Summary
X-ray computed microtomography (XMT) is used for high-resolution,
nondestructive imaging and has been applied successfully to geologic media.
Despite the potential of XMT to aid in formation evaluation, currently it is
used mostly as a research tool. One factor preventing more widespread
application of XMT technology is limited accessibility to microtomography
beamlines. Another factor is that computational tools for quantitative image
analysis have not kept pace with the imaging technology itself.
In this paper, we present a new grain-based algorithm used for network
generation. The algorithm differs from other approaches because it uses the
granular structure of the material as a template for creating the pore network
rather than operating on the voxel set directly. With this algorithm, several
advantages emerge: the algorithm is significantly faster computationally, less
dependent on image resolution, and the network structure is tied to the
fundamental granular structure of the material. In this paper, we present
extensive validation of the algorithm using computer-generated packings. These
analyses provide guidance on issues such as accuracy and voxel resolution. The
algorithm is applied to two sandstone samples taken from different facies of
the Frontier Formation in Wyoming, USA, and imaged using synchrotron XMT.
Morphologic and flow-modeling results are presented.
Introduction
Subsurface transport processes such as oil and gas production are multiscale
processes. The pore scale governs many physical and chemical interactions and
is the appropriate characteristic scale for the fundamental governing
equations. The continuum scale is used for most core or laboratory scale
measurements (e.g., Darcy velocity, phase saturation, and bulk capillary
pressure). The field scale is the relevant scale for production and reservoir
simulation.
Multiscale modeling strategies aim to address these complexities by
integrating the various length scales. While pore-scale modeling is an
essential component of multiscale modeling, quantitative methods are not as
well-developed as their continuum-scale counterparts. Hence, pore-scale
modeling represents a weak link in current multiscale techniques.
The most fundamental approach for pore-scale modeling is direct solution of
the equations of motion (along with other relevant conservation equations),
which can be performed using a number of numerical techniques. The
finite-element method is the most general approach in terms of the range of
fluid and solid mechanics problems that can be addressed. Finite-difference and
finite-volume methods are more widely used in the computational fluid dynamics
community. The boundary element method is very well suited for low-Reynolds
number flow of Newtonian fluids (including multiphase flows). Finally, the
lattice-Boltzmann method has been favored in the porous-media community because
it easily adapts to the complex geometries found in natural materials.
A less rigorous approach is network modeling, which gives an approximate
solution to the governing equations. It requires discretization of the pore
space into pores and pore throats, and transport is modeled by imposing
conservation equations at the pore scale. Network modeling involves two levels
of approximation. The first is the representation of the complex, continuous
void space as discrete pores and throats. The second is the approximation to
the fluid mechanics when solving the governing equations within the networks.
The positive tradeoff for these significant simplifications is the ability to
model transport over orders-of-magnitude larger characteristic scales than is
possible with direct solutions of the equations of motion. Consequently, the
two approaches (rigorous modeling of the conservation equations vs. network
modeling) have complementary roles in the overall context of multiscale
modeling. Direct methods will remain essential for studying first-principles
behavior and subpore-scale processes such as diffusion boundary layers during
surface reactions, while network modeling will provide the best avenue for
capturing larger characteristic scales (which is necessary for modeling the
pore-to-continuum-scale transition).
This research addresses one of the significant hurdles for quantitative
network modeling: the use of high-resolution imaging of real materials for
quantitative flow modeling. We focus in particular on XMT to obtain 3D
pore-scale images, and present a new technique for direct mapping of the XMT
data onto networks for quantitative modeling. This direct mapping (in contrast
to the generation of statistically equivalent networks) ensures that subtle
spatial correlations present in the original material are retained in the
network structure.
© 2008. Society of Petroleum Engineers
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History
- Original manuscript received:
14 July 2005
- Meeting paper published:
9 October 2005
- Revised manuscript received:
17 September 2007
- Manuscript approved:
8 November 2007
- Version of record:
25 June 2008
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