Abstract
The need for integration of different data in the understanding and characterization of reservoirs is continuously growing in petroleum geology. The large amount of data for each well and the presence of different wells to be simultaneously analyzed make this task both complex and time consuming. In this scenario, the development of reliable interpretation methods is of prime importance in order to help the geologist and reduce the subjectivity of data interpretation. In this paper, we propose a novel interpretation method based on the integration of unsupervised and supervised learning techniques. This method uses an unsupervised learning algorithm to objectively and quickly evaluate a large dataset made of subsurface data from different wells in the same field. Then it uses a supervised learning algorithm to predict and propagate the characterization over new wells. To test our approach, we use first hierarchical clustering to then feed several supervised learning algorithms in the classification domain (e.g. decision trees and linear regression).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Quinlan, J.R.: Induction on Decision Trees. Machine Learning 1, 81–106 (1986)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)
Breiman, L.: Random Forests. Mach. Learn. 45(1), 5–32 (2001)
Frank, E., Wang, Y., Inglis, S., Holmes, G., Witten, I.H.: Using Model Trees for Classification. Machine Learning 32(1), 63–76 (1998)
Frank, E., Witten, I.H.: Generating Accurate Rule Sets Without Global Optimization. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 144–151 (1998)
Le Cessie, S., Van Houwelingen, J.C.: Ridge Estimators in Logistic Regression. Applied Statistics 41(1) (1992)
Ferraretti, D., Gamberoni, G., Lamma, E.: Automatic Cluster Selection Using Index Driven Search Strategy. In: Serra, R., Cucchiara, R. (eds.) AI*IA 2009. LNCS, vol. 5883, pp. 172–181. Springer, Heidelberg (2009)
Ferraretti, D., Gamberoni, G., Lamma, E., Di Cuia, R., Turolla, C.: An AI Tool for the Petroleum Industry Based on Image Analysis and Hierarchical Clustering. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 276–283. Springer, Heidelberg (2009)
Shin-Ju, Y., Rabiller, P.: A new tool for electro-facies analysis: multi-resolution graph-based clustering. In: SPWLA 41st Annual Logging Symposium (2000)
Basu, T., Dennis, R., Al-Khobar, B.D., Al Awadi, W., Isby, S.J., Vervest, E., Mukherjee, R.: Automated Facies Estimation from Integration of Core, Petrophysical Logs, and Borehole Images. In: AAPG Annual Meeting (2002)
Blviken, E., Storvik, G., Nilsen, D.E., Siring, E., Van Der Wel, D.: Automated prediction of sedimentary facies from wireline logs. In: Geological Society, vol. 65, pp. 123–139. Special Publications, London (1992)
Knecht, L., Mathis, B., Leduc, J., Vandenabeele, T., Di Cuia, R.: Electrofacies and permeability modeling in carbonate reservoirs using image texture analysis and clustering tools. In: SPWLA 44th Annual Logging Symposium, vol. 45(1), pp. 27–37 (2004)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Academic Press, London (2006)
Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation Forest: A New Classifier Ensemble Method. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1619–1630 (2006)
Kotsiantis, S.B.: Supervised Machine Learning: A Review of Classification Techniques. Informatica 31, 249–268 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Ferraretti, D., Lamma, E., Gamberoni, G., Febo, M., Di Cuia, R. (2011). Integrating Clustering and Classification Techniques: A Case Study for Reservoir Facies Prediction. In: Ryżko, D., Rybiński, H., Gawrysiak, P., Kryszkiewicz, M. (eds) Emerging Intelligent Technologies in Industry. Studies in Computational Intelligence, vol 369. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22732-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-22732-5_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22731-8
Online ISBN: 978-3-642-22732-5
eBook Packages: EngineeringEngineering (R0)