Comparison of intelligent and statistical clustering approaches to predicting total organic carbon using intelligent systems
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We propose a two-step approach in predicting Total Organic Carbon (TOC) content from well log data. Initially, the well log data are classified into a set of electrofacies (EF). This classification does not require any further subdivision of the dataset but follows naturally based on the unique characteristics of well log measurements reflecting mineral and lithofacies responses within the logged intervals. In this study, the Self-Organizing Maps (SOM) as the intelligent data clustering methods are compared with the statistical clustering approaches including the Hierarchical Cluster Analysis (HCA) and K-means clustering to characterize and identify electrofacies. The results obtained from the all methods are compared to each other and the best method is chosen based on the cluster validity tests to clustering the petrophysical data into a certain number of EF. Afterwards, the TOC values are estimated from well log data by using the individual Artificial Neural Network (ANN) models constructed for each EF. In the second approach, the TOC data are estimated for the total interval by using a similar ANN model regardless of data clustering and EF determination. The results of two prediction methods are compared to each other and a third conventional ∆ log R technique as well. The results show that clustering of a formation into specific units (electrofacies) provides better results in TOC prediction compared to the models constructed for the whole dataset as a single cluster. In addition, intelligent systems are more efficient than the previous conventional techniques based on ∆ log R method. The proposed methodology is illustrated using a case study from the world's largest non-associated gas reservoir, the South Pars Gas Field, the Persian Gulf basin.
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