Abstract
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.