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Pore structure characterization and deliverability prediction of fractured tight glutenite reservoir based on geophysical well logging

Hu Tingting, Pan Tuo, Chen Liang, Li Jing, Liu Yu

Acta Geophysica

2024

Vol. 72, no. 1

273--286

Tight glutenite reservoirs characterization and effective hydrocarbon-bearing formation identification faced great challenge due to ultra-low porosity, ultra-low permeability and complicated pore structure. Fracturing fracture-building technique always needed to obtain deliverability because of poor natural productive capacity. Pore structure characterization and friability prediction were essential in improving such type of reservoir evaluation. In this study, fractured tight glutenite reservoirs in Permian Jiamuhe Formation that located in northwest margin of Junggar Basin, northwest China, were chosen as an example, and 25 typical core samples were drilled and simultaneously applied for mercury injection capillary pressure (MICP), nuclear magnetic resonance (NMR) and whole-rock mineral X-ray diffraction experiments. A novel method of synthetizing pseudo-pore-throat radius (Rc) distribution from porosity frequency spectra was established to characterize fractured formation pore structure. Quartz and calcite were considered as the fragile mineral, and rock mineral component ratio method was used to predict brittleness index. Meanwhile, the statistical model raised by Jin et al. (SPE J 20:518-526, 2015) was used to predict two types of fracture toughness. And then, brittleness index and fracture toughness were combined to characterize tight glutenite reservoirs friability. Combining with maximal pore-throat radius (Rmax, reflected rock pore structure) and friability, our target formations were classified into four clusters. In addition, relationships among pore structure, friability and daily hydrocarbon production per meter (DI) were analyzed, and a model to predict DI from welllogging data was established. Comparison of predicted DI with the extracted results from drill stem test (DST) data illustrated the reliability of our raised models. This would be valuable in determining optimal hydrocarbon production intervals and formulating reasonable developed plans.