Multi resolution graph based clustering analysis for lithofacies identification from well log data: Case study of intraplatform bank gas fields, Amu Darya Basin
In this study, we used the multi resolution graph based clustering (MRGC) method for determining the electrofacies (EF) and lithofacies (LF) from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low energy bank, and high energy bank, and the coincidence rate in the cored interval could reach 85% We concluded that the MRGC could be accurately applied to predict the LF in non cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were interpreted, and the distribution and petrophysical characteristics of different LF were analyzed in the framework of sequence stratigraphy.
Applied Geophysics