Lithofacies discrimination using seismic anisotropic attributes from logging data in Muglad Basin, South Sudan

Abstract

The identification of formations in anisotropic reservoirs using seismic reflection data and logging data may lead to misrepresentations of the actual formations. Lithofacies discrimination intrinsically has ambiguity, and the depositional sequences of the study area comprise shales, fine-grained sands, and silts. So it needs to reduce the uncertainty of the lithofacies discrimination using anisotropic parameters. This study proposes an approach involving seismic anisotropic parameters to discriminate between different lithofacies. We calculate four anisotropic parameters (ε, δ, γ, η) from logging data (V p, V s, and density) and then employ these for lithofacies discrimination. We compared our results to lithofacies discrimination based on traditional parameters such as V p/V s ratio, clay volume, and water saturation. Using field data from Muglad Basin in South Sudan, we show how the suggested parameters could be used to identify eleven zones with distinct lithofacies. According to the anisotropic parameters, the lithofacies discrimination results are similar to other logging data, and it is easier to separate the lithofacies than petrophysical data. Furthermore, we introduce a new parameter, i.e., the difference between the normalized anisotropic δ parameter and clay volume, which can be used as a possible indicator for heavy oil reservoirs. The new parameter matches well with water saturation in the field data application.