Predicting water permeability in sedimentary rocks from capillary imbibition and pore structure

Abstract

Please cite this article as: Benavente, D., Pla, C., Cueto, N., Galvañ, S., Martínez-Martínez, J., García-del-Cura, M.A., Ordóñez, S., Predicting water permeability in sedimentary rocks from capillary imbibition and pore structure, Engineering Geology (2015), AbstractIn this paper, absolute water permeability is estimated from capillary imbibition and pore structure for 15 sedimentary rock types. They present a wide range of petrographic characteristics that provide degrees of connectivity, porosities, pore size distributions, water absorption coefficients by capillarity and water permeabilities. A statistical analysis shows strong correlations among the petrophysical parameters of the studied rocks. Several fundamental properties are fitted into different linear and multiple expressions where water permeability is expressed as a generalized function of the properties. Some practical aspects of these correlations are highlighted in order to use capillary imbibition tests to estimate permeability. The permeability-porosity relation is discussed in the context of the influence of pore connectivity and wettability. As a consequence, we propose a generalized model for permeability that includes information about water fluid rate (water absorption coefficient by capillarity), water properties (density and viscosity), wetting (interfacial tension and contact angle) and pore structure (pore radius and porosity). Its application is examined in terms of the type of pores that contribute to water transport and wettability. The results indicate that the threshold pore radius, in which water percolates through rock, achieves the best description of the pore system. The proposed equation is compared against Carman-Kozeny's and KatzThompson's equations. The proposed equation achieves very accurate predictions of the water permeability in the range of 0.01 to 1000 mD. Highlights -Permeability expressions are obtained using open porosity and capillary coefficients.-The permeability -porosity relationship shows two zones defined by a porosity of 10%.-A generalized model for predicting intrinsic permeability is proposed.-The analysis of wetting reveals incomplete water wetting in the studied rocks.

Detection of moving capillary front in porous rocks using X-ray and ultrasonic methods

“…From Figure 2 we derived α = 1.38 10 −12 , k and C being in SI units. A similar relationship has been proposed recently by Benavente et al [13] for the prediction of permeability from capillarity and microstructural data:…”

“…Based on the analysis of a limited number of rocks, David et al [3] expected a correlation between the permeability k and the capillary coefficient C, as both parameters involve fluid displacement within the pore space. To verify this assumption on a larger dataset, capillaritypermeability data of the present study along with those of recent studies on oolitic limestones [11], dolostones [12], sandstones and carbonates [3] and a variety of sedimentary rocks [13] are reported in Figure 2. An overall positive correlation between C and the rock permeability is observed: a narrow elliptic envelope encompasses 97% of the data allowing the prediction of the rock permeability with a maximum error of about one order of magnitude.…”

Response to ENGEO7253 Discussion of: “Predicting water permeability in sedimentary rocks from capillary imbibition and pore structure” by D. Benavente et al., Engineering Geology (2015) [doi: 10.1016/j.enggeo.2015.06.003]

“…Here we present the complete wettability characterisation that we previously performed of four representative rocks studied in Benavente et al (2015): C3, T12, T13 and T14. They are constituted by different mineralogical and texture characteristics.…”

Nanoscale Pore Structure Characterization and Permeability of Mudrocks and Fine-Grained Sandstones in Coal Reservoirs by Scanning Electron Microscopy, Mercury Intrusion Porosimetry, and Low-Field Nuclear Magnetic Resonance

“…(2) Weighted Arithmetic Mean method (WAM method): (2) can also be used to derive (7). Based on the PSD data of MIP, wam (the weighted arithmetic mean of ) can be calculated.…”

“…Among many controlling parameters, permeability is the key factor governing the fluid transport in geological media and thus has the most important impact on the migration and recovery process of coalbed methane [1][2][3][4]. Moreover, pore structure characteristics, for example, pore types, pore morphology, pore size distribution (PSD), and porosity, are closely related to the permeability of reservoir rocks [5][6][7]. Therefore, investigation on the pore microstructural characteristics and permeability of typical coal reservoir rocks is of great significance to ensure the safe and efficient exploitation of coal and coalbed methane.…”