Abstract

Spectral trend attribute analysis is a method for interpreting wireline log data. This method uses a prediction error filter to calculate a new data series. It involves applying a prediction error filter to the data, which creates a new series that represents the numerical difference between the predicted and actual values at each depth. This series, known as the Prediction Error Filter Analysis (PEFA) curve, provides geological insights into the continuity or discontinuity of the stratigraphic succession. Larger errors in the PEFA curve indicate significant breaks in the succession. By numerically integrating the PEFA curve, we obtain the Integrated PEFA (INPEFA) curve, which reveals hidden trends and patterns that may not be readily visible in the original log data. The INPEFA curve is particularly useful for subdividing geological successions and identifying key surfaces. It enhances the understanding of the stratigraphic analysis of well logs. In this paper an overview of the fundamental principles behind spectral trend attribute analysis has been presented. This method offers a valuable tool for geologists seeking innovative approaches to analyze and interpret well logs.