Should You Trust Your Heavy Oil Viscosity Measurement?
Notice bibliographique
Résumé
Abstract Heavy oil viscosity is one of the few criteria available to help predict if cold production will give economic rates, or if thermal processes will be required to reduce the oil viscosity to achieve the required rates. If cold production is selected, viscosity is again used to help determine whether vertical or horizontal wells should be used. Viscosity data are also used to adjust cold production exploitation strategies if the production rates are significantly lower than expected. Petrovera conducted an extensive viscosity data collection project in a newly developed Elk Point area reservoir with lower than expected, and more erratic than expected, cold production rates. Oil samples collected over short periods of time resulted in viscosity values for the same well varying by a factor of four or more, with similar variation between close-spaced wells. Collection of repeated samples and submission of those samples to several commercial labs resulted in similar viscosity measurement scatter. To further evaluate viscosity data scatter, multiple samples were collected from one well at the same time using the same procedures. These samples were then submitted to several labs in triplicate using, three different well names, to achieve an unbiased test. Reported viscosity scatter was again large. The objectives of this paper are to:display the results of the study to bring this issue to the forefront for discussion, andencourage commercial labs to develop an industry-wide standard method of heavy oil sample cleaning and viscosity measurement. Introduction Why Is It Important to Know Heavy Oil Viscosity Accurately? Heavy oil exploitation is an important segment of the oil and gas industry in Canada1 and a number of other countries2. Motivating factors for exploitation of Canadian heavy oil and bitumen are the large volumes of resource in place and the high historic demand for asphalt-based products that are more readily obtained from heavy oil and bitumen. Western Canadian heavy oil and bitumen reservoirs (Figure 1) have been exploited with varying degrees of success for more than 60 years3. Early workers in the field of heavy oil and bitumen exploitation quickly determined that primary production responses (also called cold production responses) varied greatly from field to field. They also discovered that addition of heat in the form of steam often greatly increased the production response. Viscosity became one of the most valued criteria in their efforts to predict production response from a new field using easily measured parameters. The popularity of the viscosity screening criterion has been manifest by the fact that virtually all technical papers on heavy oil production response or production process development include a discussion of oil viscosity. Unfortunately the accuracy of some production forecasts based on oil viscosity has been poor, and this study was conducted to address one possible cause. This discussion will not dwell on other rheologic properties of heavy oil and bitumen, as these properties are not routinely measured. Rheology is well documented in the literature4–6. Several factors other than viscosity can strongly influence heavy oil and bitumen production rates.
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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 | 0,000 | 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) | 0,006 | 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 ».