Rock facies classification and its effect on the estimation of original oil in place based on petrophysical properties data

The most significant function in oil exploration is determining the reservoir facies, which are based mostly on the primary features of rocks. Porosity, water saturation, and shale volume as well as sonic log and Bulk density are the types of input data utilized in Interactive Petrophysics software to compute rock facies. These data are used to create ۱۵ clusters and four groups of rock facies. Furthermore, the accurate matching between core and well-log data is established by the neural network technique. In the current study, to evaluate the applicability of the cluster analysis approach, the result of rock facies from ۲۹ wells derived from cluster analysis were utilized to redistribute the petrophysical properties for six units of Mishrif Formation; MA, MB۱۱, MB۱۲, MB۲۱, MC۱, and MC۲. The precise facies modelling is constructed by using Petrel software while applying different appropriate scale-up methods. Consequently, the petrophysical properties such as porosity, water saturation and permeability are distributed within each unit depending on facies modelling. The Net to a gross parameter which has a significant impact on determining original oil in place (OIIP) also calculated and distributed using facies modelling. The facies modelling is performed to obtain an accurate estimation of OIIP. Finally, the results of the facies investigation have a significant effect on petrophysical properties and therefore affect the estimation of OIIP by ۲\% for the whole Mishrif Formation.

Keywords:

Rock Facies , Cluster analysis , Mishrif Formation , Buzurgan oil field , Petrophysical properties , Scale-up methods , Porosity , Water saturation , Permeability , Net to gross , Original oil in place