Tight sandstone gas serves as an important unconventional hydrocarbon resource, and outstanding results have been obtained through its discovery both in China and abroad given its great resource potential. However, heated debates and gaps still remain regarding classification standards of tight sandstone gas, and critical controlling factors, accumulation mechanisms, and development modes of tight sandstone reservoirs are not determined . Tight sandstone gas reservoirs in China are generally characterized by tight strata, widespread distribution areas, coal strata supplying gas, complex gas–water relations, and abnormally low gas reservoir pressure. Water and gas reversal patterns have been detected via glass tube and quartz sand modeling, and the presence of critical geological conditions without buoyancy-driven mechanisms can thus be assumed. According to the timing of gas charging and reservoir tightening phases, the following three tight sandstone gas reservoir types have been identified: (a) “accumulation–densification” (AD), or the conventional tight type, (b) “densification–accumulation” (DA), or the deep tight type, and (c) the composite tight type. For the AD type, gas charging occurs prior to reservoir densification, accumulating in higher positions under buoyancy-controlled mechanisms with critical controlling factors such as source kitchens (S), regional overlaying cap rocks (C), gas reservoirs, (D) and low fluid potential areas (P). For the DA type, reservoir densification prior to the gas charging period (GCP) leads to accumulation in depressions and slopes largely due to hydrocarbon expansive forces without buoyancy, and critical controlling factors are effective source rocks (S), widely distributed reservoirs (D), stable tectonic settings (W) and universal densification of reservoirs (L). The composite type includes features of the AD type and DA type, and before and after reservoir densification period (RDP), gas charging and accumulation is controlled by early buoyancy and later molecular expansive force respectively. It is widely distributed in anticlinal zones, deep sag areas and slopes, and is controlled by source kitchens (S), reservoirs (D), cap rocks (C), stable tectonic settings (W), low fluid potential areas (P), and universal reservoir densification (L). Tight gas resources with great resource potential are widely distributed worldwide, and tight gas in China that presents advantageous reservoir-forming conditions is primarily found in the Ordos, Sichuan, Tarim, Junggar, and Turpan-Hami basins of central-western China. Tight gas has served as the primary impetus for global unconventional natural gas exploration and production under existing technical conditions.

Nitrogen adsorption experiments have been extensively applied to shale pore structure research and evaluation. The pore structure can be quantitatively characterized in accordance with the nitrogen adsorption–desorption isotherm using various calculation models, whereas the results obtained using different models can more effectively indicate the pore characteristics of shale remains unclear. Further, there has not been any unified process in the optimization of calculation models for pore size distribution (PSD). In this study, the Barret–Joyner–Halenda adsorption (BJH-AD) and BJH desorption (BJH-DE) models were used with Longmaxi Formation shale as an example. Subsequently, the density functional theory (DFT) calculations were conducted on different shale lithofacies samples. Next, the pore structure parameters and heterogeneity obtained using different models were compared, and the consistency parameters of different models were obtained in accordance with Cronbach’s alpha. The results indicated that the pore structure parameters obtained using the BJH-AD model were underestimated since the macroscopic thermodynamic theory was not applicable to this study. The DFT model showed multiple peaks in the range of 1–10 nm, whereas the BJH-DE model had a significant artificial peak in the range of 3.8 nm due to the tensile strength effect, thus suggesting that the DFT model is more capable of characterizing the pores with a pore size 10 nm lower than the BJH model. The PSD curves generated using the three models exhibited multifractal characteristics, whereas the results of the heterogeneity achieved using different models were different. Moreover, the consistency of the results of different models can be studied in depth by combining Cronbach’s alpha with various heterogeneity parameters. The DFT model exhibited high consistency in pore structure parameters and pore heterogeneity, thus suggesting that the DFT method of N₂ is the optimal physical adsorption data analysis method in the shale mesoporous range. Accordingly, the nitrogen adsorption curve, the hysteresis loop shape, multifractal parameters, and Cronbach’s alpha were integrated to generate a working flow chart of the nitrogen adsorption model for N₂-adsorption-model optimization.