Fast Gaussian picking Method for NMR T2 characteristic distributions of viscous crude oil containing impurities in free fluid state

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Authors: Xinyi Zhang, Zhen Qin, Xue Zhang, Shaocheng Luo, Ke Huang, Xu Dong, Yicong Huang, Kangjian Wei
Cited by: 1
References: 22

Abstract

The nuclear magnetic resonance (NMR) T ₂ distributions of free fluid are widely used to characterize the bulk relaxation properties of fluids. However, in the heavy crude oil of reservoirs in the western South China Sea, solid-phase impurities such as waxes, asphaltenes, and colloids result in multi-peak, broad-spectrum T ₂ distributions. This complexity hinders accurate peak identification and fluid property analysis. To address this, typical crude oils from the study area were selected, and their free-fluid T ₂ distributions were measured under reservoir-temperature conditions. The observed multi-peak patterns were analyzed to determine their physical and chemical origins. Based on the Gaussian distribution function, a fast Gaussian picking method is proposed to search for representative distribution peaks in crude oil in conjunction with extreme points. The non-negative least squares (NNLS) method is utilized to fit the data, and the complex T ₂ distribution was decomposed into components representing different phases and viscosities. After mathematical-physical tests and demonstrations, the rapid classification of the T 2 distribution of the free-fluid state of impurity-containing viscous crude oil is realized. Applied to downhole NMR data, the method accurately captured the T ₂ characteristics of such crude oils, offering a fast and reliable tool for analyzing complex bulk relaxation behaviors and fluid identification. Finally, the method’s performance, influencing factors, and applicability were discussed.

Fields of Study & Subjects

NMR spectroscopy and applications Petroleum Processing and Analysis Enhanced Oil Recovery Techniques

Publication details

Journal / source
Scientific Reports
Publisher
Springer Science and Business Media LLC
Volume
16
Issue
1
Pages
4654–4654
Publication date
8 January 2026
Language
EN