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Record W1998280188 · doi:10.2118/2006-027

Coalbed Characterization Studies With X-Ray Computerized Tomography (CT) and Micro CT Techniques

2006· article· en· W1998280188 on OpenAlexafffundabout
F. Saites, Geoff Wang, Rong Guo, Karin Mannhardt, Apostolos Kantzas

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsTomographyCharacterization (materials science)X-rayComputed tomographyNuclear medicineMedical physicsComputer scienceMaterials scienceRadiologyMedicineOpticsPhysicsNanotechnology

Abstract

fetched live from OpenAlex

Abstract Much of the focus of CBM reservoir assessment in Canada is based on understanding cleats and natural fractures, both in outcrop and in core taken from well bores. Coal characterization studies using imaging devices are presented. X-ray computerized tomography (CT) and micro CT are used on coal samples to provide a better understanding of fracture morphology and apertures. Images are collected in samples of approximately 10 cm in diameter with resolution of (0.4 mm)2 using X-ray CT, and samples of 1 cm in diameter with resolution of (5_m)2 using micro CT. Visualization at both resolutions allows for discussion and comparison of structural characteristics at both scales. X-ray CT images are processed to obtain density "logs" and maps under different overburden pressures. The pore space contained helium, and, in one set of scans, argon. Bulk densities increase with increasing overburden pressure. Fracture patterns in both cores were reconstructed from the images using a fracture identification algorithm. Manipulation of the density maps can also provide local strains as a function of increasing overburden pressure. Introduction The width of the natural fractures (cleats) and the corresponding permeability is typically a strong function of the net stress in the coalbed. Understanding stress-dependent permeability is essential as declining permeability during coalbed depletion would be detrimental to the productivity of coalbed wells. By using imaging techniques, the distribution of density, fractures and volumetric strain fraction can be defined and used to understand flow characteristics of gas in coals. Computerized tomography (CT) scanning is a non-destructive laboratory technique which can provide two and three dimensional image reconstruction of opaque objects using X-rays. It is relatively easy to apply, can offer fine spatial resolution, and is adaptable to many types of experimental procedures. Micro CT is also a non-destructive technique enabling virtual slicing of opaque objects. Stacking several slices enables 3D visualization of the object. The final images show the differences in the linear attenuation coefficient of X-rays. This linear attenuation coefficient depends on the density and the atomic number of the object. Consequently, components that differ in these parameters can be distinguished. EXPERIMENTAL SAMPLE PREPARATION Two coal cores, Sample 1 and Sample 2, both from the Manville Group in Alberta, were used in this work. Figure 1 is a photograph of Sample 1 (Figure 1 A) and Sample 2 (Figure 1 B). Table 1 presents the depth of burial, the coal rank and the formation from where the coal samples were taken. Because coal is a friable material, the coal core was not a smooth cylinder as in the case of a reservoir rock. In order to mount the coal core in a rubber sleeve inside a core holder for permeability measurements, it had to be made cylindrical first. This was accomplished by casting the core in urethane in a cylindrical mold. The urethane is solid enough to keep the core intact and confine it in a core holder, but retains enough flexibility to transmit overburden pressure to the coal.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.595
Threshold uncertainty score0.709

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)0.0000.000

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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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 designBench or experimental
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

Citations8
Published2006
Admission routes3
Has abstractyes

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