Predicting oil saturation of tight conglomerate reservoirs via well logs based on reconstructing nuclear magnetic resonance T2 spectrum under completely watered conditions

Abstract

Due to complex lithology, strong heterogeneity, low porosity and permeability; resistivity logging faces great challenges in oil saturation prediction of tight conglomerate reservoirs. First, 10 typical core samples were selected to measure and analyse the porosity, permeability, nuclear magnetic resonance (NMR) T2 spectrum and mercury injection capillary pressure (MICP) curve. Second, an empirical method was proposed for reconstructing the NMR T2 spectrum under completely watered conditions using MICP curve based on the 'three-piece' power function. The parameters of different models were calibrated via experimental data analysis, respectively. The 180 core experimental data from an MICP curve were used as the input database. Porosity and permeability were regarded as the MICP data selection criteria to apply this model in formation evaluation. The comparison results show good application effects. Finally, to reflect oil saturation, the ratio of T2 geometric means of NMR T2 spectra under oil-bearing and completely watered conditions was proposed. Then, the quantitative relation between oil saturation and the proposed ratio was established via experimental data from the sealed cores, which established a quantitative prediction on oil saturation of tight conglomerate reservoirs. This showed a good application effect. The average relative error and the root mean square error (RMSE) of the predicted oil saturation and sealed coring measurement were around 10 and 3%, respectively. As the proposed method is only influenced by the wettability of reservoir and viscosity of oil, it is not only appropriate for the studied area, but also for other water-wet reservoirs containing light oil. It is important for identifying oil layers, calculating oil saturation and improving log interpretation accuracy in tight conglomerate reservoirs.