Permeability Estimation Using Clustering, Artificial Neural Network and Fuzzy Logic Method in Bangestan Carbonate Reservoir

Subject Areas :

Received: 2024-06-01 Accepted : 2024-06-09 Published : 2024-11-30

Keywords: Permeability estimation, Clustering, Fuzzy logic, Arrtificial nural network, Bangestan reservoir,

Abstract :

Permeability is one of the most important parameters of reservoir rock. The calculation of this parameter is obtained from the analysis of drilling cores, but since not all wells are coreed and the core data is not available continuously along the reservoir. The estimation of this parameter is done through petrophysical logs. There are different methods for estimating the permeability of logs, and a number of these methods were used in this study. The studied interval is Bangestan reservoir for which permeability in 4 wells was estimated. In all studied wells (A, B, C and D) petrophysical log data were available that in addition to log data in well A, there was also core permeability data. As a result, using MRGC, AHC, DC and SOM clustering methods, as well as artificial neural network and fuzzy logic, permeability was estimated in different models and calibrated with core permeability data. The coefficient of correlation coefficient between the estimated permeability in all models was evaluated in different methods to finally determine the best method with the highest rate (CC). As a result, the dynamic clustering method with a correlation coefficient of 0.8479 had the best estimate in the reservoir studied in well A that the model in this method had 16 clusters. Finally, this model was applied in all studied wells to obtain permeability in the studied interval in wells without core. Also, the fuzzy logic method with a correlation coefficient of 0.7037 was introduced as the weakest method in this study.

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