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

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