Abstract
The integration of different data in reservoir understanding and characterization is of prime importance in petroleum geology. The large amount of data for each well and the presence of new unknown wells to be analyzed make this task complex and time consuming. Therefore it is important to develop reliable prediction methods in order to help the geologist reducing the subjectivity and time used in data interpretation. In this paper, we propose a novel prediction method based on the integration of unsupervised and supervised learning techniques. This method uses an unsupervised learning algorithm to evaluate in an objective and fast way 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 unknown wells. Finally predictions are evaluated using homogeneity indexes with a sort of reference classification, created by an unsupervised algorithm.
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
Bolviken, E., Storvik, G., Nilsen, D.E., Siring, E., Van Der Wel, D.: Automated prediction of sedimentary facies from wireline logs. Geological Society, London, Special Publications 65, 123–139 (1992)
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)
Shin-Ju, Y., Rabiller, P.: A new tool for electro-facies analysis: multi-resolution graph-based clustering. In: SPWLA 41st Annual Logging Symposium (2000)
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)
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)
Ferraretti, D., Lamma, E., Gamberoni, G., Febo, M., Di Cuia, R.: Integrating clustering and classification techniques: a case study for reservoir facies prediction. Accepted to IS@ISMIS2011: Industrial Session - Emerging Intelligent Technologies in the Industry. IS@ISMIS2011 will be held on June 2011, in Warsaw, Poland (2011)
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)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn. Academic Press, London (2006)
Quinlan, J.R.: Induction on Decision Trees. Machine Learning 1, 81–106 (1986)
Breiman, L.: Random Forests. Mach. Learn. 45(1), 5–32 (2001)
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)
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)
Frank, E., Wang, Y., Inglis, S., Holmes, G., Witten, I.H.: Using Model Trees for Classification. Machine Learning 32(1), 63–76 (1998)
Le Cessie, S., Van Houwelingen, J.C.: Ridge Estimators in Logistic Regression. Applied Statistics 41(1) (1992)
Kotsiantis, S.B.: Supervised Machine Learning: A Review of Classification Techniques. Informatica 31, 249–268 (2007)
Shannon, C.E.: A Mathematical Theory of Communication. CSLI Publications, Stanford (1948)
Zhao, Y., Karypis, G.: Criterion functions for document clustering: experiments and analysis, Technical Report. Department of Computer Science, University of Minnesota (2001)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferraretti, D., Lamma, E., Gamberoni, G., Febo, M. (2011). Clustering and Classification Techniques for Blind Predictions of Reservoir Facies. In: Pirrone, R., Sorbello, F. (eds) AI*IA 2011: Artificial Intelligence Around Man and Beyond. AI*IA 2011. Lecture Notes in Computer Science(), vol 6934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23954-0_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-23954-0_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23953-3
Online ISBN: 978-3-642-23954-0
eBook Packages: Computer ScienceComputer Science (R0)