Nuclear Magnetic Resonance T₂ Distribution-Based Gas–Water Relative Permeability Prediction in Tight Sandstone Reservoirs: A Case Study on Central Sichuan Basin, China

The relative permeability (K_r) measurement of tight sandstones is challenging due to its low porosity, low permeability, and complex pore structure. Nuclear magnetic resonance (NMR) technology has the advantages of being fast, nondestructive, and noninvasive while also continuously evaluating tight reservoirs. Based on NMR technology, it is of great significance to establish an effective and reliable relative permeability prediction model to solve practical problems in oil fields. In this paper, a method for predicting gas–water relative permeability in tight sandstone reservoirs is proposed based on NMR transverse relaxation time (T₂) distribution. The gas–water relative permeability measurements are performed for tight sandstone reservoirs in the Central Sichuan Basin, China. On the basis of the analysis of the gas–water permeability features in the study area, the reservoir characteristics are clarified. Based on the NMR theories, two T₂–K_r prediction models (model 1 and optimal model 2) are derived and established, and the model performance is analyzed using experimental data and existing models (Purcell and Brooks-Corey). Finally, the optimal model is used to process NMR logging data, obtain continuous relative permeability curves, and perform a productivity prediction. The effectiveness and applicability of the method are verified using relative permeability experiments and the oil testing data. The proposed method can provide effective guidance for the prediction of relative permeability curves and productivity evaluation of tight sandstone reservoirs.