Insights into Multifractal Characterization of Coals by Mercury Intrusion Porosimetry

Abstract

Mercury intrusion porosimetry (MIP) as a practical and effective measurement has been widely used in characterizing the pore size distribution (PSD) for unconventional reservoirs (e.g., coals and shales). However, in the process of MIP experiments, the high mercury intrusion pressure may cause matrix compressibility and result in inaccurate estimations of PSD. To get a deeper understanding of the variability and heterogeneity characteristics of the actual PSD in coals, this study firstly corrected the high mercury intrusion pressure data in combination with low-temperature N 2 adsorption (LTNA) data. The results show that the matrix compressibility was obvious under the pressure over 24.75 MPa, and the calculated matrix compressibility coefficients of bituminous and anthracite coals range from 0.82 to 2.47 × 10 −10 m 2 /N. Then, multifractal analysis was introduced to evaluate the heterogeneity characteristics of coals based on the corrected MIP data. The multifractal dimension D min is positively correlated with vitrinite content, but negatively correlated with inertinite content and mercury intrusion saturation. The multifractal dimension D max shows negative relationships with moisture and ash content, and it also emerges as a "U-shaped" trend with efficiency of mercury withdrawal. It is concluded that multifractal analysis can be served as a practical method not only for evaluating the heterogeneity of coal PSDs, but also for other unconventional reservoirs (e.g., shale and tight sandstone).

Fractal analysis of pore structures in transitional shale gas reservoirs in the Linxing area, Ordos Basin

“…This means that D M2 was unreliable, and it could be noted that its correlation index, R 2 , was also low. Research by other scholars had shown that matrix compression may result from excessive mercury intrusion pressure (Zhang, et al, 2019a;Zhang et al,. 2019b).…”

Methods for Petrological and Petrophysical Characterization of Gas Shales

“…The multifractal analysis theory is an extension of fractal analysis that can disclose more detailed fractal information using a set of generalized dimensions instead of a single fractal dimension. , Previous studies have confirmed that the multifractal analysis theory can provide more detailed information of both global and local pore heterogeneity in porous media. ,, For example, the multifractal parameters (e.g., D 1 and Hurst exponent) determined by MIP measurements show a strong relationship with macro- and mesopore porosity of rocks . Multifractal analysis of NMR data from shale reservoirs revealed that the flow zone indicator is well-correlated to multifractal parameters, such as the singularity strength range (Δα) and minimum multifractal dimensions ( D min ) .…”

“…Some fluid intrusion methods, such as LPGA-N 2 and MIP, are commonly combined to comprehensively investigate the shale full PSD because both methods combined cover a pore radius range of 3–120 nm. ,, It has been found that the PSD measured by LPGA-N 2 and MIP intersect at a pore radius of approximately 25 nm, and thus, the pore radius of ∼25 nm is commonly accepted as a connect point to reconstruct a full PSD by combining the PSD of <25 nm from the LPGA-N 2 measurement with the PSD of >25 nm from the MIP measurement. The PSD of <25 nm from MIP is not chosen because matrix compressibility begins at the mercury intrusion pressure of >24.75 MPa, corresponding to a pore radius of <25 nm based on the Washburn equation …”

Analysis of the Pore Structure and Fractal Characteristics of Coal and Gas Outburst Coal Seams Based on Matrix Compression Correction

“…where V pi is the pore volume increment under pressure, P i , cm 3 /g; ∆V obs(Pi) is the measured mercury volume under pressure, P i , cm 3 /g. Since the compressibility coefficient, K c , of the coal sample could not be determined directly during the correction process used in this paper by analyzing the statistics of previous studies [28][29][30][31][32][33][34], the average grouping was divided in accordance with the R 0,max , and the average value was taken. The fitting diagram of the coal sample compression coefficient, K c , with the R 0,max is shown in Figure 2.…”