Single-phase and two-phase flow properties of mesaverde tight sandstone formation; random-network modeling approach

Bashtani, Farzad; Maini, Brij B.; Kantzas, Apostolos

doi:10.1016/j.advwatres.2016.05.006

Cited by 16 publications

(41 citation statements)

References 46 publications

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“…The pressure of the non-wetting reservoir increases assuming the outlet pressure remains constant, leading to an increasing capillary pressure value. [21,52] The wetting phase remains in the corners of the elements after a polygonal element is filled with the non-wetting phase which ensures a continuous path for the wetting phase to the outlet during the primary drainage. The primary drainage process continues until a predefined wetting phase saturation is reached, all of the elements of the network are filled by the non-wetting phase, [23,29] or a predefined slope of the two-phase capillary pressure curve is obtained.…”

Section: Pore Space Descriptionmentioning

confidence: 99%

“…[21,23,29,52] Trapping of the non-wetting phase can occur during the imbibition due to bypassing as well as snap-off. Now, the model starts to increase the wetting phase pressure while keeping the non-wetting phase pressure constant in order to allow the wetting phase to invade the network which leads to a decreasing capillary pressure.…”

Section: Pore Space Descriptionmentioning

confidence: 99%

“…[37] Connectivity number distribution, however, should be obtained by iteration with the goal of matching the experimental and calculated capillary pressure curves in a way that the absolute error is less than 10 %. According to this figure, porosity and permeability of the samples and reconstructed networks can be estimated using Equation (52). The absolute error for matching the macroscopic properties should be less than 5 %.…”

Section: Single-phase Random Network Modellingmentioning

confidence: 99%

“…The next task is to match and/or predict two-phase flow properties. [52] Figure 13. Porosity of the generated random networks compared to experimental porosity of chosen samples.…”

Section: Single-phase Random Network Modellingmentioning

confidence: 99%

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Random network modelling approach to investigate the single‐phase and quasi‐static immiscible two‐phase flow properties in the Mesaverde formation

et al. 2016

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Understanding the microscopic flow behaviour of hydrocarbons and water in porous media gains importance as more and more reservoirs are being exploited. Network modelling techniques could be extended to tighter media as long as Darcy's law is applicable. 3D random networks are constructed in order to represent the Mesaverde formation which is located in north Wyoming, USA. The network modelling software solves the fundamental equations of single-phase and two-phase immiscible flow incorporating wettability and contact angle assuming a quasi-static displacement mechanism. Macroscopic properties of the porous media network representation such as porosity, absolute permeability, and formation factor are calculated and whenever possible compared to experimental data. Subsequently, immiscible two-phase flow properties such as capillary pressure, relative permeability, and resistivity curves are predicted and compared to available experimental data. The effect of interfacial tension alteration is also investigated as an attempt to demonstrate the capability of the network modelling technique to show physical fluid behaviour. It is observed that the capillary pressure curve obtained using MICP data can be used to calibrate and validate the network model generated to represent the sample. The study shows that the modified random network modelling technique is capable of modelling low permeable porous medium and predicting single-phase and immiscible two-phase flow properties assuming quasi-static displacement mechanism.

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Section: Pore Space Descriptionmentioning

confidence: 99%

Section: Pore Space Descriptionmentioning

confidence: 99%

Section: Single-phase Random Network Modellingmentioning

confidence: 99%

“…The next task is to match and/or predict two-phase flow properties. [52] Figure 13. Porosity of the generated random networks compared to experimental porosity of chosen samples.…”

Section: Single-phase Random Network Modellingmentioning

confidence: 99%

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Random network modelling approach to investigate the single‐phase and quasi‐static immiscible two‐phase flow properties in the Mesaverde formation

et al. 2016

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“…Most recently, many three-dimensional network models have been applied in the fields of capillary pressure and relative permeability by using digital rocks, and three-dimensional pore-scale network models for an oil-water two-phase system were proposed to discuss the effect of wettability on relative permeability and the residual oil distribution [11][12][13][14][15][16]. Based on the Mesaverde tight formation, the flow properties of one-phase and miscible two-phase systems were predicted by Bashtani et al [17] utilizing a random network model in tight porous media, compared with laboratory data. Some regular 3D three-phase network models were conducted for different wettability systems [18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Solid-Liquid Interfacial Effects on Residual Oil Distribution Utilizing Three-Dimensional Micro Network Models

Zhu

Liu

et al. 2017

Energies

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A number of experiments on fluid flow at the micro/nano-scale have demonstrated that flow velocity obviously deviates from the classical Poiseuille's law due to the micro forces between the wall and the fluid. Based on an oil-water two-phase network simulation model, a three-dimensional pore-scale micro network model with solid-liquid interfacial effects was established. The influences of solid-liquid interface effects including van der Waals force and wettability on the residual oil distribution and relative permeability were investigated through microscopic simulation. The effects of pore radius, pore-throat size ratio, shaping factor, and coordination number on the residual oil distribution were analyzed at the same time. The results showed that the oil recovery would be overestimated by about 4% without van der Waals force in a water-wet reservoir. The impact of van der Waals force on water-wet reservoirs was significantly obvious in contrast with oil-wet reservoirs. In addition, the residual oil distribution was significantly influenced by pore radius in water-wet reservoir, comparatively influenced by pore-throat size ratio in oil-wet reservoir. The present study illustrates the successful application of three-dimensional micro network models considering solid-liquid interfacial effects, and provides new insights for oil recovery enhancement.

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Scale‐up of pore‐level relative permeability from micro‐ to macro‐scale

Bashtani

Kantzas

2020

Can J Chem Eng

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Scaling up relative permeability curves of wetting and nonwetting phase of drainage and imbibition processes from pore scale to macro scale is a challenge. A new method for scaling up relative permeability from micro‐ to macro‐scale is proposed based on electrical analogy of multiphase fluid flow at pore scale. The method is validated against four synthetic porous media generated using homogeneous and heterogeneous grain size distributions, each of which were cut into eight sub‐segments. Single‐phase and two‐phase flow properties were calculated for the main blocks and the subsequent sub‐segments using random network modelling technique. Then, the subsegments were randomly distributed in space to reconstruct the main blocks and the proposed scale‐up method was employed to calculate the relative permeability curves of the reconstructed blocks. Results were compared to the ones obtained directly from the network model of the original blocks and show good agreement between the calculated and scaled‐up relative permeability curves of primary drainage and secondary imbibition. Furthermore, the model was tested on real media. Eight network models were extracted from pore size distribution of core samples obtained from the Green River basin located in the Mesaverde Formation. Flow properties obtained from the network models were validated against experimental data and good agreement was observed. These network models show a higher level of heterogeneity at micro‐scale. Then, the scale‐up methods were employed in order to reconstruct the macro‐scale sample and predict its properties. Scale‐up methods successfully predict the single‐phase and two‐phase flow properties of the sample.

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Single-phase and two-phase flow properties of mesaverde tight sandstone formation; random-network modeling approach

Cited by 16 publications

References 46 publications

Random network modelling approach to investigate the single‐phase and quasi‐static immiscible two‐phase flow properties in the Mesaverde formation

Random network modelling approach to investigate the single‐phase and quasi‐static immiscible two‐phase flow properties in the Mesaverde formation

Solid-Liquid Interfacial Effects on Residual Oil Distribution Utilizing Three-Dimensional Micro Network Models

Scale‐up of pore‐level relative permeability from micro‐ to macro‐scale

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