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Record W2026231779 · doi:10.2118/2005-077

Visualization Studies on Matrix- Fracture Transfer Due to Diffusion

2005· article· en· W2026231779 on OpenAlexafffund
Can Hatiboglu, Tayfun Babadagli

Bibliographic record

VenueCanadian International Petroleum Conference · 2005
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVisualizationComputer scienceFracture (geology)DiffusionMatrix (chemical analysis)Materials scienceArtificial intelligenceThermodynamicsComposite materialPhysics

Abstract

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Abstract Co-and counter-current type transfers due to diffusion between matrix and fracture were studied experimentally using a 2-D model, and visualization equipment. The model is made of glass-beads or filter paper. Mineral oil and kerosene were used as the displaced phase. The 2-D model saturated with oil was exposed to pentane diffusion under static conditions to mimic matrix-fracture interaction during gas or liquid solvent injection in naturally fractured reservoirs. Displacement fronts and patterns were analyzed and quantified using fractal techniques to obtain correlations between the fractal properties and displacement type. A few capillary imbibition experiments showing an invasion percolation (IP) type displacement were conducted first. Diffusion type displacement resulted in diffusion limited aggregation (DLA) type patterns. Conditions yielding different types of displacement patterns were identified. The visual observations of the experiments will help develop stochastic displacement simulations using IP and DLA algorithms. Introduction Investigations on the displacement behavior under static conditions are important in different engineering practices such as exploitation of oil and gas, CO2 sequestration and groundwater contamination in naturally fractured reservoirs. Studies in this area resulted in different displacement patterns varying from piston like to fingering [1]. Quasi static displacement dominated by the capillary forces yields a process called invasion percolation (IP) [2–3]. Diffusion limited aggregation (DLA) patterns are obtained during diffusion and viscous forces dominated processes [4]. Wardlaw et al. [5] conducted experiments using glass cells and showed the importance of local differences in surface roughness on the imbibition of water and brine displacing oil. Indelman and Katz [6] analyzed the mechanics of countercurrent imbibition on 2-D models numerically and observed that the amount of imbibed liquid is a linear function of square root of time. They investigated the local heterogeneity on the process as well. Tidwell et al. [7] studied matrix imbibition during flow in the fracture. They modeled the matrix imbibition as a linear function of square root of time as suggested by Indelman and Katz and observed that this approach is applicable only to homogeneous matrix. They further investigated the heterogeneity effects using x-ray imaging and visualized the imbibition of water into a slab of volcanic tuff. They observed that the trend shows a linear behavior although the hydraulic conductivity of tuff varies by over four orders of magnitude. Medina et al. [8] conducted experiments on filter papers with different shapes and showed that in rectangular strips the dynamics of the capillary imbibition yields the classical Washburn law. Modifications to IP and DLA algorithms were developed to better simulate the flow in pore level. Biroljev et al. [9] took the gravity effect into account, developed the computer model and tested the model experimentally. They concluded that the front between the fluids is found to scale with the dimensionless Bond number (ratio between gravitational and capillary forces), and the fractal dimension of the profile is found to be De ≈1.34. Yortsos and Xu [10] and Xu et al. [11] developed a phase diagram of fully developed drainage in porous media using invasion percolation simulation runs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.262
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2005
Admission routes2
Has abstractyes

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