Application of pattern recognition techniques to hydrogeological modeling of mature oilfields

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. © 2011 Springer-Verlag Berlin Heidelberg.