Notice bibliographique
Résumé
This article, written by Assistant Technology Editor Karen Bybee, contains highlights of paper SPE 97719, "New Developments in Steamflood Modeling," by M. Kumar, SPE, C. Satik, SPE, and V. Hoang, SPE, Chevron Energy Technology Co., prepared for the 2005 SPE International Thermal Operations and Heavy Oil Symposium, Calgary, 1–3 November. Using results from fine-scale, multipattern, geostatistical models, the full-length paper reviews key issues related to steamflood modeling. Pattern-element and single-sand models used in many previous studies are not sufficient to explain observed field performance, and larger heterogeneous models give more-realistic recovery predictions. Current computing improvements make larger-scale steamflood modeling viable compared with what was possible earlier, and a realistic steamflood performance is attained when necessary details are included in the model. Introduction Steam injection is the most widely used enhanced-oil-recovery method. Current oil production by steam injection is estimated to be more than 1.1 million BOPD. Most conventional heavy-oil steamflooding projects in California, Canada, Indonesia, and Venezuela employ vertical wells, although use of horizontal producers is growing. On the other hand, extra-heavy oils may require both horizontal injectors and producers, such as in steam-assisted gravity drainage. Oil recovery can exceed 20% of the original oil in place (OOIP) for cyclic steaming and more than 50% OOIP by continuous steam injection. Geologic Models The geologic model used in this study is from portions of the Kern River field, one of the largest oil fields in the U.S. on the basis of OOIP and reserves. The Kern River field is a shallow heavy-oil field 5 miles northeast of Bakersfield, California. The field has been on steam injection since the mid-1960s. Production is from from several distinct sand zones with high permeabilities and porosities deposited in a braided river environment. The sandstones are typically medium- to very coarse-grained, poorly to very poorly sorted, and have little to no detrital clay. The high-quality reservoir sandstones are interbedded with poor-quality sandstones, siltstones, and mudstones, that may be barriers to fluid flow. The individual sand bodies, typically 50 to 100 ft thick, are separated by competent and correlatable shale layers and are steamflooded one at a time. However, some of the shales may not be continuous over the entire project area. In addition, shale continuity varies both areally and vertically. As a result, significant fluid migration can occur between sands, making reservoir management and analysis more challenging. The Kern River field properties of low reservoir pressure, high permeability, and high oil saturations are all favorable for steamflooding. Flow Simulation Model Input Parameters. An average sand porosity of 32% was used in the study. Average permeability was approximately 2,500 md. Vertical permeability was considered to be one-half the horizontal permeability. Initial reservoir temperature and pressure were 90°F and 260 psia, respectively. Initial oil saturation was approximately 50%, and initial gas saturation was 0% in the oil zone. Crude gravity was 14°API and its molecular weight was 400. Dead-oil viscosities range from 4,749 cp at 90°F to 3.6 cp at 400°F. Steam quality at the sandface was 70%. Measured relative permeability values were used. Endpoint saturations and relative permeabilities were considered to be independent of temperature. Three-phase oil relative permeabilities were calculated by use of linear interpolation.
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) | 0,000 | 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 | 0,000 | 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 ».