Enhancing reservoir characterization: A novel machine learning approach for automated detection and reconstruction of outliers-affected well log curves

The drilling process can result in irregular measurements due to unconsolidated geological formations, affecting the accuracy of wireline logging devices. This impacts the precision of elastic log measurements, such as velocity and density profiles, which are essential for reservoir characterization. The reliability of the wireline-logging tool is crucial in preventing inaccuracies when assessing reservoir rock properties. Previous studies have focused on applying machine learning (ML) techniques to wireline logging, but these methods have limited applicability, particularly for outlier detection and log reconstruction. In response, this study integrates both supervised and unsupervised ML techniques to enhance the accuracy of elastic log responses in reservoir characterization. Initially, density-based spatial clustering of applications with noise was applied for outlier detection, followed by feature selection to identify correlated logs for reconstructing the density log. A random forest regression model, optimized with particle swarm optimization (PSO), was then trained using the selected features. The comparative analysis showed a significant improvement in porosity estimation from the reconstructed density log compared to core data. Specifically, the comparison between core and original bulk density porosity yielded an R2 of 0.95 and a root mean squared error (RMSE) of 0.012. In contrast, the comparison between core and the rebuilt density log porosity resulted in an R2 of 0.98 and an RMSE of 0.007. The integration of advanced ML techniques with PSO-optimized random forest models represents a considerable advancement in the field of reservoir characterization. This approach enhances accuracy but also saves time and reduces manual effort, highlighting considerable potential for the advancement of methods in petroleum exploration and production.