Geological Controls on the Origin of Heavy Oil and Oil Sands and Their Impacts on In Situ Recovery
Notice bibliographique
Résumé
Abstract Biodegradation of crude oil in subsurface petroleum reservoirs is an important alteration process affecting most of the world's oil deposits. The process preferentially removes light components from conventional oil to form heavy oil and oil sand, which are more difficult to produce and are more costly to refine. Although reservoir temperature is a key control on biodegradation, large variations in oil properties have been documented in accumulations from similar depths within a play area. Data from the Liaohe Basin, NE China and other basins in China and elsewhere, indicate that biodegradation is most active in a narrow zone at or near the base of the oil column in contact with the water leg. The availability of nutrients from mineral dissolution within the water leg is also thought to have a significant impact upon the degree of biodegradation. Thus, the level of biodegradation increases with water leg thickness. Charge history and in-reservoir mixing of continuously charged oil with residual biodegraded oil also have a significant impact on oil physical properties. The conceptual biodegradation model proposed combines geochemical and geological factors to provide a coherent approach to estimate the impact of degradation on petroleum and to help reliably predict biodegradation risk at the prospect level. Our geochemical approach can be used to locate sweet spots (areas of less degraded oil), optimize the placement of new wells and completion intervals and help with production allocation from long production wells. Introduction Biodegradation significantly alters the composition of petroleum by the preferential removal of hydrocarbons (especially light end fractions), thus, adversely impacting oil physical properties and, consequently reducing oil producibility. Viscosity and density are key properties for the evaluation, simulation and development of petroleum reservoirs. To develop and manage heavy oil fields cost effectively, it is essential to understand the variation in petroleum fluid properties; especially viscosity throughout each reservoir within a field. A variety of studies demonstrated how oil properties in biodegraded oil accumulations can be predicted from core and cutting extracts prior to well testing using geochemical parameters(1–5). McCaffrey et al.(2) analyzed sidewall cores to identify geochemical parameters that are sensitive to secondary charge. They then developed transforms that related those geochemical parameters to oil quality. Smalley et al.(3) used a similar approach to predict oil viscosity in a biodegraded heavy oil accumulation. Guthrie et al.(4) developed a predictive model of oil quality based on a sample set of produced oils from Venezuela for predicting viscosity, API gravity and sulphur content in oil-stained sidewall cores where these properties cannot be measured directly. Koopmans et al.(5) analyzed oils from a single oilfield in the Liaohe Basin, NE China. They found the large variations in viscosity across the field can be explained by mixing, to various extents, of heavily biodegraded oils with less degraded oils. They established a simple binary mixing model, which may assist in predicting the viscosity of reservoir oils before production. Although all these studies empirically track bulk oil properties within a reservoir using geochemical parameters from oils or densely sampled core material, it has been well documented that empirical and theoretical relationships of viscosity to oil composition for whole oils cannot be universally applied(6). Something about the process of biodegradation is universal and controlled by a few key factors,
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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) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».