Machine learning enhanced formation pressure prediction using integrated well logging and mud logging.

ABSTRACT: The difficulty of accurately predicting abnormally high-pressure formation pressure is one of the critical challenges in the field of petroleum engineering. Due to the low accuracy of formation pressure prediction and the narrow drilling safety density window, accidents such as leakage and blowout occur frequently. To address this issue, improving the accuracy of pore pressure predictions is essential. The well logging and mud logging data were combined to analyze the correlation between various parameters. Analysis using the Spearman correlation coefficient revealed that pore pressure exhibits varying correlation relationships with different parameters. Pore pressure is closely related to factors such as depth, weight of hanging, and mud weight. Pore pressure has a medium to high correlat

SUBMITTER: Liang J 

PROVIDER: S-EPMC12222821 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature