Abstract
Accurately estimating the fluid distribution in the reservoir is a key challenge in the oil industry. Well-log measurements are used to quantitatively characterize subsurface properties such as porosity and fluid saturation. It was proven that it is possible to reduce the ambiguity of the interpretation of well-log measurements by examining a common set of petrophysical parameters that relate geophysical attributes to rock properties. Simultaneously conciliating acoustic velocity, density and electrical conductivity jointly in the interpretation of well logs and deep interwell measurements can significantly reduce the uncertainty of the interpretation and improve the prediction of the reservoir properties, such as saturation.
This study examines a Bayesian joint inversion approach linking rock properties with well-log measurements through constitutive equations. The procedure is applied on actual formation evaluation measurements from a carbonate reservoir in Saudi Arabia.
