Nanoscale Gas Flow in Shale Gas Sediments
Notice bibliographique
Résumé
Abstract Production of gas out of low permeability shale packages is very recent in the Western Canadian Sedimentary Basin (WCSB). The process of gas release and production from shale gas sediments is not well understood. Because of adsorptive capacity of certain shale constituents, including organic carbon content, coalbed methane models are sometimes being applied to model and simulate tight shale gas production behaviour. Alternatively, conventional Darcy flow models are sometimes applied to tight shale gas. However, neither of these approaches takes into account the differences in transport mechanisms in shale due to additional nanopore networks. Hence, the application of existing models for shale results in erroneous evaluation and predictions. Our analysis shows that a combination of a nanopore network connected to a micrometre pore network controls the gas flow in shale. Mathematical modelling of gas flow in nanopores is difficult since the standard assumption of no-slip boundary conditions in the Navier-Stokes equation breaks down at the nanometre scale, while the computational times of applicable molecular-dynamics (MD) codes become exorbitant. We found that the gas flow in nanopores of the shale can be modeled with a diffusive transport regime with a constant diffusion coefficient and negligible viscous effects. The obtained diffusion coefficient is consistent with the Knudsen diffusivity which supports the slip boundary condition at the nanopore surfaces. This model can be used for shale gas evaluation and production optimization. Introduction Shale gas is a type of reservoir classified under the Unconventional Gas heading. These ‘difficult to produce’ reservoirs will play an increasingly important role in Canadian gas production because they are showing the potential to offset declining conventional gas production. Quite simply, shale gas is natural gas produced from shale sequences. Gas shales are predominantly lithified clays with organic material and detrital minerals present in varying amounts. Organic matter is an integral constituent of a productive shale gas reservoir. In addition, these fine-grained rocks are microporous, causing low permeabilities. While shale gas production has had a long history in the United States, dating back about 80 years, it is still at the very early stages of commercial production in Canada. Very little public data exists on shale gas production, yet industry interest is on the rise. A variety of estimates indicate that between 550 and 860 trillion cubic feet of gas-in-place could exist in potential shale gas formations in Western Canada(1,2). But shales can be difficult to evaluate using conventional laboratory techniques. Much of this has to do with resident clays that can have bound water either as part of their matrix or loosely bound in the interlayers in amounts of 75 to 80%. Another challenge can be the accurate measurement of in situ permeabilities, which are on the nanoscale. Core samples have often been subjected to coring induced or stress release fractures, resulting in greatly overstated permeability measurements. While some shales in Western Canada are, at this early stage, showing the proper geochemical and reservoir properties to support gas production, new techniques need to be developed to more accurately understand shale properties and their productive potential.
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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 ».