Well Log Data Outlier Detection With Machine Learning and Python

Identification of outliers is an essential step in the petrophysical machine learning workflow

Identification of outliers is an essential step in the machine learning workflow

Photo by Will Myers on Unsplash

Outliers are anomalous points within a dataset. They are points that don’t fit within the normal or expected statistical distribution of the dataset and can occur for a variety of reasons such as sensor and measurement errors, poor data sampling techniques, and unexpected events.

Within well log measurements and petrophysics data, outliers can occur due to washed-out boreholes, tool and sensor issues, rare geological features, and issues in the data acquisition process. It is essential that these outliers are identified and investigated early on in the workflow as they can result in inaccurate predictions by machine learning models.

The example in the figure below (from McDonald, 2021) illustrates core porosity versus core permeability. The majority of the data points form a coherent cluster, however, the point marked by the red square lies outside of this main group of points,…