a new model for permeability estimation in carbonate reservoirs by using nmr t2 distribution and lsboost ensemble technique

Permeability is arguably the most critical property for evaluating flow in the reservoir. it is also one of the challenging parameters which must be measured in the field. nuclear magnetic resonance (nmr) logging across the borehole is among the popular techniques, which it is utilized to determine permeability across the reservoir. however, available correlations in literature for estimating permeability from nmr data do not usually provide acceptable accuracy in the carbonate rocks. therefore, a new model is proposed to estimate permeability by establishing a relationship between core derived permeability and extracted features from the t2 distribution curve of nmr data with the ensemble lsboost algorithm. the feature extraction process is performed using peak analysis on t2 distribution curves which it leads to 5 relevant parameters, including t2lm, tcmr, prominence, peak amplitude and width. the proposed model is validated by comparing the proposed method’s correlation coefficient against timurcoates and sdr equation estimation accuracy. the results show that our model generally provides better prediction accuracies in comparison with the empirical equationbased derived permeabilities.