The classification in metamorphic rocks using modified fuzzy cluster analysis from geophysical log data: evidence from Chinese Continental Scientific Drilling Main Hole

Abstract

Lithology is one of the most important data in evaluating reservoir, and is mainly carried out by cores recovery in laboratory which is very expensive, and its interpretation is time consuming. Accurate identification of lithology is fundamentally crucial to evaluate reservoir from geophysical log data. Pattern recognition and statistical analysis have been proved to be the most powerful methods for constructing optimal model in lithology recognition. To address this issue, a fast and practical K-means clustering algorithm is proposed in order to better deal with lithology recognition from geophysical log data. Based on the traditional K-means clustering algorithm, Euclidean distance is replaced by Mahalanobis distance; the initial cluster centers are acquired from the average of characteristic values but not selected randomly, in addition, adding weight value in each characteristic value of the objective function, and thus a lithology recognition model named modified K-means clustering is established. The method is applied to identify the Chinese Continental Scientific Drilling Main Hole (CCSD-MH) metamorphic rocks. Compared with the traditional K-means clustering, the accurate rate of the modified K-means clustering in lithologic identification has improved for the same 45 samples, raised 11.11 %. According to the modified K-means cluster algorithm, nine kinds of lithology cluster centers are acquired from 45 samples. The classes of the samples can be determined by analyzing the hamming approach degree curves, which is calculated by the undetermined samples and 9 cluster centers. The predicted results and the core recovery are exactly the same by comparison. The hamming approach degree can identify the whole well of CCSD-MH lithology effectively and accurately. This model may be made applications to other areas.