Downhole logs present a detailed plot of formation parameters versus depth. From these datasets, interpreters can identify various lithologies, and fault or foliation orientations and quickly recognize pay zones in the subsurface ore bodies. The ability to interpret a log lies in recognizing the significance of each measurement, or combination of measurements. This takes a level of expertise and experience.

Although the manual interpretation of wireline log data is still common, log evaluation has also evolved beyond this to include semi-automated interpretations and the application of machine learning algorithms. As logging tools and interpretive methods have developed in accuracy and sophistication, they are playing an expanded role in the geological decision-making process.

The level of interpretation required is highly dependent on project requirements. This can cover the range from basic lithology blocking of the log profiles through to 3D modelling and integration of the downhole data into current resource or structural models.