Application of Principal Component Analysis for Enhanced Well Log Interpretation: A Case Study from the Zubair Oil Field, South of Iraq

This study explores the application of Principal Component Analysis (PCA) to enhance well log interpretation in the Zubair oil field, south of Iraq. PCA is a statistical tool for dimensionality reduction, and it simplifies large datasets by transforming them into uncorrelated principal components, facilitating more accessible and insightful data analysis. The well log data set from an oil well (ZB-062) was analyzed using PCA and compared with conventional log interpretation methods. The PCA approach effectively identifies lithological formations and formation contacts, corroborating traditional interpretation results and demonstrating its utility in improving the accuracy of geological assessments. The geological setting of the Zubair oil field is characterized by its Cretaceous formations, providing a reliable framework for this analysis. The study highlights PCA's efficacy in processing well log data, revealing high correlations among various logs and identifying principal components that account for significant variability in the dataset. The integration of PCA in well log analysis enhances the interpretation of geological formations, contributing to more precise reservoir characterization and hydrocarbon exploration.