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Enregistrement W4239993723 · doi:10.2523/94986-ms

Recent In-Situ Oil Recovery-Technologies for Heavy- and Extraheavy-Oil Reserves

2005· article· en· W4239993723 sur OpenAlexaffabout
Luciane Cunha, J.C. Cunha

Notice bibliographique

RevueProceedings of SPE Latin American and Caribbean Petroleum Engineering Conference · 2005
Typearticle
Langueen
DomaineEngineering
ThématiqueEnhanced Oil Recovery Techniques
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésCitationPetroleumLatin AmericansLibrary scienceComputer scienceEnvironmental scienceGeologyPolitical science

Résumé

récupéré en direct d'OpenAlex

Recent In-Situ Oil Recovery-Technologies for Heavy- and Extraheavy-Oil Reserves Luciane Bonet Cunha; Luciane Bonet Cunha U. of Alberta Search for other works by this author on: This Site Google Scholar Jose C.S. Cunha Jose C.S. Cunha U. of Alberta Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Rio de Janeiro, Brazil, June 2005. Paper Number: SPE-94986-MS https://doi.org/10.2118/94986-MS Published: June 20 2005 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Cunha, Luciane Bonet, and Jose C.S. Cunha. "Recent In-Situ Oil Recovery-Technologies for Heavy- and Extraheavy-Oil Reserves." Paper presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Rio de Janeiro, Brazil, June 2005. doi: https://doi.org/10.2118/94986-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Latin America and Caribbean Petroleum Engineering Conference Search Advanced Search ProposalThe heavy and extra heavy oils in Canada represent an amount of recovery oil resources of about 300 billion barrels. These vast quantities of heavy and extra heavy oil are trapped in shallow, accessible reservoirs, but are difficult to extract. Producers involved in heavy oil recovery face special challenges in producing these high-viscosity crudes.Conventional heavy oil recovery methods have showed to provide limited oil displacement efficiencies in Canada's heavy and extra heavy oil deposits. To overcome their inherent difficulties several variations of steam, air and solvent injection methods have been proposed. The most interesting ones appeared along with developments in horizontal well technology. These methods combine the concept of oil gravity drainage with the conventional air-steam and solvent-based heavy oil recovery processes and the horizontal well technology. Methods known as cyclic steam stimulation - CSS, steam-assisted gravity drainage - SAGD, solvent vapor extraction - VAPEX, and top-dow combustion, are examples of this class of methods.This article presents an overview of the recent production technologies for extra heavy oil reserves. Some of the properties of heavy oil are summarized and a review of the drilling/completion and production techniques that help to make heavy-oil reservoirs profitable assets is presented. Both, limitations and potential benefits of these techniques are described.IntroductionAlberta's Oil Sands contain the largest crude bitumen resource in the world, approximately 1,628 billion barrels of initial in-place and 174 billion barrels of remaining established reserves. Over 80% of these reserves can be produced only by using in-situ recovery methods and research to find more effective in-situ recovery methods is incetivated.Majority of oil sands in Canada are deposited in Alberta, in three areas called Athabasca, Cold Lake, and Peace River. In-situ steam-based recovery methods have been required to produce bitumen more effectively and economically, each adapted to the specific geologic conditions of the reservoirs.This work includes a literature review on the recent in-situ oil recovery-technologies for heavy and extraheavy-oil reserves. Oil sands development and production statistics data for the three main oil sands areas will be covered. Also, the typical in-situ recovery methods, drilling/completion and production techniques which are applied in Canadian oil sands areas will be reviewed.Alberta Oil Sands Geology, Reserves and ProductionThe Alberta oil sands geology has been described in several reports(1),(2). The Alberta oil sands deposits are mostly contained in the Lower Cretaceous sands and are located in three geographic areas: Athabasca, Cold Lake, and Peace River(3) (Figure 1). Table 1 shows geological features in Canadian oil sands areas(2).Canada has 179 billion barrels of proved reserves, the second largest proved reserves in the world, as of the end of 2003 (Table 2)(4). Alberta oil sands has 1.6 trillion barrels of initial volume in-place of crude bitumen and reserves of about 174 billion barrels. It is estimated that eighty-one percent of the 174 billion barrels of remaining established reserves can be recovered by applying in-situ recovery methods (Table 3)(5). Oil sands production in Alberta is about 900 thousand barrels per day from the Athabasca, Cold Lake and Peace River deposits at the end of 2003 (Figure 2)(5),(6),(7). Figure 3 shows the locations of the significant oil sands projects including the mining projects. From the total production, about 60% is from surface mining, only in the Athabasca area, and the other 40% from in-situ recovery methods which consist of thermal recovery (27%) and primary production (13%). Keywords: in-situ recovery method, operation, enhanced recovery, heavy oil, oil sand, upstream oil & gas, cold lake, bitumen, drainage, recovery method Subjects: Improved and Enhanced Recovery, Unconventional and Complex Reservoirs, Oil sand, oil shale, bitumen This content is only available via PDF. 2005. Society of Petroleum Engineers You can access this article if you purchase or spend a download.

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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: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,872
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
Bibliométrie0,0000,000
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 Opus0,010
Tête enseignante GPT0,215
Écart entre enseignants0,206 · 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'étudeAutre devis
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

Citations2
Publié2005
Routes d'admission2
Résumé présentoui

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