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 selection and distance-based learning for estimating continuous log responses in data-limited scenarios. The proposed workflow is applied to an open-source dataset from the University of Kansas and focuses on predicting Sonic (Delta T, DT) and Gamma Ray (GR) logs across five wells. Pearson correlation analysis is used to identify the most relevant input features, followed by min–max normalization to ensure distance metric consistency. The dataset is split into training and testing subsets using a 70/30 ratio, and the optimal number of neighbors (k) is determined by minimizing the root mean square error (RMSE). The KNN models achieved high predictive performance, with test R 2 values ranging from 0.942 to 0.963 for DT logs and from 0.927 to 0.930 for GR logs. Although minor overfitting was observed, the difference between training and testing performance remained limited, indicating satisfactory generalization. The results demonstrate that KNN regression provides a robust and computationally efficient solution for missing well-log prediction in geophysical applications, offering a practical alternative for enhancing reservoir characterization when log data are incomplete.