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Record W2551855500 · doi:10.2118/183360-ms

Coal Bed Methane - Unconventional Gas Becomes an Optimised Solution

2016· article· en· W2551855500 on OpenAlexaboutno aff
Nick Amott, Paul Garlick, Paul Andrews, Steven van Wagensveld

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsUnconventional oilTight gasNatural gasCoalHydraulic fracturingPetroleum engineeringEnvironmental scienceProduction (economics)MethaneShale gasCoal miningCoalbed methaneWaste managementOil shaleEngineeringChemistryEconomics

Abstract

fetched live from OpenAlex

Abstract Tight gas resources as exemplified by the dramatic increase in production in the USA and being aggressively developed worldwide, have analogues with Coal Bed Methane (CBM). CBM (sometimes known as Coal Seam Gas) is an unconventional resource where gas is adsorbed within the solid matrix of a coal seam. CBM projects are characterised by large, complex and repetitive gathering system infrastructure which are similar to the general traits of tight gas production systems, especially those associated with fracturing well stimulation techniques. CBM has been utilised as a source of natural gas for decades, predominantly in the USA, Canada and Australia. However, the majority of the worlds’ 143 trillion cubic metres of Coal Bed Methane remain untapped. To date, the more favourable economics of conventional gas reserves extraction and the recent burgeoning shale gas resource in the USA has limited CBM exploitation. However, increasing demand for gas in eastern markets has led to renewed interest and investment in CBM development. This paper presents a "primer" for CBM covering the resources, extraction methods, gas characterisation and environmental considerations; including co-production of water and land management issues due to the thousands of wells required for a development. The collected gas is usually compressed, dehydrated and exported as sales gas, and provides an excellent feedstock for LNG production. Unlike conventional gas production, the gas and water production rates from an individual CBM well cannot be known until the well is completed (and in the case of tight gas, fractured), so CBM/tight gas projects present design engineers with technical challenges in terms of optimisation, equipment sizing and modularisation. The geographically distributed, capital intensive nature of CBM projects also places great focus on cost and scope optimisation by operating companies and design contractors. An introduction to CBM production and facilities design is presented in this paper, together with discussion of an innovative, patent pending method of modelling gas and water gathering system networks, using Monte Carlo analysis and a genetic algorithm for cost optimisation.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.021
GPT teacher head0.244
Teacher spread0.223 · 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 designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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