Electro-facies classification based on core and well-log data

Abstract

Abstract Facies studies represent a key element of reservoir characterization. In practice, this can be done by making use of core and petrophysical data. The high cost and difficulties of drilling and coring operations coupled with the time-intensive nature of core studies have led researchers toward using well-log data as an alternative. In the Teapot Dome Oilfield, where core data are limited to those from only a single well, we used well-log data for reservoir electro-facies (EF) studies via two unsupervised clustering methods, namely multi-resolution graph-based clustering (MRGC) and self-organizing map (SOM). Satisfactory results were obtained with both methods, distinguishing seven electro-facies from one another, where MRGC had the highest discriminatory accuracy. The best reservoir quality was exhibited by electro-facies 1, as per both methods. Our findings can be used to avoid some time-intensive steps of conventional reservoir characterization approaches and are useful for prospect modeling and well location proposal.

Keywords

• Multi-resolution graph-based clustering (MRGC)
• Self-organizing map (SOM)
• Electro-facies
• Lithofacies