Probabilistic Approach Using Rock Physics Characteristics and Petrophysical Modelling to Predict Carbonate Reservoir Properties: A Case Study From the Beda Field, Amin High, Sirt Basin, Libya

ABSTRACT The Beda carbonates reservoir is extensively developed, featuring multiscale reservoir‐forming mechanisms such as diagenesis, complex pore space structures and fault‐related fractures that result in strong heterogeneity. The rock physics templates (RPT) were built as a reliable tool for lithology and pore fluid interpretation for the carbonate reservoir by linking the elastic properties (velocity, impedance and density) with the petrophysical characteristics such as porosity, permeability and fluid saturation obtained directly from well log data from 64 wells, 11 of which include core data of Beda Field in the western Sirt Basin. Five main E‐W bounding faults are identified and mapped based on well correlation, forming a complex fault‐block reservoir that also controls the distribution of reservoir lithofacies and petrophysical parameters as defined by 3D facies and petrophysical models. A lithological analysis of core data has allowed the identification of seven lithofacies units, which are divided into three rock units within the Beda reservoir and are composed mainly of alternating limestone and dolostone. Based on the characteristics of log responses, the reservoir is divided into nine electrofacies units reflecting differences in petrophysical and physical characteristics. Crossplots of mu‐rho ( μ ρ ) versus lambda‐rho ( λ ρ ) show that a clean carbonate zone with high porosity and highly cemented carbonate with low porosity can easily be distinguished, indicating that the mu‐rho ( μ ρ ) attribute is sensitive to rigidity and therefore can be used as an indicator for lithology changes. Moreover, the lambda‐rho ( λ ρ ) has provided a highly effective tool for discriminating fluid saturation, particularly in differentiating hydrocarbon‐filled zones from water‐filled zones in the reservoir, where its values decrease significantly once the pore space is filled with gas. The v p /v s versus AI p crossplots show that the clean carbonate and shale line can easily be detected. A shale‐rich zone is characterised by increased v p /v s and decreased AI p . Additionally, crossplots with porosity as a colour‐scale successfully divided the reservoir into three porosity zones: excellent, fair to very good and poor. The crossplot proved to be a hydrocarbon indicator in which the brine‐saturated zone has higher v p /v s values than the oil‐saturated zone. However, the separation of gas‐saturated and water‐saturated zones is more difficult in the case of carbonate due to its more complex pore structure.