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Enregistrement W4233789682 · doi:10.2523/84198-ms

A Stochastic Approach to Extract Vapex Related Dispersion Coefficients from Magnetic Resonance Images

2003· article· en· W4233789682 sur OpenAlexaffabout
Karmaker Kulada, Brij Maini

Notice bibliographique

RevueProceedings of SPE Annual Technical Conference and Exhibition · 2003
Typearticle
Langueen
DomaineChemistry
ThématiquePetroleum Processing and Analysis
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésCitationExhibitionComputer scienceInformation retrievalWorld Wide WebArt historyArt

Résumé

récupéré en direct d'OpenAlex

A Stochastic Approach to Extract Vapex Related Dispersion Coefficients from Magnetic Resonance Images Kulada Karmaker; Kulada Karmaker University of Calgary Search for other works by this author on: This Site Google Scholar Brij B. Maini Brij B. Maini University of Calgary Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. Paper Number: SPE-84198-MS https://doi.org/10.2118/84198-MS Published: October 05 2003 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Karmaker, Kulada, and Brij B. Maini. "A Stochastic Approach to Extract Vapex Related Dispersion Coefficients from Magnetic Resonance Images." Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. doi: https://doi.org/10.2118/84198-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search SummaryIn vapor extraction (Vapex) process, the dispersional mixing between injected solvent vapor (propane) and in-situ bitumen occurs along the oil-solvent interface during the oil drainage process. The solvent dispersion coefficient is a key parameter that governs the oil dilution efficiency as well as the rate of production. To predict the field performance of the Vapex process it is vital to accurately estimate the value of the dispersion coefficient at the field conditions of interest. Currently, there are no factual data available in the literature and there is no proven empirical methodology for estimating the dispersion coefficients that would be pertinent to the Vapex process.Recently, the Magnetic Resonance Imaging (MRI) tools have been used to gain insights into the Vapex process. The MRI technique can generate 2-dimensional (2-D) images during the progress of a laboratory-scale Vapex experiment. Both the original bitumen and the solvent vapor are virtually invisible in these MRI generated 2-D images. However, the propane saturated bitumen is clearly visible and in the diluted oil zone, the signal intensity is a function of the dissolved solvent concentration.This paper describes a new technique to extract the net dispersion coefficients pertinent to the Vapex process from 2-D MRI images captured during a test. A new mathematical model has been developed for analyzing such 2-D images. The model portrays the unique context of mass transfer mechanisms and the interface propagation in the Vapex process. The technique has been used on a previously published MRI image and found to be very effective and straightforward.IntroductionThe Vapex process can be described as a solvent analogue of the steam assisted gravity drainage (SAGD) process1, which involves a drastic reduction of oil viscosity by diluting the in-situ bitumen with vaporized hydrocarbon solvents (HCS). The process uses a horizontal well pair with the production well located near the bottom of the pay zone and the injection well completed right above the production well as schematically shown in Figure-1. The injected solvent vapor, such as propane, dissolves into the bitumen and reduces its viscosity to a low value so that the diluted bitumen can drain down, under the gravity force, into the production well. The pore space around the injection well in the swept zone remains filled with solvent vapor and is called "vapor chamber". The diluted oil flow occurs in a thin film (drainage edge) adjoining the interface as illustrated in Figure-1. In the progress of oil drainage, the vapor chamber grows and the vapor-bitumen interface moves laterally at a certain velocity depending on the rate of interfacial mass transfer of solvent into bitumen.There are several mechanisms that enhance the interfacial mass transfer process in porous media, as explained by Das and Butler2. However, the mixing of solvent and bitumen occurs mainly by a combined mechanism of molecular diffusion and convective dispersion that takes place mostly within the drainage edge. While history matching the results of Vapex experiments using reservoir simulation software, Nghiem3 has shown that the convective dispersion is more important than the molecular diffusion in the mixing process. For a satisfactory history match, he needed transverse dispersion coefficients that were at least an order of magnitude larger than the molecular diffusion coefficient. Keywords: experiment, equation, enhanced recovery, dispersion coefficient, successive image, concentration, signal strength profile, propane, interface velocity, upstream oil & gas Subjects: Improved and Enhanced Recovery This content is only available via PDF. 2003. Society of Petroleum Engineers You can access this article if you purchase or spend a download.

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,121
Score d'incertitude au seuil0,774

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,012
Tête enseignante GPT0,234
Écart entre enseignants0,222 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
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

Citations0
Publié2003
Routes d'admission2
Résumé présentoui

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Même revueProceedings of SPE Annual Technical Conference and ExhibitionMême sujetPetroleum Processing and AnalysisTravaux en français237 207