Characterizing full-scale pore-throat systems constitutes a critical challenge in the investigation of hydrocarbon-bearing spaces within tight unconventional reservoirs. Given the intricate nature of micro–nano-scale pore throats, individual characterization techniques are insufficient to achieve a comprehensive and precise description. In response, this study develops a Gaussian Mixture Model (GMM)-oriented methodology for full-scale pore-throat analysis integrating multi-source data, which encompasses five successive procedures: data optimization, optimal cluster number determination, model analysis, data fusion, and data reconstruction. Taking tight mixed-lithology samples from Block D of the Qaidam Basin as the research object, effective pore-throat thresholds were defined based on lithology-dependent breakdown pressures to facilitate cluster analysis of multi-source datasets. Following the screening of representative pore-throat clusters and data fusion via Gaussian Mixture functions, the full-scale pore-throat distribution was ultimately derived. Comparative analysis demonstrates that Nuclear Magnetic Resonance (NMR) and High-Pressure Mercury Intrusion (HPMI) data exhibit satisfactory fitting consistency at major cluster peaks, with NMR being more effective in resolving nanopores and HPMI excelling in characterizing medium to large pores. Comprehensive evaluation results validate that the proposed methodology enables efficient integration of multi-technical data, uncovers hidden pore-throat systems, and realizes innovative fractal dimension analysis of full-scale pore-throat structures by taking pore-throat clusters as the basic analytical unit. Consequently, this work offers a novel methodological framework for the quantitative characterization of full-scale pore-throats using multi-source data.
Luo et al. (Fri,) studied this question.