Bayesian Neural Network Method for Intelligent Drilling Pattern Discovery using Block Geological Information as Prior Knowledge
Authors: Yan Zhi , Song Xianzhi , Zhu Zhaopeng , Xiao Shijie , Zhou Detao , Pan Tao , Liu Muchen , Li Qifeng 
Published: February 2025

Summary
In recent years, researchers have frequently utilized near-well interpolation or real-time mapping of logging parameters to characterize geological data, integrating these approaches with intelligent time-series algorithms to develop predictive models for the rate of penetration (ROP). These methodologies allow for the sequential acquisition of comprehensive well geological information and point-to-point real-time geological data collection. However, due to uncertainties in geological features such as interbedded lithologies, and the discrete layer-like distribution of geological attributes with increasing depth, these models often underperform in complex geological settings.
This study has developed a method for acquiring geological information in accordance with stratigraphic distribution patterns and integrates it into an intelligent prediction model for ROP, using the representation of geological uncertainty. Initially, near-well logging information is interpolated into the current well as discrete geological data under stratigraphic constraints, using a sliding window to process layers at fixed depth intervals. Then, a Gaussian mixture model is applied to perform variational inference on the geological data within each window, using the Bayesian Information Criterion to assess the distribution of geological attributes within these windows. This distribution is incorporated as geological prior knowledge into a Bayesian neural network (BNN) model, where an online update model for ROP prediction is established in each window based on real-time logging data.
This study conducted comprehensive wellbore testing on four wells using the same tool assembly and stratigraphic levels. In this region characterized by significant interbedded lithology, the real-time ROP prediction model constructed using traditional geological information acquisition methods underperformed compared to the model proposed in this study. The model developed in this research outputs the 95% confidence interval for predicted ROP, with R2 values ranging from 0.86 to 0.91. Moreover, the confidence interval encompasses 97% of the actual ROP labels, indicating the model's effective performance and accurate representation of geological uncertainty. Additionally, under the assumption of independently and identically distributed geological attributes, the model independently assesses the severity of drastic lithological changes along the depth sequence based on the size of the uncertainty interval within the geological attribute window.
This study presents a method for acquiring geological information aligned with stratigraphic patterns and integrates geological uncertainty into an ROP prediction model using a Bayesian neural network. The model reliably predicts ROP values and geological uncertainty at current depths in formations with significant interbedded lithology. It also evaluates the severity of soft and hard rock interbedding between current and lower depths, providing crucial guidance for on-site drilling operations.