A Rock-Constrained Multilevel GMM Approach for Wellsite NMR Multifluid Quantitative Evaluation in Shale

Identification of fluid components and reliable saturation evaluation remain critical challenges in shale exploration and development. Conventional core experimental methods are often limited by high costs and long cycle times, while laboratory nuclear magnetic resonance (NMR) techniques fail to perform rapid and continuous measurements.  In this study, we propose a rock-constrained multilevel Gaussian mixture model (GMM) approach for the quantitative evaluation of multiple fluids in shale using wellsite NMR data. The proposed workflow begins with normalization and thresholding of the T₁–T₂ spectra. A rock-physics constraint is then incorporated to partition the relaxation domain and distinguish movable fluids from bound fluids. GMM clustering is first applied independently within each relaxation region to extract representative fluid signatures. These characteristic signatures are subsequently integrated into a second-stage GMM analysis, enabling robust identification and quantitative evaluation of individual fluid components. Application to representative shale NMR datasets, coupled with multi-state core experiments, demonstrates that the proposed method effectively identifies multiple fluid components, including heavy components, bound oil, and movable oil. Furthermore, the saturation of various hydrocarbon components can be quantitatively predicted, demonstrating strong agreement with Rock-Eval measurements. The proposed approach provides a practical and reliable solution for rapid downhole fluid identification and saturation evaluation in shale reservoirs.