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Enregistrement W2915140887 · doi:10.2118/0312-0090-jpt

Technology Focus: Heavy Oil (March 2012)

2012· article· en· W2915140887 sur OpenAlexaboutno aff
Cam Matthews

Notice bibliographique

RevueJournal of Petroleum Technology · 2012
Typearticle
Langueen
DomaineChemistry
ThématiquePetroleum Processing and Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOil reservesPetroleum industryPetroleumProcess (computing)Enhanced oil recoveryPetroleum engineeringVariety (cybernetics)Work (physics)Quality (philosophy)Computer scienceEngineering managementEnvironmental scienceConstruction engineeringBiochemical engineeringEngineeringMechanical engineeringEnvironmental engineeringChemistryArtificial intelligence

Résumé

récupéré en direct d'OpenAlex

Technology Focus The rapid growth in interest and in development activities related to unconventional oil and gas resources, including heavy oil, is clearly evident throughout the industry. One outcome has been a tremendous increase in the number of SPE papers written this past year on various topics associated with development and recovery optimization of heavy-oil reservoirs. Another is that petroleum-engineering departments at many more universities worldwide are actively engaged in teaching courses and performing research related directly to viscous- and/or heavy-oil recovery. One topic in particular has gained more attention: development and application of enhanced thermal-recovery methods that use various solvents as a means to improve recovery and to reduce operating costs significantly relative to conventional thermal projects. Continued knowledge development in this subject area through a combination of reservoir-scale physics and chemical-process analysis, simulation-capability advancement, laboratory testing, and field-piloting work is needed to enable operators to design and implement these methods effectively and commercially, especially for viable development of thinner, lower-quality heavy-oil reservoirs. One synopsis paper and a reading paper were selected to provide further insights regarding the potential and the challenges associated with the use of solvent-recovery techniques in such applications. The other papers were chosen to illustrate the variety and significance of the challenges operators may encounter in assessing and/or pursuing the development of heavy-oil reservoirs under different settings and conditions. These include various problems that had to be dealt with during testing of a heavy-oil well in an offshore location; the many planning issues, design tradeoffs, and performance considerations associated with the sequencing and conversion of a heavy-oil-field development from cold to hot production; the difficulties experienced in planning and conducting pilot operations in a high-viscosity oil field overlain by thick permafrost in the Russian Arctic; and the ability to achieve adequate recovery with steaming of fractured carbonate reservoirs. Recent literature also describes several interesting technology developments, modeling studies, and field-trial activities related to the use of in-situ combustion and electrical-heating methods as alternative heavy-oil-recovery techniques. Several additional papers present results from investigations of CO2 injection into heavy-oil or bitumen reservoirs to achieve both improved oil recovery and greenhouse-gas sequestration, while many others describe new developments and/or field experiences involving waterflooding and polymer flooding of heavy-oil reservoirs. The many papers written on these topics can be sourced through OnePetro. Recommended additional reading at OnePetro: www.onepetro.org. SPE 146671 A Semianalytical Approach for Estimating Optimal Solvent Use in a Solvent-Aided SAGD Process by Subodh Gupta, SPE, Cenovus Energy, et al. SPE 149917 Approaches to Development of High-Viscosity Oil Fields in Arctic Conditions by Example of the Russkoe Field by I. Edelman, OJSC TNK-BP Management, et al. SPE 150692 A New Perspective on Tracking Energy Transfer in SAGD Derived From 3D Numerical Field-Scale Simulations by C.C Ezeuko, SPE, University of Calgary, et al. SPE 144524 Recovery Mechanism of Steam Injection in a Carbonate Reservoir by Guo-Qing Tang, SPE, Chevron, et al. SPE 148966 Analysis of the First CHOPS Pilot for Heavy-Oil Production in Kuwait by Tirtharenu Sanyal, Kuwait Oil Company, et al. SPE 148917 Effect of Methane Coinjection in SAGD—Analytical and Simulation Study by Jyotsna Sharma, SPE, University of Calgary, et al.

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)0,001
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: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,766
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,009
Tête enseignante GPT0,253
Écart entre enseignants0,244 · 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'étudeSans objet
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
Publié2012
Routes d'admission1
Résumé présentoui

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