Machine learning-driven classification of hydraulic flow units for enhanced reservoir characterization

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(3)

Published: March 1, 2025

This study focuses on the classification of Hydraulic Flow Units (HFUs) within Lower Goru reservoir using a hybrid modeling approach for more precise and data-driven characterization. The methodology begins with K-means clustering, which groups into distinct HFUs based properties. To enhance accuracy this classification, Particle Swarm Optimization (PSO) is employed to optimize clustering process. flow capacity rock quality each HFU are then assessed two key indicators: zone indicator (FZI) index (RQI). results reveal four HFUs: Clean Sandstone, Clayey Shaly Shale. Among these, 1 (Clean Sandstone) exhibits highest FZI RQI values, indicating excellent capacity, while 2 (Clayey demonstrates moderate suggesting good potential. In contrast, 3 (Shaly 4 (Shale) show progressively lower reflecting poorer reduced integrated significantly improves precision characterization by combining PSO optimization, petrophysical indicators such as RQI. study's findings not only provide valuable understanding dynamics fluid potential but also our comprehension spatial distribution properties HFU, offering solid foundation optimizing hydrocarbon recovery enhancing management approaches.

Language: Английский

Integrating geophysical logs for reservoir assessment of paleocene reservoir, Manzalai gas field, Kohat Basin, Pakistan

Carbonates and Evaporites, Journal Year: 2025, Volume and Issue: 40(2)

Published: May 6, 2025

Language: Английский