Characterizing Moisture and Gas Content of Coal by Low-Field NMR
Notice bibliographique
Résumé
Abstract Coalbed Methane (CBM) shows great potential to be an important energy source. One key factor for the successful development of CBM processes is to characterize coal on its moisture and gas content. Usually, the moisture and gas content of coal are determined from laboratory analysis. Low-field nuclear magnetic resonance (NMR) is a relatively new technique used in logging and in the analysis of fluids contained in reservoir rocks. This paper investigates the potential for coal characterization by low-field NMR. Low-field NMR detects hydrogen-bearing molecules and, in reservoir rock samples, distinguishes between ‘free’ bulk fluid and ‘bound’ surface fluid. Coal contains free water in the cleats as well as moisture that forms an integral part of the coal structure. Methane gas is a light hydrocarbon gas and coal contains free methane gas in fractures and adsorbed methane in internal surfaces. NMR characterization of moisture and adsorbed gas in coal and implications for moisture, adsorption isotherm and gas content measurements are explored. Experiments of moisture-free coal, moist coal and coal/water mixtures indicated drastically different spectra. From these spectra, free and bound water could be estimated using a methodology that is currently applied in clay-rich formations. In this paper, two sets of data are presented. First, measurements at ambient conditions provided a reference to other conventional moisture and cutoff data. Second, a high-pressure cell for the measurement of adsorbed coal was used and comparisons were made. Coal samples in the form of powder and chunk were used. The paper focuses on the methods and results to date. Introduction Coalbed methane (CBM) has evolved into a commercially profitable source of 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 assets are in Alberta(1). Coalbed methane has the potential of contributing a significant portion of Canadian natural gas production in the near future. One key factor for the successful development of CBM processes is to characterize coal on its moisture and gas content. Usually, the moisture of coal is determined from coal proximate analysis and the gas content of coal is determined by the desorption measurement in the laboratory. Low-field NMR is a relatively new technique used in logging and in the analysis of fluids contained in reservoir rocks. Nuclear magnetic resonance occurs when the nuclei of certain atoms (i.e. hydrogen proton) are immersed in a static magnetic field and exposed to a second oscillating magnetic field(2). NMR provides a non-destructive analytical method of detecting hydrocarbons in reservoirs(3) and characterizing the hydrocarbon gas(4). Generally, the NMR spectrum can provide three types of information: the quantities of the fluids in the rock, the mobility (viscosity) of these fluids and information about the pores that contain these fluids. As a source rock and at the same time a reservoir rock, coal contains large amounts of water and methane. Water exists as free water in the cleats as well as the moisture that forms an integral part of the coal structure.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».