Deep-learning-based prediction of mutant formation pore pressure: A case study from the Xihu Sag in the East China sea

Accurate formation pore pressure prediction is essential for hydrocarbon exploration and development. Extensive exploration practices have demonstrated that subsurface formation pore pressure often exhibits nonlinear abrupt variations. Taking the Xihu Sag in the East China Sea as a case, mutant formation pore pressure (MFPP) is highly prevalent in Paleogene adjacent wells and formations, resulting in frequent drilling incidents within this sag. However, field researchers face challenges in predicting these pressure anomalies using analogy-based methods, which depend on data from nearby wells and formations. Conventional prediction approaches (e.g., Eaton’s and Bowers’ method) are constrained by linear assumptions and regional empirical parameter dependency, leading to significant errors in MFPP prediction. Typically, these methods yield prediction errors of approximately 5% in simple, gradual pressure intervals, while errors can fluctuate between 5% and 20% in complex pressure mutation intervals. To address these limitations and capture the underlying nonlinear relationships, deep learning methods have proven particularly advantageous. This study employed a deep learning approach to construct a model that correlates well-log lithology parameters with pore pressure. A hybrid neural network architecture called CNN-LSTM-ATTENTION was created by merging the benefits of several networks, innovating the model structure. Through 5-fold cross-validation, this architecture demonstrated its suitability for MFPP prediction. With an average accuracy of 96.7% in blind testing, the results suggest that this model performs exceptionally well predicting the MFPP. It greatly improves the MFPP's prediction accuracy compared to traditional pressure prediction methods. Meanwhile, it validates the feasibility of deep learning algorithms in addressing the challenges associated with MFPP.