Use of Pore Network Models to Simulate Laboratory Corefloods in a Heterogeneous Carbonate Sample

Abstract

Many carbonate rocks have pore features that are large at the core plug scale. Laboratory assessment of recovery behavior in these carbonates can be unreliable. Pore network modeling offers an approach to improve our analysis of complex flows in such heterogeneous rocks. We have discussed earlier our experimental data on a heterogeneous dolomite, the calibration methodology to link pore model inputs and laboratory data, and our initial attempts to use pore network models to match the experiments. This paper reports new results on our attempts to match the coreflood data. We analyze spatial correlation in our core sample using thin section and Computed Tomography (CT) data. We also investigate further the derivation of throat sizes using mercury injection data. Finally, we compare pore network model predictions to coreflood data. Introduction Carbonate rocks often display heterogeneities significant at the core plug scale. Conventional analysis is likely to be inadequate, and it is not surprising that the literature contains few references to multiphase flow studies on such rocks. A review of studies on carbonate systems by Espie et al. shows a wide range of process efficiency. They find that the residual oil saturation to water flood varies from 28 to 80% oip and that to tertiary miscible flood varies from 0 to 50% oip. Pore network (PN) models (figure 1) attempt to relate macroscopic behavior of porous media to simplified representations of the flow physics and pore structure. The general PN models consider capillary, viscous, and gravitational forces. They are useful for understanding multiphase flow mechanisms and the effects of pore scale heterogeneity. However, except for a few cases, pore network models have not been linked directly to data on real rocks. Technological advances in computing and flow visualization are now allowing us to connect laboratory tests and network models more closely. For example, we can now run 500 000 node models to recreate fine scale CT flow images. Experimental Data on Carbonate Sample In an earlier paper, we have discussed our experimental data on a dolomite rock from the Beaverhill Lake formation, Canada (k=300md, =14%, L=4.9 Cm, d=3.8cm). Figure 2 is the CT derived porosity distribution along a cross-section parallel to flow. It shows that the porosity is heterogeneously distributed. The thin section picture in figure 3 displays millimeter size pore features. Figure 4 is a CT image of a unit mobility, equal density miscible flood and it reveals that the flow channeled along a connected high porosity path. Figure 5 contains cross-sectional images of water saturation obtained at Swi, and at the end of different rate water floods. These images correspond closely to the porosity image in figure 2. At the flow rate of 0.5 feet/day, the water mostly invades the high porosity, high permeability channel. High oil saturation of 67% PV remains in large unswept areas. Completely new channels are invaded as the flow rate is increased and the residual oil saturation at the flow rate of 120 feet/day has decreased to 37% PV. This waterflood is a drainage process and new channels are invaded as the flow rate increases. We were unsuccessful in using conventional analysis to extract meaningful information from this data. P. 359^

Microtomography and Pore-Scale Modeling of Two-Phase Fluid Distribution

“…The simplicity of the idealized geometry of the flow channels makes possible simulation of a wide variety of multi-phase fluid displacement scenarios (Bakke and Øren 1997;Xu et al 1999;Blunt 2001;Knackstedt et al 2001;Patzek 2001;Øren and Bakke 2003;van Dijke et al 2007). However, this simplicity is compensated by the difficulty of generating a network of pore bodies and pore throats, which would adequately represent the pore space of a particular rock sample.…”

Study on microheterogeneity and seepage law of reservoir after long-term water flooding

“…Moreover, after long-term flooding in reservoir of the Jidong Oilfield, the separation of pore and throat becomes better, and microheterogeneity of reservoir gradually becomes weaker. The rule has not been reported before (Elliott and Dover 1982;Sorbie et al 1989;Lymberopoulos and Payatakes 1992;Xu et al 1999;Fredrich et al 1995;Fredrich 1999;Delerue et al 1999;Lindquist and Venkatarangan 1999;Liang et al 2000;Blunt (2001); Radlinski et al 2002;Siddiqui and Khamees 2004;Coenen et al 2004;Arns et al 2004Arns et al , 2005Knackstedt et al 2006;Al-Kharusi and Blunt 2007;Tomutsa et al 2007;Hou et al 2007;Youssef et al 2008;Dong and Blunt 2009;Mustafa et al 2009;Xu et al 2010;Peng et al 2012;Saurabh et al 2013;Theis et al 2014). …”

“…Since the pore network model was proposed by Fatt (1956aFatt ( , b, c) in 1956, it has been a powerful tool to study microscopic reservoir structure and seepage characteristics. Elliott and Dover (1982), Sorbie et al (1989), Lymberopoulos and Payatakes (1992), Xu et al (1999), Fredrich et al (1995), Fredrich (1999, Delerue et al (1999), Lindquist and Venkatarangan (1999), Liang et al (2000), Blunt (2001), Radlinski et al (2002), Siddiqui and Khamees (2004), Coenen et al (2004), Knackstedt et al (2006), Al-Kharusi and Blunt (2007), Tomutsa et al (2007), Blunt (2009), Peng et al (2012), Saurabh et al (2013), Theis et al (2014), have researched the technologies and algorithms of constructing the digital core and 3D pore network model, and the 3D pore network model has been rapidly developed and widely applied. Among those technologies, the method of constructing 3D pore network model with digital core reconstructed by high-precision X-ray CT scanning is dominant, and it is future direction of researching microstructure of reservoir pore (Hou et al 2007;Arns et al 2004Arns et al , 2005Youssef et al 2008;Mustafa et al 2009).…”

Multiscale Flow and Transport in Highly Heterogeneous Carbonates

“…They concluded from their study that the conventional core analysis methods including the capillary number and capillary end effect methods failed to explain their experiment data. Therefore, pore-network models were developed (Xu et al 1999) to analyze the flow and transport through heterogeneous carbonates. Cooper et al (1999) also used the pore-network model to understand the flow properties of carbonates.…”