Application of Multifractal Analysis Theory to Interpret T2 Cutoffs of NMR Logging Data: A Case Study of Coarse Clastic Rock Reservoirs in Southwestern Bozhong Sag, China - Zefan Wang; Yanbin Yao; Ruying Ma; Xiaona Zhang; Guibin Zhang (Fractal and Fractional, 2023)
The Paleocene Kongdian Formation coarse clastic rock reservoir in Bozhong Sag is rich in oil and gas resources and has huge exploration potential. However, the coarse clastic rock reservoir has the characteristics of a complex pore structure and strong heterogeneity, which restrict the accuracy of evaluating the reservoir’s physical properties, such as porosity and permeability, for field evaluation. Nuclear magnetic resonance (NMR) technology has become a popular methods for unconventional reservoir evaluation because it can obtain abundant reservoir physical property information and because of its ability to identify fluid characteristics information. The transverse relaxation time (T2) cutoff (T2C) value is an important input parameter in the application of NMR technology. The accuracy of the T2C value affects the accuracy of the reservoir evaluation. The standard method for determining the T2C value requires a series of complicated centrifugation experiments in addition to the NMR experiments, and its application scope is limited by obtaining enough core samples. In this study, 14 core samples from the coarse clastic rock reservoir in the southwestern Bozhong sag of the Bohai Bay Basin were selected, and NMR measurements were carried out under the conditions of fully saturated water and irreducible water to determine the T2C value. Based on the multifractal theory, the NMR T2 spectrum of the saturated sample was analyzed, and the results show that the NMR T2 distribution of the saturated sample has multifractal characteristics, and the multifractal parameter Dq and the singular intensity range Δα have a strong correlation with the T2C value. Thus, based on multiple regression analyses of the multifractal parameters with the experimental T2C value of 10 core samples, we propose a method to predict the T2C value. After applying this method to 4 samples that were not used in the modeling, we confirmed that this method can be used to predict the T2C value of core samples. Furthermore, we expanded this method to the field application of a production well in Bozhong sag by adding an empirical index in the model. The new model can be used to directly calculate the T2C value of NMR logging data, and it does not require any other extra data, such as those from core analysis. This method is applicable in fast reservoir evaluations by only using NMR logging data in the field. The research results improve the accuracy of field NMR logging reservoir evaluations.