Application of Bayesian Statistics in Hydraulic Flow Units Modeling and Permeability Prediction: A Case Study of a Carbonate Reservoir in Southwest of Iran

The determination of rock types for petrophysical studies has a wide range of applications. It is widely used in drilling and production, as well as especially in the study and characterization of reservoirs. Zoning of flow units and permeability estimation are one of the challenging tasks of reservoir studies, which uses the integration of data from the well logs and the analysis of the core. In this study, a Bayesian theory-based statistical model is proposed to identify hydraulic flow units in coreless wells using the concept of hydraulic flow unit and to estimate permeability. In the flow zone indicator (FZI) method, the formation is divided into five hydraulic flow units. In the Winland R35 method, however, it is divided into four hydraulic flow units. The Bayesian statistical model divides the existing complex carbonate reservoir rock data into three hydraulic flow units with the most probability of similarity. The properties of the second and third hydraulic flow units are more similar compared to those the first hydraulic unit. The permeability estimation modeling based on the Bayesian method has acceptable precision, and the validation of its results with the core data indicates a precision factor of 0.96. The findings of this study can help better understand the concept of flow units and more effectively estimate the permeability of the rocks of the heterogeneous carbonate reservoirs.