Applications of machine learning for facies and fracture prediction using Bayesian Network Theory and Random Forest: Case studies from the Appalachian basin, USA

Identification and prediction of facies and fractures are critical to subsurface geosystems analysis and hydrocarbon exploration. However, accurate prediction of facies and fractures is hard in the absence of high-resolution advanced geophysical logs (e.g. image logs) and core samples. This study demonstrates the application of machine learning algorithms, such as Bayesian Network Theory and Random Forest to predict the presence of different facies and fractures in sedimentary rocks using common well logs. We build and train supervised machine learning models using Bayesian Network Theory and Random Forest to classify facies and fractures in unconventional shale and conventional sandstone, and carbonate reservoirs. The trained machine-learning models are cross-validated 10-fold to check their robustness. Application of Bayesian Network theory and Random Forest shows that both facies and fractures can be predicted with high accuracy using limited common well logs. In addition, the Bayesian Network shows the complex causal relationship among the input petrophysical parameters and output (facies or fracture), which is its unique feature compared to other machine learning algorithms, such as Random Forest.