Estimating Vsand and reservoir properties from seismic attributes and acoustic impedance inversion: A case study from the Mansuri oilfield, SW Iran

This study integrates 3D seismic attributes and well log data to determine Vsand and reservoir property distribution of the Oligo-Miocene Asmari Formation in the western Dezfol Embayment, SW Iran. The rocks consist of complex geology with siliciclastic and carbonate lithology. Hence, it creates the need for a precise interwell estimation of lithology and reservoir properties. For this purpose, first the acoustic impedance was obtained by a model-based inversion algorithm. Then, the acoustic impedance attribute and other sample-based seismic attributes were integrated with sand volume and petrophysical data by using multiple attribute regression and neural networks in order to predict Vsand and reservoir property. In the next stage, cross-validation was used to estimate the reliability of the derived multi-attribute transforms. Based on the results of neural networks, the highest cross-correlation was observed between seismic attributes and the observed target logs at seven wells in the study area. After validation, and making a comparison between different available techniques for sand volume, effective porosity and water saturation estimation, multiple regression transform and neural network were used for the first two and latter, respectively. The derived sand volume and reservoir property maps for the Asmari reservoir indicated that high-porous and high-sand volume parts were laterally more continuous in the central and east part of the area under study. In addition, high-porosity zones were more related to high sand volume parts. Based on the result of interpretation and the relationship between core and acoustic impedance, variations in acoustic impedance were related to variations in geological characteristics of Asmari reservoir in the field. Therefore, seismic inversion as a powerful tool can facilitate the detailed studies of sedimentary facies and lithology in the reservoir which contribute to understand the subsurface reservoirs heterogeneities and drilling strategy of future drilling campaigns in the study area.