Analysis of Coalbed Methane Production by Reservoir and Geomechanical Coupling Simulation
Notice bibliographique
Résumé
Abstract An overview of coalbed methane (CBM) reservoir characteristics, its unique production mechanisms, and the influence of geomechanical processes on these production mechanisms are discussed from a reservoir engineering point of view. Models have been developed to predict changes in cleat porosity and permeability with in situ conditions (stress, pressure, gas sorption, and temperature). Explicit-sequential coupling simulations are conducted for conventional CBM depletion production. The study shows that during production, coal matrix shrinkage due to methane desorption results in an increase of permeability within coal seams in most regions close to the producer even though the mean effective stresses increase. The predicted production rate and cumulative production from explicit-sequential coupling simulations are higher than that from conventional simulations. The developed models also allow coalbed permeability anisotropy to be considered. Introduction Worldwide coalbed methane (CBM) reserves have been estimated at 84 ~ 262 trillion m3 (2,980 ~ 9,260 trillion ft3)(1). The majority of these CBM reserves are mainly located in Russia (17 ~ 113 trillion m3), Canada (6 ~ 76 trillion m3), China (30 ~ 35 trillion M3), Australia (8 ~ 14 trillion m3), and USA (11 trillion m3)(1). In the United States, CBM accounted for 10% of dry gas reserves and 8% of dry gas production in 2003(2). In other countries, such as China, Canada, and Australia, CBM projects are attracting more and more attention by resource companies. The production methods of CBM include conventional pressure depletion production and enhanced coalbed methane (ECBM) recovery. At present, CBM is mainly recovered by the former method. In ECBM, gases such as N2, CO2, or flue gas are injected to displace methane and maintain coalbed pressure. This recovery method is still in its infancy with only two field-scale ECBM projects (one injected N2 and the other injected CO2)(3), and one singlewell pilot project(4) worldwide. Productivity evaluation and prediction are important steps in the development of CBM reservoirs. Because gas storage mechanisms in coal seams (mainly adsorbing on the walls of pores) are different from that in conventional gas reservoirs (compressed in pores), conventional reservoir simulators generally do a poor job in predicting CBM production. Over the past decade, many models have been developed to characterize CBM production processes (5–7). Commercial simulators for CBM production can be categorized into two types: modified conventional black oil simulators and modified compositional simulators. With the recognition of the stress dependency of coal permeability and porosity and shrinkage/swelling of the coal matrix due to desorption/adsorption, some simulators have been modified to accommodate these characteristics(3). However, in these simulators the influence of in situ stresses is simplified with an analytic model or a monotonic relation between the permeability ratio and pressure changes. Durucan et al. developed a finite element model to simulate the in situ stress changes near wellbores and coupled the stress changes with fluid flow simulation by characterizing dynamic changes in permeability(8).
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 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 | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 ».