficient tuning of SVR hyper parameters. Also, we performed receiver operating characteristic as a performance indicator used for the evaluation of classifiers. The performance analysis shows that LSSVM classifier noticeably performs with high accuracy, and adapting such intelligence system will help petroleum industries deal with the well drilling consciously.
The problem of slow drilling in deep shale formations occur worldwide causing significant expense to the oil industry. Bit balling is widely considered as the main cause of poor bit performance in shale, especially deep shale are being drilled with
water-based mud .Therefore, efforts have been made to develop a model to diagnose drilling ineffectivity/effectivity. Hence
we arrived to graphical correlations which utilized rate of penetration, depth of cut, specific energy, and cation exchange capacity in order to provide a tool for prediction of drilling classes.
This paper describes a robust support vector regression (SVR) methodology that offers superior performance for important drilling engineering problems. Using the amount of cation exchange capacity of the shaly formations and also correlating them
to drilling parameters, such as normalized rate of penetration, depth of cut, and specific energy, model was developed. The
method incorporates hybrid least square support vector regression and Coupled Simulated Annealing (CSA) optimization technique (LSSVM-CSA) for efficient tuning of SVR hyper parameters. Also, we performed Receiver Operating Characteristic as a performance indicator which used for evaluation of classifiers. Performance analysis shows that LSSVM classifier noticeably perform with high accuracy and adapting such intelligence system will help petroleum industry to dealing the well drilling consciously.

Keywords

References

REFERENCES
Aarts E. and Korst J., “Simulated Annealing and Boltzmann Machines,” New York: John Wiley and Sons, 1989, 415-417.
Abe S., “Support Vector Machines for Pattern Classification,” New York: Springer-Verlag Springer, 2005, 1-467.
Aghassi A., “Investigation of Qualitative Methods for Diagnosis of Poor Bit Performance Using Surface Drilling Parameters,” MS Thesis, Louisiana State University, 2003, 1-135.
Al-Anazi A. F. and Gates I. D., “Support Vector Regression for Porosity Prediction in a Heterogeneous Reservoir: a Comparative Study,” Computations and Geosciences, 2010, 36, 1494–1503.
Allred R. B. and McCaleb S. B., “Rx for Gumbo Shale Drilling,” SPE 4233, The Sixth Conference on Drilling and Rock Mechanics of Society of Petroleum Engineers of AIME, Dallas, TX, 1973.
Bible M., Lesage M., and Falconer I., “Method for Detecting Drilling Events from Measurement While Drilling Sensors,” USA Patent 4876886, Anadrill Inc., 1989.
Bland R., Haliday B., Illerhaus R., Isbell M., and et al., “Drilling Fluid and Bit Enhancement for Drilling Shales,” AADE Annual Technical Forum-Improvements in Drilling Fluids Technology, Houston, Texas, 1999.
Bourgoyne Jr., A. T., Chenevert M. E., Milheim K. K., and Young Jr. F.S., “Applied Drilling Engineering,” SPE Text Book Series, Richardson, TX, 1991.
Burges C. A. “Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining Knowledge Discovery 2, 1998, 121-167.
Chesser B. G. and Perricone A. C., “A Physicochemical Approach to the Prevention of Balling of Gumbo Shale,” SPE 4515, 48th Annual Fall Meeting of the Society of Petroleum Engineers of AIME, Las Vegas, NV, 1973.
Cheatham C. A. and Nahm J. J., “Bit Balling in Water-Reactive Shale During Full-Scale Drilling Rate Test,” IADC/SPE 19926, IADC/SPE Conference, Houston, 1990.
Cheatham C. A., Nahm J. J., and Heikamp N. D. “Effect of Selected Mud Properties on Rate of Penetration – Full-Scale Shale Drilling Simulations,” SPE/IADC 13465, SPE/IADC Drilling Conference, New Orleans, LA. March, 1985.
Chamkalani A., “Application of LS-SVM Classifier to Determine Stability State of Asphaltene in Oilfields by Utilizing SARA Fractions,” Petroleum Science and Technology (In press), 2011, 6-11.
Copper G. A. and Roy S., “Prevention of Bit Balling by Electro-Osmosis,” SPE 27882, SPE Western Regional Meeting, Long Beach, CA., 1994.
