Abstract

Abstract The anisotropy and upscaling factors adjustment are very important in the proper integration of the core ultrasonic and log sonic data for hydrocarbon reservoir characterization. Related to this issue, this paper evaluates the effects of the lithology and facies types on the anisotropy parameters and upscaling factor. The data are taken from the deep-water oil-gas Sadewa field located in the Kutei Basin, East Kalimantan, Indonesia at water depths of 500–750 m. The main reservoirs in this field are the Upper Miocene sand reservoirs deposited in the upper slope fan and channel facies. Fifty core plugs sampled at depths around 3000–4000 m were collected for thin-section petrography analysis and ultrasonic measurements. The thin-section petrography analysis shows that both facies areas are dominated by greywackes with parallel lamination structure and/or intergranular porosity. The 1 MHz ultrasonic velocities were measured in the 50 core plugs. Meanwhile, 10–40 KHz dipole sonic log data sampled at the same plug’s depth positions were used to calculate the elastic (Vp, Vs, Poisson ratio, etc) and Thomsen’s e, γ and δ anisotropy parameters. The results show that for the parallel lamination and intergranular porosity greywacke samples, e parameter is the best for compensating the anisotropy effect in the integrated core and log data reservoir quality determination (sand shale ratio, percentage of quartz contents, the effective and the total porosities). However, when the samples are non-intergranular porosity greywacke, the anisotropy effect in the core-log data correlation, is too irregular to be compensated by any elastic and anisotropy parameter. The core-log data upscaling factor in the channel facies is much smaller than the upper slope fan facies, and it is in line with the gamma-ray log data which indicates that the sandy channel facies is more homogeneous than the intercalated shale-sand upper slope fan facies. The overall results suggest that the lithology and facies types significantly affect the anisotropy parameters and upscaling factor. In addition, the best elastic or anisotropic parameters) should be determined to minimize the effects in the core and log data integration for hydrocarbon reservoir characterization.

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