Rock Typing Based on Wetting-Phase Relative Permeability Data and Critical Pore Sizes

Abstract

Summary Rock typing based on mineralogical, hydraulic, or petrophysical similarities is important to reservoir characterization and simulation. In the literature, classifying rocks using single-phase data has been widely studied. Most methods use porosity and permeability measurements to identify rocks with similar characteristic pore sizes. In this study, we invoke concepts from critical-path analysis (CPA) and propose a new rock-typing method on the basis of two-phase flow data, such as water relative permeability krw. We classify rocks based on their similarities in the critical pore radius rc at the same effective water saturation Se. For this purpose, we first convert the Sw−krw plots to Se−rc curves and then apply a curve clustering method to identify similar rocks. To evaluate the proposed approach, we simulated flow in pore networks with many different pore-scale properties. By varying the pore-throat size distribution, contact angle, pore coordination number, pore-shape distribution, and clay content, we generated a wide range of pore networks. Overall, two-phase flow in 240 pore networks were simulated. In addition to synthetic pore networks, pore networks were generated based on properties of Berea, Mt. Simon, and Fontainebleau sandstones. By analyzing the single-phase simulation results, we identified 8 and 15 rock types using the porosity-formation factor and reciprocal formation factor-permeability data, respectively. However, using the two-phase data, we detect 12 rock groups.

Estimating Permeability and Its Scale Dependence at Pore Scale Using Renormalization Group Theory

“…To evaluate the performance of the RGT and EMA in the estimation of REV permeability, we generated 40 synthetic pore networks (Table 3) and simulated the formation factor and permeability. Recall that in this set of pore network simulations, there exist 64 pore bodies along each side of the networks representing the REV size (Yokeley et al., 2021). The value of porosity in these pore networks ranged from 5% to 17%.…”

“…According to the results of Yokeley et al (2021), the REV is reached when there exist at least 64 pore bodies on each side of the pore network. In contrast to Networks 1.1-3.7, the actual physical size of these 40 networks is not the same since the pore-throat length distribution varied from one network to another following the truncated Weibull probability density function (same as in Equation 5).…”

Employing Statistical Algorithms and Clustering Techniques to Assess Lithological Facies for Identifying Optimal Reservoir Rocks: A Case Study of the Mansouri Oilfields, SW Iran

“…The change in the characteristics of the cavity geometry determines the existence of separate regions (flow units) with fluid flow characteristics. The classical separation of rock formations is based on geological observations and empirical relationships between permeability and porosity logs [1,[7][8][9]. However, a rock formation determined for each permeability and porosity may show different values, indicating the presence of several flowing units.…”

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

“…Gomes et al (2008) emphasized the importance of the main facies, sedimentary environments, the process of later diagenesis, and the relationship between rock and fluid by Specific Core Analysis (SCAL) to establish relationships between geological facies, petrophysical groups, and rock classification. Their proposed method of classifying carbonate rocks solved some industrial problems and differences between geological facies and petrophysical groups (Hosseini et al, 2023a;Kianoush et al, 2022a;Kolbikova et al, 2021;Kumar et al, 2023;Tavakkoli and Amini, 2006;Yokeley et al, 2021). Kharrat et al (2009) used ANNs and geostatistical data to model hydraulic flow units to estimate permeability and rock classification.…”