Archive/Multiscale Fractal Characterization of Pore Structure and Reservoir Quality Based on Deep-Learning-Assisted Pore Extraction in the Majiagou Tight Dolomite Gas Reservoir, Central Ordos Basin, China
Multiscale Fractal Characterization of Pore Structure and Reservoir Quality Based on Deep-Learning-Assisted Pore Extraction in the Majiagou Tight Dolomite Gas Reservoir, Central Ordos Basin, China
Xiaohong Deng, Congjun Feng, Xiaoping Gao et al.
23 de julio de 2026
en

Abstract

Tight dolomite gas reservoirs are promising exploration targets, yet their evaluation is complicated by multiscale pore-throat heterogeneity and poor seepage connectivity. Here, high-pressure mercury intrusion (HPMI), nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), and deep-learning-assisted pore extraction were integrated to characterize the pore-throat structure and fractal features of the Middle Ordovician Majiagou Formation in the Ordos Basin. The reservoir is dominated by diagenetic-origin pores, mainly intercrystalline and intragranular dissolution pores, together with microfractures, and can be classified into three types with progressively poorer connectivity and flow capacity. Type I reservoirs contain more regular pores, larger pore-throat systems, and better storage and seepage capacity; Type II reservoirs are intermediate, whereas Type III reservoirs exhibit complex pore morphology, isolated pore networks, poor petrophysical properties, and limited gas-flow potential. The corresponding fractal dimensions are weakly correlated but complementary: DSEM captures pore-boundary complexity, DHPMI reflects pore-throat architecture and capillary-pressure-controlled seepage pathways, and DNMR reflects multiscale movable-fluid distribution. Clay minerals, especially illite-rich mixed layers, further intensify pore-throat heterogeneity. Increasing fractal dimension is generally associated with higher displacement and median pressures, but poorer connectivity, porosity, permeability, movable-fluid content, and gas deliverability. These results provide a basis for the quantitative evaluation of multiscale pore systems and reservoir quality in tight dolomite gas reservoirs.

IPC Classification

G06H04

Keywords

multiscalefractalcharacterizationporestructurereservoirqualitybaseddeep-learning-assistedextractionmajiagoutightdolomitecentralordosbasinchinafractionalreservoirspromisingexplorationtargetsevaluationcomplicated
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