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.