Prediction of Electrofacies Based on Flow Units Using NMR Data and SVM Method: a Case Study in Cheshmeh Khush Field, Southern Iran

Document Type: Research Paper

Authors

1 Department of Petrophysics, Pars Petro Zagros Engineering & Services Company, Tehran, Iran

2 Department of Petroleum Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

3 Faculty of Petrochemical and Petroleum Engineering, Hakim Sabzevari University, Sabzevar, Iran

Abstract

The classification of well-log responses into separate flow units for generating local permeability models is often used to predict the spatial distribution of permeability in heterogeneous reservoirs. The present research can be divided into two parts; first, the nuclear magnetic resonance (NMR) log parameters are employed for developing a relationship between relaxation time and reservoir porosity as well as introducing the concept of relaxation group. This concept is then used for the definition of electrofacies in the studied reservoir. A graph-based clustering method, known as multi resolution graph-based clustering (MRGC), was employed to classify and obtain the optimum number of electrofacies. The results show that the samples with similar NMR relaxation characteristics were classified as similar groups. In the second part of the study, the capabilities of nonlinear support vector machine as an intelligent model is employed to predict the electrofacies and permeability distribution in the entire interval of the reservoir, where the NMR log parameters are unavailable. SVM prediction results were compared with laboratory core measurements, and permeability was calculated from stoneley wave analysis to verify the performance of the model. The predicted results are in good agreement with the measured parameters, which proves that SVM is a reliable tool for the identification of electrofacies through the conventional well log data.

Keywords


References

Tiab D. and Donaldson E. C., “Petrophysics Theory and Practice of Measuring Reservoir Rock and Fluid Transport Properties,” Elsevier Press, Oxford, 2004, 112-132.

Busch J., Fortney W., and Berry L., “Determination of Lithology from Well Logs by Statistical Analysis,” SPE Formation Evaluation, 1987, 2(04), 412-418.

Delfiner P., Peyret O., and Serra O., “Automatic Determination of Lithology from Well Logs,” SPE Formation Evaluation, 1987, 2(03), 303-310.

El-Sheikh T. S. and Syiam M., “An Efficient Technique for Lithology Classification,” Geoscience and Remote Sensing, 1989, 27(5), 629-632.

Lim J. S., Kang J. M., and Kim J., “Multivariate Statistical Analysis for Automatic Electrofacies Determination from Well log Measurements,” in Asia Pacific Oil & Gas Conference & Exhibition, 1997.

Huang T. M., Kecman V., and Kopriva I., “Kernel Based Algorithms for Mining Huge Data Sets,” Springer, 2006, 17, 125-173.

Chikhi S. and Shout H., “Using Probabilistic Neural Networks to Construct Well Facies,” WSEAS Trans. Syst., 2003, 2(4), 839-843.

Chikhi S., Batouche M., and Shout H., “Hybrid neural Network Methods for Lithology Identification in the Algerian Sahara,” International Journal of Computational Intelligence, 2005, 1(1), 25-33.

Carrasquilla A., Silvab J., and Flexa R., “Associating Fuzzy Logic, Neural Networks and Multivariable Statistic Methodologies in the Automatic Identification of Oil Reservoir Lithologies through Well Logs,” Revista de Geologia, 2008, 21(1), 27-34.

Li Z., Weida Z., and Licheng J., “Radar Target Recognition Based on Support Vector Machine,” in Signal Processing Proceedings, WCCC-ICSP, 5th International Conference on. 2000. IEEE, 2000.

Choisy C. and Belaid A., “Handwriting Recognition Using Local Methods for Normalization and Global Methods for Recognition in Document Analysis and Recognition,” Proceedings. 6th International Conference on. 2000. IEEE, 2001.

Gao J., Harris C. J., and Gunn S. R., “On a Class of Support Vector Kernels Based on Frames in Function Hilbert Spaces,” Neural Computation, 2001, 13(9), 1975-1994.

Shin C., Kim K. I., Park M. H., and Kim H. J., “Support Vector Machine-based Text Detection in Digital Video,” in Neural Networks for Signal Processing X, Proceedings of the 2000 IEEE Signal Processing Society Workshop, 2000.

Ma C., Randolph M. A., and Drish J., “A Support Vector Machines-based Rejection Technique for Speech Recognition,” in Acoustics, Speech, and Signal Processing, Proceedings.(ICASSP›01). 2001 IEEE International Conference on. 2001. IEEE, 2001.

Van Gestel T., Suykens J. A. K., Baestaens D. E., Lambrechts A., et al., “Financial Time Series Prediction using Least Squares Support Vector Machines within the Evidence Framework,” Neural Networks, IEEE Transactions on, 2001, 12(4), 809-821.

Al-Anazi A. and Gates I., “On the Capability of Support Vector Machines to Classify Lithology from Well Logs,” Natural Resources Research, 2010, 19(2), 125-139.

Al-anazi A. F. and Gates I. D., “Support-vector regression for permeability prediction in a heterogeneous reservoir: A comparative study,” SPE Reservoir Evaluation & Engineering, 2010, 6(10), 21-18.

Al-Anazi A. and Gates I., “Support Vector Regression for Porosity Prediction in a Heterogeneous Reservoir: A Comparative Study,” Computers & Geosciences, 2010, 36(12), 1494-1503.

Vapnik V., “The Nature of Statistical Learning Theory,” Springer Science & Business Media, 2000.

Wang L., “Support Vector Machines: Theory and Applications,” Springer Science & Business Media, 2005:

Al-Anazi A., Gates I., and Azaiez J., “Fuzzy Logic Data-driven Permeability Prediction for Heterogeneous Reservoirs,” Paper SPE 121159 Presented at the EUROPEC/EAGE Conference and Exhibition, 2009.

Lu J., Plataniotis K., and Ventesanopoulos A., Face Recognition Using Feature Optimization and V-support Vector Machine,” IEEE Neural Netw. Signal Process, 2001, 373–382.

Wong K.W.,Fung C. C., Ong Y. S., and Gedeon T. D., “Reservoir Characterization Using Support Vector Machines,” in Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on. 2005. IEEE., 2005.

Al-Anazi A. and Gates I., “Support Vector Regression to Predict Porosity and Permeability: Effect of Sample Size,” Computers & Geosciences, 2012, 39, 64-76.

Suykens J. A., “Data Visualization and Dimensionality Reduction Using Kernel Maps with a Reference Point,” Neural Networks, IEEE Transactions, 2008, 19(9), 1501-1517.

Georgi D. and Menger S., “Reservoir Quality, Porosity and Permeability Relationships,” in Proc. 14th Mintrop Seminar, Münster. 1994.

Ohen H. A. and Ajufo A., “A Hydraulic (Flow) UMR Based Model for the Determination of Petrophysical Properties from nmr Relaxation Measurements,” SPE Annual Technical Conference and Exhibition, Dallas, Texas, 1995.

Parra J., Hackert C. L., Collier H. A., and Bennett M., “NMR and Acoustic Signatures in Vuggy Carbonate Aquifers,” in SPWLA 42nd Annual Logging Symposium, Society of Petrophysicists and Well-Log Analysts, 2001.

Ye S. J. and Rabiller P., “A New Tool for Electro-Facies Analysis: Multi-resolution Graph-based Clustering,” in SPWLA 41st annual logging symposium, Society of Petrophysicists and Well-Log Analysts, 2000.