Machine Learning Models for Subsurface Pressure Prediction: A Data Mining Approach

Precise pore pressure prediction is highly essential for safe and effective drilling; however, the nonlinear and heterogeneous nature of the subsurface strata makes it extremely challenging. Conventional physics-based methods are not capable of handling this nonlinearity and variation. Recently, machine learning (ML) methods have been deployed by researchers to enhance prediction performance. These methods are often highly domain-specific and produce good results for the data they are trained for but struggle to generalize to unseen data. This study introduces a Hybrid Meta-Ensemble (HME), a meta model framework, as a novel data mining approach that applies ML methods and ensemble learning on well log data for pore pressure prediction. This proposed study first trains five baseline models including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Deep Feedforward Neural Network (DFNN), Random Forest (RF), and Extreme Gradient Boost (XGBoost) to capture sequential and nonlinear relationships for pore pressure prediction. The stacked predictions are further improved through a meta learner that adaptively reweighs them according to subsurface heterogeneity, effectively strengthening the ability of ensembles to generalize across diverse geological settings. The experimentation is performed on well log data from four wells located in the Potwar Basin which is one of Pakistan’s principal oil- and gas-producing regions. The proposed Hybrid Meta-Ensemble (HME) has achieved an R2 value of 0.93, outperforming the individual base models. Using the HME approach, the model effectively captures rock heterogeneity by learning optimal nonlinear interactions among the base models, leading to more accurate pressure predictions. Results show that integrating deep learning with robust meta learning substantially improves the accuracy of pore pressure prediction.