Micropore network modelling from 2D confocal imagery: impact on reservoir quality and hydrocarbon recovery

Jobe, Tom; Geiger, Sebastian; Jiang, Zeyun; Zhang, Shuo; Agar, Susan M.

doi:10.1144/petgeo2017-017

Cited by 8 publications

(3 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method can be expensive and time consuming. Furthermore, it provides little insight into the local multi-scale structural influences on permeability, which is a well-recognized and significant challenge for upscaling carbonates 21 – 25 because bulk measurements do not provide enough information to accurately extrapolate flow properties to other samples based on structural information. Moreover, flow measurements at the core scale are often not representative of flow at the reservoir scale due to large-scale heterogeneities present in the reservoir that are not captured in a small cm-scale core sample (e.g.…”

Section: Introductionmentioning

confidence: 99%

Upscaling the porosity–permeability relationship of a microporous carbonate for Darcy-scale flow with machine learning

Menke

Maes

Geiger

2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

The permeability of a pore structure is typically described by stochastic representations of its geometrical attributes (e.g. pore-size distribution, porosity, coordination number). Database-driven numerical solvers for large model domains can only accurately predict large-scale flow behavior when they incorporate upscaled descriptions of that structure. The upscaling is particularly challenging for rocks with multimodal porosity structures such as carbonates, where several different type of structures (e.g. micro-porosity, cavities, fractures) are interacting. It is the connectivity both within and between these fundamentally different structures that ultimately controls the porosity–permeability relationship at the larger length scales. Recent advances in machine learning techniques combined with both numerical modelling and informed structural analysis have allowed us to probe the relationship between structure and permeability much more deeply. We have used this integrated approach to tackle the challenge of upscaling multimodal and multiscale porous media. We present a novel method for upscaling multimodal porosity–permeability relationships using machine learning based multivariate structural regression. A micro-CT image of Estaillades limestone was divided into small 603 and 1203 sub-volumes and permeability was computed using the Darcy–Brinkman–Stokes (DBS) model. The microporosity–porosity–permeability relationship from Menke et al. (Earth Arxiv, https://doi.org/10.31223/osf.io/ubg6p, 2019) was used to assign permeability values to the cells containing microporosity. Structural attributes (porosity, phase connectivity, volume fraction, etc.) of each sub-volume were extracted using image analysis tools and then regressed against the solved DBS permeability using an Extra-Trees regression model to derive an upscaled porosity–permeability relationship. Ten test cases of 3603 voxels were then modeled using Darcy-scale flow with this machine learning predicted upscaled porosity–permeability relationship and benchmarked against full DBS simulations, a numerically upscaled Darcy flow model, and a Kozeny–Carman model. All numerical simulations were performed using GeoChemFoam, our in-house open source pore-scale simulator based on OpenFOAM. We found good agreement between the full DBS simulations and both the numerical and machine learning upscaled models, with the machine learning model being 80 times less computationally expensive. The Kozeny–Carman model was a poor predictor of upscaled permeability in all cases.

show abstract

Section: Introductionmentioning

confidence: 99%

Upscaling the porosity–permeability relationship of a microporous carbonate for Darcy-scale flow with machine learning

Menke

Maes

Geiger

2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Saturation of permeable rock samples with a pore-filling material, e.g., epoxy, that provides a contrast between the optical and/or electric properties of the void space and mineral grains has been reported by several researchers, for example (Waldo and Yuster, 1937, Pittman and Duschatko, 1970, Wardlaw, 1976, Yanguas and Paxton, 1986, Klaver et al, 2015, Jobe et al, 2018. The pore-filling epoxy resin supports fragile pores at different stages of thin-section preparation, such as grinding or polishing.…”

Section: Introductionmentioning

confidence: 96%

Validation of High Pressure Resin Impregnation Technique for High Resolution Confocal Imaging of Geological Samples

Hassan

Yutkin

Chandra

et al. 2019

Day 4 Thu, March 21, 2019

View full text Add to dashboard Cite

In this paper, we present a procedure for high pressure resin impregnation of microporous rock. This procedure produces the highquality pore casts that reveal the fine details of the complex pore space of micritic carbonates. We carefully test our resin impregnation procedure and demonstrate that it renders the high resolution, 3D confocal images of pore casts. In our work, we use silicon micromodels as a reference to validate the key parameters of high-pressure resin impregnation. We demonstrate possible artifacts and defects that might develop during rock impregnation with resin, e.g., the resin shrinkage and gas trapping. The main outcome of this paper is a robust protocol for obtaining the high-quality epoxy pore casts suitable for rock imaging with Confocal Laser Scanning Microscopy (CLSM). We have implemented this protocol and provided the high resolution, three-dimensional (3D) imagery and description of microporosity in micritic carbonates.

show abstract

“…On the other hand, archaeologists use surface casts as an analytical tool to understand material cultures through experimentation to imitate ancient gestures and technologies [5]. In geoscience, pore identification from pore cast thin-section images is widely used to estimate porosity and evaluate the types of pores and pore structure parameters [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Quality Evaluation of Epoxy Pore Casts Using Silicon Micromodels: Application to Confocal Imaging of Carbonate Samples

et al. 2021

View full text Add to dashboard Cite

Pore casting refers to filling the void spaces of porous materials with an extraneous fluid, usually epoxy resin, to obtain a high-strength composite material, stabilize a fragile porous structure, produce a three-dimensional replica of the pore space, or provide imaging contrast. Epoxy pore casting may be accompanied by additional procedures, such as etching, in which the material matrix is dissolved, leaving a clean cast. Moreover, an epoxy resin may be mixed with fluorophore substances to allow fluorescence imaging. Our work aims to investigate and optimize the epoxy pore casting procedure parameters, for example, impregnation pressure. We use silicon micromodels as a reference to validate the key parameters of high-pressure resin impregnation. We demonstrate possible artifacts and defects that might develop during impregnation with resin, e.g., resin shrinkage and gas trapping. In the end, we developed an optimized protocol to produce high-quality resin pore casts for high-resolution 3D imaging and the description of microporosity in micritic carbonates. In our applications, the high-quality pore casts were acid-etched to remove the non-transparent carbonate material, making the pore casts suitable for imaging with Confocal Laser Scanning Microscopy (CLSM). In addition, we evaluate the quality of our etching procedure using micro-computed tomography (micro-CT) scans of the pre- and post-etched samples and demonstrate that the etched epoxy pore casts represent the pore space of microporous carbonate rock samples with high fidelity.

show abstract

Micropore network modelling from 2D confocal imagery: impact on reservoir quality and hydrocarbon recovery

Cited by 8 publications

References 64 publications

Upscaling the porosity–permeability relationship of a microporous carbonate for Darcy-scale flow with machine learning

Upscaling the porosity–permeability relationship of a microporous carbonate for Darcy-scale flow with machine learning

Validation of High Pressure Resin Impregnation Technique for High Resolution Confocal Imaging of Geological Samples

Quality Evaluation of Epoxy Pore Casts Using Silicon Micromodels: Application to Confocal Imaging of Carbonate Samples

Contact Info

Product

Resources

About