Method for Formation Pore Pressure Prediction Based on Heterogeneous Transfer Learning

Accurate prediction of formation pore pressure is of great significance for drilling safety, the efficient development of oil and gas resources, and engineering risk control. Traditional methods based on empirical parameters or mechanical models are difficult to fully adapt to complex geological conditions. Although intelligent models have strong nonlinear modeling capabilities, they are highly dependent on large-scale and high-quality training data, and tend to suffer from poor generalization ability and insufficient adaptability in blocks with limited samples or significant differences in geological characteristics. To improve the adaptability of the model between different blocks, this study introduces a heterogeneous transfer learning method to construct a formation pore pressure prediction model suitable for scenarios with inconsistent feature spaces. This method can effectively transfer knowledge from the source domain to the target domain, alleviating the prediction difficulties caused by differences in data distribution. Experimental results show that the proposed method still maintains excellent prediction accuracy and stability under the conditions of limited training samples and complex geological conditions, and has better generalization ability and cross-block applicability compared with traditional models.