A hybrid approach for litho-facies characterization in the framework of sequence stratigraphy: a case study from the South Pars gas field, the Persian Gulf basin

Abstract

The Upper Dalan and Kangan formations with dominant lithology of limestone and dolomite associated with anhydrite nodules and interbeds form the Permo-Triassic succession of South Pars gas field (SPGF) and host the largest none-associated gas reservoir in the world. The current study focuses on preparing a comprehensive litho-facies model in the framework of sequence stratigraphy. For this purpose, Self-Organizing Map Neural Network (SOM-ANN) and hierarchical cluster analysis (HCA) were utilized as effective tools to prepare the preliminary data for litho-facies mapping. Neural networks (self-organizing maps) and hierarchical clustering approaches were applied to characterize litho-facies in un-cored but logged wells. Particularly, the powerful visualization tools of the SOM-ANN which provide more information in comparison to HCA facilitate the task of establishing an order of priority between the distinguished electro-facies groups. The mentioned method of SOM-ANN clustering algorithm showed a good performance in petrophysical data clustering and litho-facies determination. Based on the porosity and permeability maps at different depth levels, the target reservoir is ranked and classified into four litho-facies and six electro-facies. They include litho-facies 3 with good reservoir quality (equivalent of electro-facies 4–6), litho-facies 4 with moderate reservoir quality (equivalent of electro-facies 2) and litho-facies 1 and 2 with poor to bad reservoir quality (equivalent of electro-facies 1 and 3).The main litho-facies assemblages are indicative of deposition within tidal flat, lagoon, shoal and off-shoal environments. The most shoal litho-facies with best reservoir quality occurs in the high energy sub-environment within upper transgression position (HST) of the 3rd-order cycle in K4 and K2 reservoir units. Distribution of the petrophysical characteristics was analyzed in detail in the framework of electro-facies and sequence stratigraphy. The methodology is illustrated by using a case study from SPGF, Iran.