Aspect-Ratio-Dependent Pore-Size Distribution from MICP Measurement

Abstract Pore size distribution (PSD) is one of the most important properties for characterizing the pore systems of porous media. Typically, a single aspect ratio (α_), defined as the ratio of the short-axis diameter over the long-axis diameter of a pore, is assumed (i.e., set to 1.0) when calculating PSD from mercury injection capillary pressure (MICP) data. This assumption implies that pores can be modeled as cylindrical tubes. In carbonate rocks, the pore network is complex and comprises multiple pore systems with varying values of α_. The objective of this study is to quantify the effect of α_ on PSD derived from MICP measurements. To achieve that objective, we initially characterize pores with different pore systems (multimodal) with different α_, which is measured from high-resolution digital images or derived from sonic measurements. The α_ distribution from digital images can be grouped into two (bimodal) or three (trimodal) pore systems, with the average α_ for each pore system being calculated. Next, we establish the relationship between capillary pressure (Pc), semi-long axis (a), and α_ for elliptical tubes. This relationship is used to transfer Pc to values of a representing different pore size. We then derive individual PSDs for each pore system by automatically selecting the best-fit combination of pore volume (PV)-based or frequency-based probability density distributions based on MICP data. We apply the proposed method to two MICP datasets, in which the corresponding α_ measurements from digital images indicate the presence of multimodal pore systems. An experiment-based analytical method and a multi-Gaussian decomposition method were executed for comparison. The results demonstrate that the proposed method provides a better fit to MICP data compared to traditional methods. This improvement arises from its consideration of α_ when converting Pc to pore size. In addition, one challenge with experiment-based analytical methods is that they are not directly based on PSD, and the fitting parameters (such as the Thomeer pore geometrical factor, G) may not be sufficient to describe the complex geometrical characteristics. On the other hand, the multi-Gaussian decomposition method assumes PV-based Gaussian distributions, overlooking frequency-based and other distribution types (e.g., uniform, triangular, beta, gamma, etc.), which the results of the proposed method have been shown to be necessary. By using the proposed method, additional pore-structure parameters, including the fractal dimension describing pore network tortuosity and volume fractions of each pore system, are also obtained during inversion. This yields detailed characterization information on the pore network. By incorporating α_ into the process of converting Pc to pore throat sizes and deriving individual PSDs for each pore system, more details of PSD are revealed, leading to improved reservoir characterization.