US-12625510-B2 - Methods and devices for dynamic pore network modeling of two-phase flow in mixed-wet porous media

US12625510B2US 12625510 B2US12625510 B2US 12625510B2US-12625510-B2

Abstract

A method and system for predicting dynamic two-phase fluid flow in a mixed-wet porous medium by one or more central processing units (CPUs), comprising generating a set of movements of main terminal menisci (MTMs) of a two-phase fluid within a pore network model (PNM) of a porous media sample having a set of pore elements; by generating a pressure field for each of the set of movements of MTMs based on at least an inlet capillary pressure or a set of flow injection boundary conditions; identifying a set of local capillary pressures and a set of arc meniscus (AM) locations based on the pressure field; identifying a set of fluid displacement potentials based on at least the set of local capillary pressures and a set of threshold capillary pressures; and identifying a highest positive fluid displacement potential from a set of fluid displacements.

Inventors

Mohammad SEDGHI
Yanbin GONG
Mohammad Piri

Assignees

UNIVERSITY OF WYOMING

Dates

Publication Date: 20260512
Application Date: 20230829

Claims (20)

1 . A method for predicting dynamic two-phase fluid flow in a mixed-wet porous medium by one or more central processing units (CPUs), comprising: generating a set of movements of main terminal menisci (MTMs) of a two-phase fluid within a pore network model (PNM) of a porous media sample having a set of pore elements by generating a pressure field for movements of MTMs based on at least an inlet capillary pressure or a set of flow injection boundary conditions; identifying a set of local capillary pressures and a set of arc meniscus (AM) locations based on the pressure field; identifying a set of fluid displacement potentials based on at least the set of local capillary pressures and a set of threshold capillary pressures; identifying a highest positive fluid displacement potential from a set of fluid displacements of the two-phase fluid within the PNM; and performing a set of fluid displacements of the two-phase fluid within the PNM based on the highest positive fluid displacement potential.
2 . The method of claim 1 , wherein the method further comprises: identifying a set of fluid-fluid interface locations and the set of fluid displacement potentials based on the set of fluid displacements; and identifying the set of threshold capillary pressures and the set of fluid displacement potentials based on the identified set of fluid-fluid interface locations.
3 . The method of claim 2 , further comprising: identifying the highest positive displacement potential from the set of fluid displacements; or processing invalid assignments of fluid invasion for any of the set of pore elements within the PNM to reverse the invalid assignments; generating at least one of fluid saturation, fluid-fluid interface locations, and phase conductance for each pore element within the PNM; setting an injection volume of an invading phase corresponding to the set of fluid displacements; and determining, based on the set of fluid displacements, that there is no available fluid displacement within the PNM.
4 . The method of claim 1 , wherein identifying the highest positive fluid displacement potential from the set of fluid displacements comprises determining that there is no available fluid displacement within the PNM.
5 . The method of claim 1 , further comprising: outputting results of any set of fluid displacements if a number of flow steps remaining is equal to zero; or generating a new set of fluid displacements if the number of flow steps remaining is greater than zero.
6 . The method of claim 1 , further comprising: obtaining data for the PNM; identifying isolated pore elements within the PNM; processing the PNM to produce a set of decomposed portions of the PNM; identifying a set of properties for each of the set of decomposed portions of the PNM; and applying a set of boundary conditions to the set of fluid displacement potentials within the PNM.
7 . The method of claim 6 , wherein the set of boundary conditions comprise a mono-injection of a single fluid phase and a co-injection of two fluid phases at an inlet, wherein the co-injection is based on at least a set of all inlet pore throats and a specified volumetric injection rate of invading and defending phases.
8 . The method of claim 1 , further comprising generating local flow rates for each of a set of MTM phase boundaries.
9 . The method of claim 1 , wherein generating pressure fields for each fluid displacement comprises: calculating an outlet capillary pressure; generating a direction of MTM movements; updating the pressure fields; and determining whether the MTM movements are consistent with the updated pressure fields.
10 . The method of claim 9 , wherein generating the direction of MTM movements comprises determining whether an MTM is present in a pore element.
11 . The method of claim 9 , wherein updating the pressure fields comprises: calculating displacement potentials for all possible MTM movements; generating a local flow rate for each of the all possible MTM movements; and generating pressure fields based on the local flow rate.
12 . The method of claim 1 , wherein the set of fluid displacements comprise at least one of: a piston-like displacement; a cooperative pore-body filling displacement; a snap-off displacement; and a layer-formation-and-collapse displacement.
13 . The method of claim 1 , further comprising generating a threshold capillary pressure value for each of the MTM movements and the set of fluid displacement potentials.
14 . The method of claim 1 , further comprising assigning each pore element an advancing contact angle value and a receding contact angle value.
15 . The method of claim 1 , further comprising generating a hydraulic conductivity value for each of the MTM movements.
16 . The method of claim 1 , further comprising generating phase connectivity of the PNM.
17 . The method of claim 1 , wherein the pore elements comprise a set of pore bodies and a set of pore throats, wherein both the set of pore bodies and the set of pore throats correspond to physical geometries of the porous media sample.
18 . The method of claim 1 , wherein the porous media sample is a digital rock sample.
