Defining Electrofacies from Logs and Core Data: Principles of Supervised and Non-Supervised Approaches

The definition of log facies from wireline logs and their calibration against core data is one of the keys to successful formation evaluation and rock-typing. It allows to derive the input data that is essential for building accurate 3D geocellular reservoir models. Multi-variate statistics provide the right tools that allow: to analyze wireline logs and core data; to predict electrofacies from a large number of wells and using complete sets of logs; to calibrate the detected log facies against core data; to predict the log facies at the non-cored intervals and non-cored wells; to quantify the uncertainty of the log facies determination. This paper, which is based on a real case study, describes two basic approaches for determining and predicting electrofacies, based on multi-variate statistical analysis: A non-supervised approach, that is purely based on multi-variate statistical analysis of the wireline logs, regardless of the core data. A supervised approach, that integrates wireline logs with core data. As a second step, it describes how these two basic approaches can be combined to identify and predict optimal log facies at cored and non-cored wells, in an integrated and robust workflow: Multi-variate density function interpretation and cluster identification Cluster interpretation and electrofacies definition, using core data Electrofacies prediction at the non-cored intervals and non-cored wells.