Notice bibliographique
Résumé
This article, written by Assistant Technology Editor Karen Bybee, contains highlights of paper SPE 137006, ’A Systematic Workflow Process for Heavy Oil Characterization: Experimental Techniques and Challenges,’ by A.I. Memon, SPE, J. Gao, SPE, S.D. Taylor, SPE, T.L. Davies, SPE, and N. Jia, SPE, Schlumberger, originally prepared for the 2010 SPE Canadian Unconventional Resources and International Petroleum Conference, Calgary, 19-21 October. The paper has not been peer reviewed. The full-length paper summarizes a heavy-oil fluid-characterization technique that includes fluid-sample handling, pressure/volume/temperature (PVT) analysis, fluid viscosity, emulsion and rheology, and slow kinetics of gas evolution during a constant-com-position-expansion (CCE) experiment. The experimental methodologies, including the merits and experimental limitations for these measurements, are discussed. Introduction The increasing demand for energy has increased the interest in heavy oil and bitumen. One of the keys to meeting this increasing demand for heavy oil is a thorough characterization of the reservoir fluids. Heavy or viscous oils typically are defined as either heavy oil or bitumen. Heavy oils have a gravity between 22.3 and 10°API and a viscosity of 100 to 100 000 mPas. Bitumens are oils with a gravity less than 10°API and viscosity greater than 100 000 mPass. Fluid characterization of heavy oils and bitumen is required for several purposes, including oil-quality evaluation, selection and optimization of production processes, facilities planning, transport planning, and process monitoring. In the case of selecting and optimizing processes to extract heavy oil from a reservoir, fluid-characterization efforts normally are focused on understanding the mobility and changes to the mobility under different production conditions. Such understanding and the ability to manipulate mobility depend on knowledge of petroleum-fluid thermodynamics, chemistry, and transport phenomena. Heavy-oil production methods can be divided into four main categories: (1) cold-depletion production, (2) water-flood production, (3) thermal production, and (4) solvent-flood production processes. Cold-depletion production methods do not require any addition of heat and can be used when the viscosity of heavy oil at reservoir conditions is sufficiently low to allow the flow of oil to the surface. In some cases, cold-production processes will include diluent injections within the wellbore to decrease fluid viscosity. Waterflooding is a cold secondary-oil-recovery method to produce heavy oil with relatively low viscosity. This method has not been successful for moderate- to high-viscosity heavy oil because of several limitations including fingering of waterflood fronts, which may result in poor sweep efficiency. In typical thermal methods, steam is injected in one of several configurations such as huff ‘n’ puff or steamflooding using a multiwell process or steam-assisted gravity drainage. The solvent-flood production process includes the injection of vaporized solvents such as propane or carbon dioxide. Some processes under consideration may combine approaches from these four categories, such as steam/solvent-injection processes.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».