Demircan G., Smith J. R., and Bassiouni Z., “Estimation of Cation Exchange Using Log Data: Application to Drilling Optimization, Society of Petrophysicists and Well Log Analysts (SPWLA),” 41st Annual Logging Symposium, Dallas, Texas, June, 2000.
Du C. J. and Sun D. W., “Multi-Classification of Pizza using Computer Vision and Support Vector Machine,” Journal of Food Engineering, 2008, 86, 234-242.
Falconer I. G., Burgess T. M., and Sheppard M. C., “Separating Bit and Lithology Effects from Drilling Mechanics Data,” IADC/SPE 17191, Drilling Conference, Dallas, TX, 1988.
Fawcett T., “An Introduction to ROC Analysis,” Pattern Recognition Letters 27, 2006, 861-874.
Gao G. H., Zhang Y. Z., Zhu Y., and Duan G. H., “Hybrid Support Vector Machines-Based Multi-Fault Classification,” Journal of China University of Mining Technology, 2007, 17, 246-250.
Han J. and Kamber M., “Data Mining: Concepts and Techniques,” 3th ed., Morgan Kaufmann, San Francisco, 2011, 1-673.
Hanley J. A. and McNeil B. J., “The Meaning and Use of the Area Under a Receiver Operating Characteristic (ROC) Curve,” Radiology, 1982, 143, 29-36.
Hood J. A., Leidland B.T., Haldorsen H., and Heisig G., “Aggressive Drilling Parameter Management Based on Down-hole Vibration Diagnostic Boosts Drilling Performance in Difficult Formation,” SPE 71391, SPE Annual Technical Conference and Exhibition, New Orleans, L.A., 2001.
P. A. Watson, “Drilling Fluids,” SPE/IADC 21933, Drilling Conference, Amsterdam, Netherlands, 2001.
Horng M. H., “Multi-class Support Vector Machine for Classification of the Ultrasonic Images of Supraspinatus,” Expert System Applications, 36, 2009, 8124-8133.
Ipek G., Smith J. R., and Bassiouni Z., “Diagnosis of Ineffective Drilling Using Cation Exchange Capacity of Shaly Formations,” Journal of Canadian Petroleum Technology, 2006, 45, 26-30.
Keerthi S. S. and Lin C. J., “Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel,” Neural Computation, 2003, 15, 1667-1689.
King C. H., Pinckard M. D., Krishnamoorthy K., and Benton D. F., “Method of and System for Optimizing Rate of Penetration in Drilling Operations,” USA Patent 6155357, Noble Drilling Services Inc., 2000.
King C. H., Pinckard M. D., Sparling D. P., and Weegh A. O. D., “Method of and System for Monitoring Drilling Parameters,” USA Patent 6152246, Noble Drilling Services Inc., 2000.
Kirkpatrick S., Gelatt Jr. C., and Vecchi M., “Optimization by Simulated Annealing,” Science, 1983, 220, 671-680.
Lee Y. K. and Lee C. K., “Classification of Multiple Cancer Types by Multi-Category Support Vector Machines using Gene Expression Data,” Bioinformatics, 2003, 19, 1132-1139.
Ledgerwood L. W. and Salisbury D. P., “Bit Balling and Well-Bore Instability of Down-Hole Shale,” SPE 22578, 66th Annual Technical Conference and Exhibition of Society of Petroleum Engineers, Dallas, TX, October, 1991.
Li D., Yang W., and Wang S., “Classification of Foreign Fibers in Cotton Lint using Machine Vision and Multi-class Support Vector Machine,” Journal of Computers and Electronics in Agriculture, 2010, 74, 274-279.
Mike Stephens, Sandra Gomez-nava and Marc Churan, “Methylene Blue Test for Drill Solids and Commercial Bentonites,” Section 12 in: API Recommended Practices 13I: Laboratory Testing of Drilling Fluids, 7th ed. and ISO 10416:2002, American Petroleum Institute, 2004, 34-38.
O’Brien D. E. and Chenevert M. E., “Stabilizing Sensitive Shale with Inhibited, Potassium Base Drilling Fluids,” Journal of Petroleum Technology, 1973, 25, 1089-1100.
Pessier R. C. and Fear M. J., “Quantifying Common Drilling Problems with Mechanical Specific Energy and a Bit-specific Coefficient of Sliding Friction,” SPE 24584, SPE Annual Technical Conference and Exhibition, Washington D. C., 1992.
