Subtopic Deep Dive
X-ray Computed Tomography in Pore-Scale Modeling
Research Guide
What is X-ray Computed Tomography in Pore-Scale Modeling?
X-ray Computed Tomography in Pore-Scale Modeling uses micro-CT imaging to reconstruct 3D porous media microstructures for simulating multiphase flow in enhanced oil recovery.
Micro-CT scans capture pore geometries at resolutions below 1 micron, enabling extraction of pore networks for flow simulations. These models predict displacement efficiency by validating against in-situ experiments. Over 50 papers since 2015 apply this to EOR, with Xiong et al. (2016) review cited 551 times.
Why It Matters
Pore-scale models from micro-CT improve EOR forecasts by linking microscopic multiphase flow to reservoir-scale recovery, as in Raeini et al. (2017) generalized network extraction (334 citations) reducing discretization errors. In oil shale pyrolysis, Saif et al. (2017) used X-ray micro-tomography to image microstructure changes (307 citations), aiding kerogen conversion predictions. Singh et al. (2017) visualized snap-off events (263 citations), quantifying capillary trapping for CO2 storage and foam EOR.
Key Research Challenges
Network Extraction Accuracy
Extracting representative pore networks from micro-CT images introduces uncertainties in topology and throat sizes. Raeini et al. (2017) developed generalized workflows to coarse-grain void space, yet oversimplification persists. Validation against experiments remains inconsistent across resolutions.
Multiphase Dynamics Simulation
Simulating snap-off, pore-filling, and ganglion dynamics requires coupling topology with physics. Singh et al. (2017) imaged these events in real-time, but models struggle with wettability variations. Armstrong et al. (2016) highlighted phase topology roles beyond Darcy's law (221 citations).
Scale-Up to Reservoir
Bridging pore-scale insights to Darcy-scale predictions faces upscaling challenges in reactive EOR. Hommel et al. (2018) reviewed porosity-permeability evolution (314 citations), noting geochemical alterations complicate transfers. Multi-scale imaging like Saif et al. (2017) helps but integration lags.
Essential Papers
Review of pore network modelling of porous media: Experimental characterisations, network constructions and applications to reactive transport
Qingrong Xiong, Todor G. Baychev, Andrey P. Jivkov · 2016 · Journal of Contaminant Hydrology · 551 citations
Pore network models have been applied widely for simulating a variety of different physical and chemical processes, including phase exchange, non-Newtonian displacement, non-Darcy flow, reactive tr...
Generalized network modeling: Network extraction as a coarse-scale discretization of the void space of porous media
Ali Q. Raeini, Branko Bijeljic, Martin J. Blunt · 2017 · Physical review. E · 334 citations
A generalized network extraction workflow is developed for parameterizing three-dimensional (3D) images of porous media. The aim of this workflow is to reduce the uncertainties in conventional netw...
Porosity–Permeability Relations for Evolving Pore Space: A Review with a Focus on (Bio-)geochemically Altered Porous Media
Johannes Hommel, Edward Coltman, Holger Class · 2018 · Transport in Porous Media · 314 citations
Reactive transport processes in a porous medium will often both cause changes to the pore structure, via precipitation and dissolution of biomass or minerals, and be affected by these changes, via ...
Multi-scale multi-dimensional microstructure imaging of oil shale pyrolysis using X-ray micro-tomography, automated ultra-high resolution SEM, MAPS Mineralogy and FIB-SEM
Tarik Saif, Qingyang Lin, Alan R. Butcher et al. · 2017 · Applied Energy · 307 citations
From connected pathway flow to ganglion dynamics
Maja Rücker, Steffen Berg, Ryan T. Armstrong et al. · 2015 · Geophysical Research Letters · 269 citations
Abstract During imbibition, initially connected oil is displaced until it is trapped as immobile clusters. While initial and final states have been well described before, here we image the dynamic ...
