@article{Zhu2026, 
author = {Xue-Juan Zhu and Shao-Gui Deng and Yi-Ren Fan and Xin-Min Ge and Zhou-Tuo Wei},
title = {A new method for fluid saturation calculation in low resistivity oil reservoirs with nuclear magnetic resonance-constrained triple-water resistivity model},
year = {2026},
journal = {Petroleum Science},
volume = {23},
number = {4},
pages = {1817-1828},
keywords = {Low resistivity oil reservoirs, Triple-water resistivity model, Nuclear magnetic resonance (NMR) logging, Fluid saturation calculation},
url = {https://www.sciopen.com/article/10.1016/j.petsci.2025.12.026},
doi = {10.1016/j.petsci.2025.12.026},
abstract = {To address the evaluation difficulty of hydrocarbon saturation in low resistivity reservoirs, an innovative method for calculating oil saturation is proposed using a nuclear magnetic resonance (NMR)-constrained triple-water resistivity model. This model explicitly distinguishes three conductive water phases: movable water, capillary-bound water, and clay-bound water. Resistivity response equations are established for both water-saturated and hydrocarbon-bearing rocks. A cooperative inversion framework is proposed based on NMR and conventional logging data, incorporating high-precision NMR-derived parameters as constraints during conventional logging inversion. The pore component volumes are obtained with the NMR T2 spectrum decomposition and served as a priori information for the nonlinear optimization of porosity exponents. This enables the construction of a pore component inversion algorithm using conventional logging data, thereby extending water saturation calculation applicability in complex reservoirs. The method incorporates data-driven optimization to effectively reduce the reliance on core-based calibration data (mercury injection, petrophysical experiments, cation exchange capacity (CEC) tests). Application in a Bohai Bay Basin low resistivity reservoir demonstrates superior saturation calculation accuracy compared to traditional models. The integration of multi-physics logging inversion with nonlinear optimization effectively enhances conductivity mechanism characterization in reservoirs with complex pore systems, providing a robust technical solution for quantitative evaluation of low resistivity oil reservoirs.}
}