Well Log Cluster Analysis: an Innovative Tool for Unconventional Exploration Tristan Euzen, Eric Delamaide, Tom Feuchtwanger, Kim Kingsmith.

Exploring for economically viable accumulations of hydrocarbons, the task of explorationists is becoming considerably more complex in mature basins because of the unconventional nature of most remaining resources. Analyzing a large volume of data is required in order to develop a comprehensive understanding of reservoir distributions and/or their production performance characteristics. Furthermore, the unconventional nature of reservoirs makes it challenging to interpret well log data and indentify potential gas pay using conventional data analysis approaches.

Well log cluster analysis is an innovative approach that provides the explorationist with an efficient tool for analyzing, screening and filtering a large volume of well log data, in order to identify and map potential hydrocarbon accumulations. The method involves seeking high density areas (clusters) in the multivariate space of well log data, in order to define classes of similar log responses. These classes called electrofacies can be calibrated using various sources of data such as core description or production data and can then be used to process very quickly hundreds of wells.

Cluster analysis and electrofacies classification have been applied to develop unconventional gas prospects in the Upper Mannville incised valley fills of the WCSB. The reservoirs dominantly consist of arkoses with complex mineralogy, and are characterized by both conventional and unconventional production from low resistivity pays. A cluster analysis was first performed on 5 producing wells used as training samples, and an electrofacies classification was then performed on 395 wells in an area of 28 townships. A quality control of the results was done by checking the electrofacies interpretation in front of producing intervals from 41 wells. The preliminary results are very promising, with 89% of the producing intervals used in the blind test properly predicted by the electrofacies classification. Finally, potential gas bearing intervals identified by the electrofacies classification have been mapped in order to define prospective areas. This approach has many potential applications in the field of unconventional hydrocarbons, especially where conventional log analysis doesn't work properly and where a large volume of well log data is available.

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