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Enregistrement W2037113553 · doi:10.2118/07-04-cs

Field Test of SAGD as Follow-Up Process to CSS in Liaohe Oil Field of China

2007· article· en· W2037113553 sur OpenAlexaboutno aff
Liuqing Yang

Notice bibliographique

RevueJournal of Canadian Petroleum Technology · 2007
Typearticle
Langueen
DomaineEngineering
ThématiqueEnhanced Oil Recovery Techniques
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPetroleum engineeringOil in placeSteam-assisted gravity drainageOil fieldOil viscositySteam injectionGeologyPilot testOil productionOil sandsEnvironmental scienceViscosityPetroleumAsphaltMaterials science

Résumé

récupéré en direct d'OpenAlex

Abstract The Du 84 block of the Shu-1 area in the Liaohe Oil Field is located in Panjin City, Liaoning Province, China. The production formation, Guantao, contains extra heavy oil with a depth of 530–640 m. The reservoir is characterized in thick pay, with high permeability and a very active aquifer. The dead oil viscosity is 230,000 mPa.s at 50 °C. Although the Cyclic Steam Stimulation (CSS) process using vertical wells has been applied successfully in producing oil from the reservoir, the anticipated ultimate oil recovery is less than 29% of the original oil in place (OOIP). To enhance oil recovery beyond that of the CSS, physical and numerical modeling studies were carried out. The Steam Assisted Gravity Drainage (SAGD) process using a combination of vertical and horizontal wells was proposed as a follow-up process to CSS. An additional 27% recovery is anticipated with the proposed follow-up process. This would give a total recovery of 56%. A pilot with four horizontal producers was implemented in the field. CSS was used initially in the horizontal wells for establishing the communication with the surrounding vertical wells. The pilot was then converted successfully to SAGD operations with horizontal wells as continuous producers and some of the surrounding vertical wells as continuous injectors. A total of 44,500 m3 of oil has been produced over the 12 months of SAGD operations between June 2005 and June 2006. The field implementation process and pilot performance, as well as the challenges with this project, are presented in this paper. Introduction This paper is the continuation of an earlier work(1) in which a field pilot was proposed for testing Steam Assisted Gravity Drainage (SAGD) as a follow-up process to CSS based on a reservoir model and feasibility studies. Two field pilot projects were constructed in 2003 in the Du 84 block of the Su-1 area in the Liaohe Oil Field. One pilot is producing from the Xinglongtai formation and the other one is producing from the Guantao formation. The pilot in the Guantao formation was converted to SAGD operations in early 2005 and the field results are encouraging. The field performance from this pilot is reported in this paper. The Steam Assisted Gravity Drainage (SAGD) process, which was described by Dr. R.M. Butler in the late 1970's(2), has been applied successfully for the production of bitumen and heavy oil since it was tested in the Underground Test Facility (UTF) in the Athabasca oil sands of Alberta, Canada(3). Several commercial projects have been implemented in the field in Canada since then. The Liaohe Oilfield Company constructed its first SAGD pilot in China in 1996 in the Xinlongtai formation, which contains extra heavy oil at a depth of 750 m from the surface. The pilot consisted of one stacked well pair and was operated for about one and a half years. The suspension of the pilot test was due to:insufficient lift capacity provided by the gas lift system; and,difficulties in communication resulting from too large a vertical separation between the injector and the producer.

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)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,678
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0040,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,003
Tête enseignante GPT0,232
Écart entre enseignants0,229 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
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

Citations22
Publié2007
Routes d'admission1
Résumé présentoui

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