Abstract
The lithofacies identification is critical for forecasting sweet spots of hydrocarbon explorations. Well logs are widely used in lithofacies identifications because they are petrophysical measurements of subsurface stratigraphy which reflect lithological successions and depositional processes. The traditional lithofacies identification from well logs is a manual work that is time-consuming and bias-prone. An automated and bias-free method is in demand. To this end, we created a lithofacies dataset of eleven wells with well log records and lithofacies descriptions that were interpreted manually based on facie s analysis of drilling cutting descriptions and well logs. Then we developed machine learning models that were trained using the lithofacies dataset of the fluvial-lacustrine Upper Trias sic Xujiahe and Lower Jurassic Ziliujing formations in Yuanba Area, northern Sichuan Basin of southwestern China. By employing extreme gradient boosting and resampling algorithms, this machine learning model is efficient and outperforms support vector machine and multiple-layer perception, as indicated by its highest accuracy and Fl-score of 0.90, the highest AUC of 0.94, as well as the shortest training time. Moreover, the result suggests that resampling is necessary for lithofacies identification with the unbalanced dataset. A combined method of over sampling and undersampling is better than a single resampling method. This study presents a successful application of machine learning in fluvial-lacustrine lithofacies identification from well logs and suggests the great potentiality of machine learning in subsurface hydrocarbon explorations.
NSTL