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Record W2268067398 · doi:10.2118/09-07-40

Assessing the Water Uptake of Alberta Coal and the Impact of CO2 Injection with Low-Field NMR

2009· article· en· W2268067398 on OpenAlexafffundabout
Rong Guo, Apostolos Kantzas

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCoalbed methaneCoalDewateringWettingMoistureMethaneCoal miningBound waterWater contentPetroleum engineeringChemistryMaterials scienceGeologyGeotechnical engineeringComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Coal property characterization is an essential step to develop coalbed methane (CBM) recovery processes. In most cases, coal contains free water in the cleats (except dry coal), as well as moisture that forms an integral part of the coal structure. Most CBM production starts with dewatering coalbeds to initialize the gas recovery. Therefore, the wetting behaviour of coal by water is an important aspect in coal property studies. As CO2 has a strong affinity to coal, CO2 injection may change the coal wetting behaviour during a so-called enhanced coalbed methane process (ECBM). Studies on coal wettability are rare. This paper investigates the water uptake by Alberta coal and its wettability alteration due to CO2 injection using low-field nuclear magnetic resonance (NMR). Low-field NMR is a technique used in logging and in the analysis of fluids contained in reservoir rocks. It measures the hydrogen density in reservoir fluids and distinguishes between 'free' bulk water and 'bound' surface water. CO2 is invisible to NMR, but its impact can be detected by changes in the water signal. Experiments on coal samples in the form of dry and moist powder and chunk are used. The water uptake rate can be shown by monitoring the geometrical mean transverse relaxation time. From the spectra of different coal samples, water can be characterized into free, capillary-bound and subsurface-bound (adsorbed) water. These forms of water have different uptake behaviour inside coal. The injection of CO2 will cause coal dewatering, and the effect will increase with elevated CO2 pressure. Introduction Coalbed methane (CBM) has evolved into a commercially profitable source of unconventional natural gas. Canada has vast resources of coal and it has been estimated that the total in-place reserves are 36 ? 1012 m3. Over 60% of Canada's CBM resource is in Alberta(1). Coalbed methane has the potential of contributing a significant portion of Canadian natural gas production in the foreseen future. Using CO2 to enhance methane recovery has been discussed by several researchers(2, 3). This process is called CO2-ECBM. If successful, its implications include CO2 sequestration in deep unmineable coalbeds. Coal property characterization is an essential step to develop CBM/ECBM recovery processes. In most cases, coal is wet and contains free water in the cleats, as well as moisture that forms an integral part of the coal structure. Some coals found in the Western Canadian Sedimentary Basin's Horseshoe Canyon Formation are dry coals, which means the coal cleats no longer preserve free water. Most CBM production starts with dewatering coalbeds to initialize gas recovery. Therefore, the wetting behaviour of coal by water is an important aspect in coal property studies. As CO2 has a strong affinity to coal, CO2 injection may change the coal wetting behaviour in the ECBM process. Published studies on coal wettability are very rare to the best of our knowledge. Low-field NMR is a relatively new technique used in logging and in the analysis of fluids contained in reservoir rocks.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.976

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 head0.004
GPT teacher head0.277
Teacher spread0.273 · 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 designTheoretical or conceptual
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

Citations29
Published2009
Admission routes3
Has abstractyes

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