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Record W2040215333 · doi:10.2118/05-10-02

Sensitivity Study of Coalbed Methane Production With Reservoir and Geomechanic Coupling Simulation

2005· article· en· W2040215333 on OpenAlexaffabout
Fei Gu, Rick Chalaturnyk

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCoalbed methanePermeability (electromagnetism)MethaneCoalShrinkageCoal miningDewateringPetroleum engineeringEffective stressMaterials scienceEnvironmental scienceGeologyGeotechnical engineeringChemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Permeability of coal seams is one of the key factors for the success of coalbed methane (CBM) developments. It is dominated by cleat permeability in coal, which is very sensitive to the change of effective stresses. The coal matrix shrinkage due to methane production also influences cleat permeability. Using an explicit-coupling simulation method, which simultaneously simulates multiphase fluid flow and coal deformation, and a coupling permeability model, which considers the effects of the effective stress change and coal matrix shrinkage on cleat permeability, the sensitivity of CBM production to ten engineering, geologic, and coal intrinsic parameters such as cleat permeability, cleat spacing, well control area, depth, and methane content, etc., were studied in this paper. These parameters are stress and matrix shrinkage related parameters or have significantly influences on CBM production identified from previous studies. The production rate and final gas recovery from conventional simulations and coupling simulations are also compared. Of the parameters studied, permeability, cleat spacing, and in situ stresses were found to be the most sensitive parameters that influence CBM production. Medium sensitivity was found for the coefficient of matrix shrinkage, the Langmuir volume, pressure gradient, and well control area, while the least sensitive parameters included Poisson's ratio, Young's modulus, and the Langmuir pressure. Introduction With reserves of 84 ˜ 262 trillion m3 (2,980 ˜ 9,260 trillion ft3) all over the world(1), coalbed methane (CBM) has come to represent a real gas supply to meet current and future natural gas demands. In the United States, CBM accounted for 10% of dry gas reserves and 8% of dry gas production in 2003(2). The worldwide development of CBM is also accelerating in many other countries such as China, Canada, and Australia. The success of CBM developments depends on many factors, but specific properties of a coal seam remain the fundamental controlling factor. Many people have investigated the effects of coal seam properties on CBM production and recovery(3–6). The studies of Sawyer et al. indicated that cleat (fracture) permeability and relative permeability, not gas diffusion, control long-term productivity, and that optimum well spacing also depends on cleat permeability(3). Reid et al.'s results showed that permeability, initial desorption pressure, and drainage area are the most important reservoir parameters for CBM production(4). Young et al. pointed out that permeability, well spacing, and the degree of coal saturation have the greatest impacts on the long-term performance of CBM wells(5). A completed parametric study by Roadifer et al. illustrated that for coal-only reservoirs (without adjacent sand layers), the five parameters having the most impact on the peak gas rate are, in order of highest to lowest, permeability, free gas saturation, degree of saturation of the coal, damage skin factor, and thickness(6). The results of the above-mentioned studies clearly indicate that cleat permeability is likely the most important factor for CBM production. However, in all these investigations, permeability was considered as a constant.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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

Citations40
Published2005
Admission routes2
Has abstractyes

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