Dynamics of snap-off and pore-filling events during two-phase fluid flow in permeable media
Kamaljit Singh, Hannah Menke, Matthew Andrew et al. · 2017 · Scientific Reports · 263 citations
Fundamentals of foam transport in porous media
Anthony R. Kovscek, Clayton J. Radke · 1993 · 258 citations
Foam in porous media is a fascinating fluid both because of its unique microstructure and because its dramatic influence on the flow of gas and liquid. A wealth of information is now compiled in th...
Reading Guide
Foundational Papers
Start with Kovscek and Radke (1993) for foam transport basics in porous media; Raoof and Hassanizadeh (2009) for pore-network generation methods; Armstrong et al. (2013) for micro-CT capillary number validation.
Recent Advances
Raeini et al. (2017) for generalized network extraction; Singh et al. (2017) for snap-off dynamics; Saif et al. (2017) for multi-scale oil shale imaging.
Core Methods
Micro-CT imaging (Saif 2017), pore network modeling (Xiong 2016; Raeini 2017), ganglion dynamics tracking (Singh 2017; Rücker 2015), porosity-permeability relations (Hommel 2018).
How PapersFlow Helps You Research X-ray Computed Tomography in Pore-Scale Modeling
Discover & Search
Research Agent uses searchPapers with 'X-ray CT pore-scale EOR' to retrieve Xiong et al. (2016, 551 citations), then citationGraph maps 200+ citing works on network modeling, and findSimilarPapers links to Raeini et al. (2017) for extraction methods.
Analyze & Verify
Analysis Agent applies readPaperContent to parse Saif et al. (2017) micro-CT data, verifies ganglion dynamics claims via verifyResponse (CoVe) against Singh et al. (2017), and runs PythonAnalysis with NumPy to compute porosity-permeability correlations, graded by GRADE for statistical rigor.
Synthesize & Write
Synthesis Agent detects gaps in snap-off modeling between Rücker et al. (2015) and Singh et al. (2017), flags wettability contradictions; Writing Agent uses latexEditText for pore network diagrams, latexSyncCitations for 20+ refs, and latexCompile for EOR report export.
Use Cases
"Analyze porosity changes in oil shale pyrolysis from micro-CT data"
Research Agent → searchPapers('Saif 2017 oil shale') → Analysis Agent → readPaperContent + runPythonAnalysis (pandas plot porosity vs. pyrolysis temp) → matplotlib figure of evolution curves.
"Write LaTeX section on snap-off in pore-scale EOR with citations"
Synthesis Agent → gap detection (Singh 2017 vs. Rücker 2015) → Writing Agent → latexEditText('snap-off dynamics') → latexSyncCitations(10 refs) → latexCompile → PDF with ganglion diagrams.
"Find GitHub repos for micro-CT pore network extraction code"
Research Agent → searchPapers('Raeini 2017 network extraction') → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect → verified extraction scripts for imbibition sims.
Automated Workflows
Deep Research workflow scans 50+ papers via searchPapers on 'micro-CT EOR pore-scale', structures report with citationGraph clusters on foam/transport. DeepScan applies 7-step CoVe to verify Raeini et al. (2017) against experiments, checkpointing topology stats. Theorizer generates upscaling theory from Hommel et al. (2018) porosity relations.
Frequently Asked Questions
What defines X-ray CT in pore-scale modeling?
Micro-CT reconstructs 3D porous microstructures from X-ray scans for multiphase flow simulations in EOR, as in Saif et al. (2017).
What are key methods?
Pore network extraction (Raeini et al., 2017), dynamics imaging of snap-off/pore-filling (Singh et al., 2017), and multi-scale pyrolysis analysis (Saif et al., 2017).
What are foundational papers?
Kovscek and Radke (1993) on foam fundamentals (258 citations); Raoof and Hassanizadeh (2009) pore-network generation (178 citations); Armstrong et al. (2013) critical capillary number via micro-CT (172 citations).
What open problems exist?
Upscaling reactive changes (Hommel et al., 2018); accurate wettability in mixed systems (Alhammadi et al., 2017); real-time ganglion dynamics beyond lab scales (Rücker et al., 2015).
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Part of the Enhanced Oil Recovery Techniques Research Guide