A data-driven approach for missing well-log prediction using KNN regression

In petroleum geophysics, well logs are fundamental for subsurface characterization; however, missing logs frequently occur due to tool failure, legacy data gaps, or economic constraints, limiting reliable reservoir evaluation. The primary aim of this study is to develop and evaluate a simple, nonparametric machine learning framework for predicting missing geophysical well logs using K-Nearest Neighbors (KNN) regression. A secondary aim is to assess the effectiveness of correlation-guided feature