Integrating hybrid weak learners for lithofacies classification using well log data

To identify depth intervals within reservoirs that have a high likelihood of containing hydrocarbons and possess adequate permeability for efficient hydrocarbon extraction, the examination of well cores is crucial. Given the heterogeneous nature of petrophysical properties across different locations, a single core per area is insufficient, leading to higher expenses due to the need for multiple cores. This research suggests the application of machine learning techniques, particularly weak learners, to predict lithofacies using well log data. The results obtained show the effectiveness of the hybrid weak learners approach, even when dealing with limited data.