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Enregistrement W2039688407 · doi:10.2118/04-06-01

A Fully Implicit Single Phase T-H-M Fracture Model for Modelling Hydraulic Fracturing in Oil Sands

2004· article· en· W2039688407 sur OpenAlexafffund
Ali Pak, Dave Chan

Notice bibliographique

RevueJournal of Canadian Petroleum Technology · 2004
Typearticle
Langueen
DomaineEngineering
ThématiqueHydraulic Fracturing and Reservoir Analysis
Établissements canadiensUniversity of Alberta
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaShell Canada
Mots-clésHydraulic fracturingFracture (geology)MechanicsGeologyPetroleum engineeringGeotechnical engineeringPlane stressFlow (mathematics)Finite element methodStress (linguistics)Fluid dynamicsGeomechanicsEngineeringStructural engineering

Résumé

récupéré en direct d'OpenAlex

Abstract Enhancing oil extraction from oil sands with a hydraulic fracturing technique has been widely used in practice. Due to the complexity of the actual process, modelling of hydraulic fracturing is far behind its application. Reproducing the effects of high pore pressure and high temperature, combined with complex stress changes in the oil sand reservoir, requires a comprehensive numerical model which is capable of simulating the fracturing phenomenon. To capture all of these aspects in the problem, three partial differential equations, i.e., equilibrium, flow, and heat transfer, should be solved simultaneously in a fully implicit (coupled) manner. A fully coupled thermo-hydro-mechanical fracture finite element model is developed to incorporate all of the above features. The model is capable of analyzing hydraulic fracture problems in axisymmetric or plane strain conditions with any desired boundary conditions, e.g., constant rate of fluid injection, pressure, temperature, and fluid flow/thermal flux. Fractures can be initiated either by excessive tensile stress or shear stress. The fracture process is simulated using a node-splitting technique. Once a fracture is formed, special fracture elements are introduced to provide in-plane transmissivity of fluid. Effectiveness of the model is evaluated by solving several examples and comparing the numerical results with analytical solutions. The model is also used to simulate large-scale laboratory hydraulic fracturing experiments. Introduction Hydraulic fracturing technique has been a fast growing technology since its first application in 1947. By 1988, more than one million hydraulic fracturing treatments had been performed(1), and today this technique is one of the most important methods in enhancing oil extraction from wells. Hydraulic fracturing in oil sand reservoirs plays an even more important role. Due to low temperature and low permeability of oil sand deposits and high viscosity of bitumen, oil is virtually immobile(2). Hence, any attempt for in situ oil extraction should employ one of the following techniques: cyclic steam stimulation, in situ combustion, or hydraulic fracturing. Despite the fact that hydraulic fracturing technology has advanced significantly over the past fifty years, our ability to model the process has not changed as rapidly. As a matter of fact, this technique has been so successful that in the past, designingthe treatment with a high degree of precision was not of any interest. But as the industry moved towards applications of very high volume/rate, and highly engineered and sophisticated hydraulic fracturing treatments, the demand for more rigorous designs in order to optimize the procedure have become more important. On the other hand, without a thorough understanding of the physical process and the factors that are involved, our ability for an optimal design is limited. Modelling fluid flow combined with heat transfer in the reservoir has been used by the industry for a long time, and the fracturing process was often designed based on twodimensional closed-form solutions, such as Geertsma-de Klerk(3), or GdK in brief, and Perkins-Kern(4) and Nordgren(5), or PKN. Most of the flow and heat transfer models are based on the finite difference method, and effects of stresses and deformations in the ground, if not totally ignored, are solved in a decoupled or partially coupled manner with other elements.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,452
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0050,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,011
Tête enseignante GPT0,221
Écart entre enseignants0,210 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations16
Publié2004
Routes d'admission2
Résumé présentoui

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