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Record W1981762096 · doi:10.2118/2007-179

Effects of Porosity and Material Fluctuations on Gas Transport and Sorption Kinetics in Coalbeds

2007· article· en· W1981762096 on OpenAlexafffund
Ebrahim Fathi, I. Yücel Akkutlu, L.B. Cunha

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSorptionPorosityKineticsMaterials scienceEnvironmental scienceComposite materialChemistryAdsorptionPhysics

Abstract

fetched live from OpenAlex

Abstract Natural gas transport and storage in coal is important for accurate predictions of production rates from coalbeds. Recent investigations based on nondestructive imaging have shown that coals are complex materials exhibiting nonuniform pore structure. Standard approach to describe gas/coal interactions is deterministic and neglects the effects of local spatial heterogeneities in material content and porosity. In this work, adopting a weak-noise approximation, these heterogeneity effects on diffusive gas transport are investigated using a statistical approach in the presence of non-equilibrium (kinetic) gas sorption with random partition coefficient. It is found that the gas-coal system behaves distinctively in the presence of kinetics and that the coal matrix heterogeneities generate multiplicative non-trivial effects on transport. Consequently, average gas concentration field is significantly different than the one obtained using an equivalent yet purely deterministic (i.e., homogeneous) approach. Using fractional gas recovery curves, the results are shown for coals exhibiting Gaussian porosity distributions with varying correlation length scales. A new upscaled deterministic gas mass balance is proposed. The work is a unique approach for understanding coalbed environment and development of sound numerical gas production/storage models. Introduction Coal is a mixture of various minerals and organic material exhibiting an intricate pore network. Variations in its material properties (e.g., rank and maceral content) add to its structurally complex nature and influence its gas retention (sorption) capacity. Much work has been carried out in understanding the pore structure of coal. Characteristically, coalbeds are dual-porosity environments with a network of fractures imbedded within a porous matrix. The coal matrix often exhibits a multi-scale heterogeneity with pores varying in size from micrometer (macro- and mesopores) to angstrom (micro- and submicropores). The gas sorption capacity of coal tends to increase with the volume of small-scale pores displaying significantly large specific surface area. Coal matrices have traditionally been considered as a porous material with a network of interconnected macropores, (Bond, 1956, Bhatia, 1987). According to this viewpoint, natural gas migration in and production from coalbeds have similarities to production from conventional naturally fractured reservoirs. This viewpoint, however, has been disputed by Larsen and Wernett (1988) suggesting that the macropores may not necessarily be connected; therefore, the gas molecules are anticipated to reach the macropores and fractures only by diffusive transport through the microporous solid material. Walker and Mahajan (1993) and Siemons et al. (2007) provided further experimental evidence of diffusive gas transport in the coal micropores. Efforts also have been put forth to identify relationships between the matrix pore structure and its material content. Although these investigations have generally been qualitative, it is shown that the coal porosity is somewhat related to its material properties (White et al. 2005). Typically, porosity has a tendency to decrease as the coal rank, i.e., thermal maturity, increases from lignite to bituminous and anthracite. Gan et al. (1972) observed that porosity is primarily dominated by the macropores in lower rank coals. The influence of maceral, i.e., organic, composition on porosity has also been considered by several groups.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score1.000

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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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 designObservational
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

Citations1
Published2007
Admission routes2
Has abstractyes

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