19 . An apparatus, comprising: a memory comprising executable instructions, and one or more processors configured to execute the executable instructions and cause the apparatus to: generate a set of movements of main terminal menisci (MTMs) of a two-phase fluid within a pore network model (PNM) of a porous media sample having a set of pore elements by generating a pressure field for movements of MTMs based on at least an inlet capillary pressure or a set of flow injection boundary conditions; identify a set of local capillary pressures and a set of arc meniscus (AM) locations based on the pressure field; identify a set of fluid displacement potentials based on at least the set of local capillary pressures and a set of threshold capillary pressures; identify a highest positive fluid displacement potential from a set of fluid displacements of the two-phase fluid within the PNM; and perform a set of fluid displacements of the two-phase fluid within the PNM based on the highest positive fluid displacement potential.
20 . A non-transitory computer-readable medium comprising executable instructions that, when executed by one or more processors of an apparatus, cause the apparatus to: generate a set of movements of main terminal menisci (MTMs) of a two-phase fluid within a pore network model (PNM) of a porous media sample having a set of pore elements by generating a pressure field for the movements of MTMs based on at least an inlet capillary pressure or a set of flow injection boundary conditions; identify a set of local capillary pressures and a set of arc meniscus (AM) locations based on the pressure field; identify a set of fluid displacement potentials based on at least the set of local capillary pressures and a set of threshold capillary pressures; identify a highest positive fluid displacement potential from a set of fluid displacements of the two-phase fluid within the PNM; and perform a set of fluid displacements of the two-phase fluid within the PNM based on the highest positive fluid displacement potential.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This applications claims benefit of U.S. Provisional Patent Application No. 63/402,002, filed Aug. 29, 2022, the entirety of which is herein incorporated by reference. BACKGROUND Field Aspects of the present disclosure generally relate to methods and systems for physical characterization of porous media, and more particularly, to predicting dynamic two-phase fluid flow in a mixed-wet porous medium. Description of the Related Art Modeling techniques for fluid flow through porous media are broadly implemented for petroleum resource development, materials engineering, food packaging, and medical technology development. Fluid flow modeling techniques may be equipped to illustrate both physical and chemical media properties like permeability, capillary pressure, fluid saturation, contact angle, wettability, or other similar properties, which may be used to characterize fluid behavior within a porous media sample without requiring expensive destruction of the sample. Although current techniques for modeling fluid flow through porous media are based on technological advancements made over many years, resultant models may still be tenuous representations of actual porous media. For example, fluid flow models of porous media may require low-resolution implementations to match currently available computational capabilities. As a result, fluid flow models based on porous media having microscale porosities may not accurately reflect physical and chemical properties of the media. Accordingly, there is an impetus to improve the accuracy of fluid flow modeling, including, for example: improving image processing techniques to allow for higher resolution model input and model output, improving image processing techniques to allow for more accurate model input and model output, improving in-situ characterization extraction techniques to better capture fluid behavior in microscale pore features, enhancing computational processing capability to reduce computational expense, enhancing computational processing capability increase modeling speed, increasing automation for iterative modeling steps, improving model capability for dynamic modeling of different fluid flow environments, improving model capability for dynamic modeling of larger fluid flow environments, and the like. Consequently, there exists a need for further improvements in fluid flow modeling of porous media to overcome the aforementioned technical challenges and other challenges not mentioned. SUMMARY One aspect of the present disclosure provides a method for predicting dynamic two-phase fluid flow in a mixed-wet porous medium by one or more central processing units (CPUs). The method may include generating a set of movements of main terminal menisci (MTMs) of a two-phase fluid within a pore network model (PNM) of a porous media sample having a set of pore elements by generating a pressure field for each of the movements of MTMs based on at least an inlet capillary pressure or a set of flow injection boundary conditions. The method may include identifying a set of local capillary pressures and a set of arc meniscus (AM) locations based on the pressure field. The method may include identifying a set of fluid displacement potentials based on at least the set of local capillary pressures and a set of threshold capillary pressures. The method may include identifying a highest positive fluid displacement potential from a set of fluid displacements of the two-phase fluid within the PNM. The method may include performing a set of fluid displacements of the two-phase fluid within the PNM based on the highest positive fluid displacement potential. One aspect of the present disclosure provides a non-transitory computer-readable medium comprising computer-executable instructions for predicting fluid flow in a mixed-wet porous medium that, when executed by one or more processors, cause one or more central processing units (CPUs) to perform a method of predicting fluid flow in the mixed-wet porous medium. The method may include generating a set of movements of main terminal menisci (MTMs) of a two-phase fluid within a pore network model (PNM) of a porous media sample having a set of pore elements by generating a pressure field for each of the movements of MTMs based on at least an inlet capillary pressure or a set of flow injection boundary conditions. The method may include identifying a set of local capillary pressures and a set of arc meniscus (AM) locations based on the pressure field. The method may include identifying a set of fluid displacement potentials based on at least the set of local capillary pressures and a set of threshold capillary pressures. The method may include identifying a highest positive fluid displacement potential from a set of fluid displacements of the two-phase fluid within the PNM. The method may include performing a set of fluid displacements of the two-phase fluid within the PNM based on the highest posit