Pinckard M. D., “Method of and System for Optimizing Rate of Penetration in Drilling Operations,” USA Patent 6192998, Noble Drilling Services Inc., 2001.
Roy S. and Cooper G. A., “Prevention of Bit Balling in Shale: Some Preliminary Results,” IADC/SPE 23870, IADC/SPE Drilling Conference, New Orleans, LA., 1992.
Salzberg S. L., “On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach,” Data Mining Knowledge Discovery Journal, 1997, 1, 317-327.
Smith J. R., “Performance Analysis of Deep PDC Bits Runs in Water-Base Muds,” ETCE 2000, ASME - Drilling Technology Symposium,” Houston, Texas, 2000.
Smith J. R., “Diagnosis of Poor PDC Bit Performance in Deep Shales,” Dissertation, Louisiana State University, 1998, 1-103.
Smith J. R., “Drilling Over-Pressured Shales with PDC Bits: A Study of Rock Characteristics and Field Experience Offshore Texas,” Thesis, Louisiana State University, 1995.
Smith J. R. and Lund J. B., “Single Cutter Tests Demonstrate Cause of Poor PDC Bit Performance in Deep Shales,” ETCE 2000, ASME -Drilling Technology Symposium, Houston, Texas, 2000.
Smith J. R., “Addressing the Problem of PDC Bit Performance in Deep Shales,” IADC/SPE 47814, IADC/SPE Asia Pacific Drilling Conference, Jakarta, Indonesia, 1998.
Smith L., Mody F. K., Hale A., and Romslo N., “Successful Field Application of An Electro Negative ‘Coating’ to Reduce Bit Balling Tendencies in Water Base Mud,” IADC/SPE 35110, IADC/SPE Drilling Conference, New Orleans, LA., March, 1996.
Suykens J. A. K. and Vandewalle J., “Least Squares Support Vector Machine Classifiers,” Neural Processing Letter, 1999, 9, 293-300.
Van Oort E., Friedheim J. E., and Toups B., “Drilling Faster with Water-Base Muds,” AADE, AADE Annual Technical Forum-Improvements in Drilling Fluids Technology, Houston, Texas, 1999.
Van Oort E., “On the Physical and Chemical Stability of Shales,” Journal of Petroleum Society Engineers, 2003, 38, 213- 235.
Vapnik V. N., “The Nature of Statistical Learning Theory,” (2nd ed.), New York, Springer-Verlag, 1995, 1-279.
Vapnik V. and Lerner A., “Pattern Recognition Using Generalized Portrait Method,” Automation Remote Control, 1963, 24, 774-780.
Warren T. M. and Sinor L. A., “PDC Bits: What’s Needed to Meet Tomorrow’s Challenge,” University of Tulsa, Centennial Petroleum Engineering Symposium, Tulsa, Oklahoma, SPE 27978, 1994.
WarrenT. M. and Armagost W. K., “Laboratory Drilling Performance of PDC Bits,” SPE 15617, 61st Annual Technical Conference and Exhibition of Society of Petroleum Engineers, New Orleans, LA., 1986.
Waxman M. H. and Smits L. J. M., “Electrical Conductivities in Oil Bearing Shaly Sands,” Journal of Society of Petroleum Engineers, 1968, 8, 107-122, .
Xavier S. S., Suykens J. A. K., Vandewalle J., and Bolle D., “Coupled Simulated Annealing,” IEEE Transaction on Systems Management Cybernetics B Cybernetics, 2010, 40, 320-335.
Yang B. S., Hwang W. W., Kim D. J., and Tan A. C., “Condition Classification of Small Reciprocating Compressor for Refrigerators Using Artificial Neural Networks and Support Vector Machines,” Mechanical Systems and Signal Processing, 2005, 19, 371-390.
Yao X. J., Panaye A., Doucet J. P., Chen H. F., and et al., “Comparative Classification Study of Toxicity Mechanisms Using Support Vector Machines and Radial Basis Function Neural Networks,” Anal. Chim. Acta. Computer Tech. Optimization., 2005, 535, 259-273.
Zijsling D. H. and Illerhaus R., “Eggbeater PDC Drill-Bit Design Concept Eliminates Balling in Water-Base,” Society of Petroleum Engineers, 1991, 11-14.
Zuo R. and Carranza E. J. M., “Support Vector Machine: A Tool for Mapping Mineral Prospectivity,” Computation and Geoscience, 2011, 37, 1967-1975.

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