Evaluation of oil saturation in shale from an NMR T1–T2 map based on density peak clustering and Gaussian mixture model

Fluid evaluation plays a vital role in reserve calculation and perforation location selection in shale oil exploration. Manual classification with fixed cutoffs and the blind source separation (BSS) method are the main approaches used for fluid typing in NMR T1–T2 maps. To overcome the subjectivity of the manual method and the dependence of BSS on a large dataset, an approach that integrates a clustering method, density peak clustering (DPC), and a spectral fitting technique, the Gaussian mixture model (GMM), is introduced in this paper to classify fluid components and evaluate oil saturation from T1–T2 maps. To select the number of fluid types automatically, traditional DPC is improved by defining a metric rnew based on local outlier degree, local density, relative distance, and threshold r*. The clustering accuracy of the r*−DPC method is 83.64%, which is much higher than the 52.73% accuracy of traditional DPC, and the r*–DPC method achieves better performance in fluid partitioning than other commonly used clustering algorithms. With the fluid centers information provided by r*–DPC, the GMM method is implemented to fit and extract the T1–T2 signatures of multiple fluids from T1–T2 maps. The DPC–GMM method was applied to core T1–T2 measurements and T1–T2 logging datasets, and the experimental results reveal that the average relative error of oil saturation is between 17.10% and 21.63%, which is nearly 15% lower than that of the BSS method. Furthermore, the DPC–GMM method requires less time and is easier to implement, which can make it an efficient and practical approach to fluid evaluation.