Integrating facies analysis and geostatistical methods in an onshore oil field using petrophysical groups

Abstract

Reservoir facies studies are of great importance in different stages of exploration and development of hydrocarbon fields. We have aimed to generate a reservoir facies model for the Asmari Formation in an onshore oil field located southwest of Iran. Input data for electrofacies (EF) clustering algorithms are used, which include gamma-ray (GR), density (RHOB), porosity, and sonic logs from four wells. We obtain the petrophysical group (PG) and EF class using core data (mercury injection capillary pressure) and well-logs analysis. The integration of PGs and log EF significantly decreases the uncertainty in reservoir modeling, which alternatively enhances field development decisions. We compare the multiresolution graph-based clustering (MRGC) and k-means clustering methods. EF clustering results find nine EF classes. We delineate high-quality reservoirs based on lower GR, RHOB, and high-porosity logs. Next, we use the clustering results in the static reservoir modeling process, using the sequential index simulation and indicator kriging methods. The comparison between the facies obtained models and existing drilling core data finds that the absolute percentage error of the MRGC algorithm is less than that of the k-means algorithm. The results obtained by this study can provide useful information for the development of hydrocarbon exploration plans in the studied oil field.