Application of Machine Learning Algorithms for Predicting Formation Pore Pressure: A Case Study in the Sapphire field, Offshore Nile Delta, Egypt

Accurate pore pressure prediction is critical for safe and efficient drilling operations. However, conventional methods face challenges due to insufficient quality of seismic data and limited wellbore availability. This study explores the application of machine learning algorithms (decision tree, random forest and XGBoost) to predict pore pressure at the offshore Nile Delta Basin, especially in the Sapphire Field, which consists of a complex geological environment with vertically stacked reservoirs. The XGBoost model achieved the best performance with 99% accuracy on training data and 97% on test data, using 53,711 recorded data points from seven drilling records in four wells. The model has been adjusted to accurately predict pore pressure using existing drilling logs in the absence of direct measurements. Error analysis demonstrated a strong correlation with actual data. This approach has the potential to enhance pressure prediction operations and mitigate drilling risk.