Abstract
Several pattern recognition techniques are applied for hydrogeological modeling of mature oilfields. Principle component analysis and clustering have become an integral part of microarray data analysis and interpretation. The algorithmic basis of clustering – the application of unsupervised machine-learning techniques to identify the patterns inherent in a data set – is well established. This paper discusses the motivations for and applications of these techniques to integrate water production data with other physicochemical information in order to classify the aquifers of an oilfield. Further, two time series pattern recognition techniques for basic water cut signatures are discussed and integrated within the methodology for water breakthrough mechanism identification.
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Seright, R.S., Lane, R.H., Sydansk, R.D.: A strategy for attacking excess water production, paper SPE 84966 (2003)
Sheremetov, L., Alvarado, M., Bañares-Alcántara, R., Anminzadeh, F.: Intelligent Computing in Petroleum Engineering (Editorial). J. of Petroleum Science and Engineering 47(1-2), 1–3 (2005)
Gaskari, R., Mohaghegh, S.D., Jalali, J.: An Integrated Technique for Production Data Analysis with Application to Mature Fields, SPE 100562 (2006)
Birkle, P., Martínez-García, B., Milland-Padrón, C.M., Eglington, B.M.: Origin and evolution of formation water at the Jujo–Tecominoacán oil reservoir, Gulf of Mexico. Part 2: Isotopic and field-production evidence for fluid connectivity. Applied Geochemistry 24, 555–573 (2009)
Lim, J.S.: Multivariate Statistical Techniques Including PCA and Rule Based Systems for Well Log Correlation. In: Nikravesh, M., Aminzadeh, E., Zadeh, L.A. (eds.) Developments in Petroleum Science, pp. 673–688. Elsevier, Amsterdam (2003)
Yortsos, Y.C., Choi, Y., Yang, Z., Shah, P.C.: Analysis and Interpretation of Water/Oil Ratio in Waterfloods. SPEJ 4, 413–424, paper SPE 59477 (1999)
Bailey, B., Crabtree, M., Tyrie, J., Elphick, J., Kuchuk, F., Romano, C., Roodhart, L.: Water control. Oilfield Review, Schlumberger (2000)
Sheremetov, L., Batyrshin, I., Cosultchi, A., Martínez-Muñoz, J.: SMART-Agua: a Hybrid Intelligent System for Diagnostics. In: INES 2006, London, pp. 238–243 (2006)
Batyrshin, I., Sheremetov, L.: Time Series Pattern Recognition Based on MAP Transform and Local Trend Associations. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds.) CIARP 2006. LNCS, vol. 4225, pp. 910–919. Springer, Heidelberg (2006)
Cook, L.M.: Oscillation in the simple logistic growth model. Nature 207, 316 (1965)
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Sheremetov, L., Cosultchi, A., Batyrshin, I., Velasco-Hernandez, J. (2011). Application of Pattern Recognition Techniques to Hydrogeological Modeling of Mature Oilfields. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Ben-Youssef Brants, C., Hancock, E.R. (eds) Pattern Recognition. MCPR 2011. Lecture Notes in Computer Science, vol 6718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21587-2_10
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DOI: https://doi.org/10.1007/978-3-642-21587-2_10
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