A new integrated workflow for improving permeability estimation in a highly heterogeneous reservoir of Sawan Gas Field from well logs data

Abstract

The Sawan Gas Field is one of the most promising gas fields in Pakistan with a cumulative production of 850 BCF. The repetition of coarse sandstone, medium sandstone, and sandstone-shale intercalation in the production zone cause extreme heterogeneity. Consequently permeability varies enormously (from 0.01 mD to more than 1000 mD). Nevertheless, verifiable and accurate estimation of permeability in the production zone with no previous laboratory-derived data is considered a challenging task. In this study, we explore a methodology for improving permeability estimation based on the combination of neural network (NN), multiple variable regression, and classification of data mining using conventional well logs (GR, LLD, RHOB, DT, and NPHI). The approach works in two-steps. First, we compute permeability using empirical, statistical, and virtual techniques on a fully cored well in order to select the specialized regression model that will be responsible for building data partitioning and classification of data mining task. To improve the efficiency of the classifier model, we combine the NN with multiple variable regression for predicting accurate permeability values. In step-2, the proposed regression model was employed to determine the final permeability values from data partitioning and classification of data mining. The final result of this study revealed that the proposed approach which combines NN, multiple variable regression, and classification of data mining provide more uniform, accurate, and qualitative estimation of permeability compared with stand-alone generic or global regression model. Also electrofacies (EFs) classification was conducted over the model to validate the proposed approach.