MétaCan
Menu
Retour à la cohorte
Enregistrement W4255454756 · doi:10.2523/65525-ms

Optimization Methodology for Cyclic Steam Injection With Horizontal Wells

2000· article· en· W4255454756 sur OpenAlexaboutno aff
E. Escobar, P. Valko, W.J. Lee, M.G. Rodriguez

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEngineering
ThématiqueReservoir Engineering and Simulation Methods
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCitationLibrary scienceComputer scienceDownloadInformation retrievalWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

Optimization Methodology for Cyclic Steam Injection With Horizontal Wells E. Escobar; E. Escobar PDVSA INTEVEP Search for other works by this author on: This Site Google Scholar P. Valko; P. Valko Texas A&M Search for other works by this author on: This Site Google Scholar W.J. Lee; W.J. Lee Texas A&M Search for other works by this author on: This Site Google Scholar M.G. Rodriguez M.G. Rodriguez PDVSA E&P Search for other works by this author on: This Site Google Scholar Paper presented at the SPE/CIM International Conference on Horizontal Well Technology, Calgary, Alberta, Canada, November 2000. Paper Number: SPE-65525-MS https://doi.org/10.2118/65525-MS Published: November 06 2000 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Escobar, E., Valko, P., Lee, W.J., and M.G. Rodriguez. "Optimization Methodology for Cyclic Steam Injection With Horizontal Wells." Paper presented at the SPE/CIM International Conference on Horizontal Well Technology, Calgary, Alberta, Canada, November 2000. doi: https://doi.org/10.2118/65525-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE/CIM International Conference on Horizontal Well Technology Search Advanced Search AbstractHorizontal wells are becoming a very important component in the thermal recovery of heavy oil reservoirs. The success of a cyclic steam injection project depends strongly on the selection of key parameters, such as cycle length and amount of steam injected. The numerical simulation of horizontal wells, especially under non-isothermal conditions, is computationally demanding. When optimization is combined with numerical simulation, the computing time requirement may be prohibitive and it is not guaranteed that the optimal conditions will be found.In this research, a new methodology has been developed for optimizing the cyclic steam injection process for vertical and horizontal wells. The procedure integrates oil production characterization using numerical simulation, net present value maximization through a Quasi-Newton method, and model validation/tuning. The three-stage procedure provides the optimum number and/or duration of cycles, the optimal amounts of steam to be injected in each cycle and the optimal value of the overall economic indicator.The optimization algorithm was successfully validated with published results obtained from the discrete maximum principle. The methodology was then applied to determine the optimal conditions of cyclic steam injection for a horizontal well located in Bachaquero field, Venezuela.IntroductionThermal stimulation of heavy-oil producing wells by cyclic steam injection has received attention since the early 1960's. Currently, steam stimulation is being applied on a commercial scale, particularly in Venezuela, California and Canada.With the arrival of horizontal well technology, the production from heavy and extra heavy oil reservoirs has been considerably improved. One of the prospective areas for using horizontal wells is thermal recovery using steam.Horizontal wells represent an indispensable technology for the production of bitumen or extra heavy oil formations. Process like SAGD, HASD drive, and Vapex have been specially designed using horizontal wells for recovery of oil that is immobile at original reservoir conditions. Today, these processes represent the most feasible alternatives to produce relatively deep bitumen formations.For conventional heavy oil reservoirs, the selection of horizontal wells is not a simple issue. Horizontal wells have been successfully applied in areas where gas and/or water conning is the major problem. However, factors such as vertical and horizontal permeability anisotropy, reservoir thickness, and sand production have strong influence in the production performance of this type of well. Under specific reservoir scenarios, the use of horizontal wells does not always represent the best alternative.In thermal oil recovery like cyclic steam injection and steam drive, horizontal wells have notable advantages over vertical wells such as better heat distribution and lateral transportation of fluids. In addition, the number of wells necessary to produce a pattern decreases in reservoirs with close well spacing. Nevertheless, the success of the steam injection process strongly depends on ensuring a uniform placement of the steam along the total well length.Success in the combination of cyclic steam injection with horizontal wells will depend upon an appropriate technical and economic design. To the best of our knowledge, no optimization methodology has been developed to support design decisions about thermal stimulation of horizontal wells. Among the complications associated with this task are the lack of long field experience under a wide range of conditions, and lack of an analytical solution to predict the oil recovery from a thermally stimulated horizontal well. Keywords: steam injection, production rate, upstream oil & gas, horizontal well, sagd, artificial intelligence, optimization methodology, thermal method, reservoir, production time Subjects: Improved and Enhanced Recovery, Thermal methods This content is only available via PDF. 2000. SPE/PS-CIM International Conference on Horizontal Well Technology You can access this article if you purchase or spend a download.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,027
Tête enseignante GPT0,274
Écart entre enseignants0,247 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations15
Publié2000
Routes d'admission1
Résumé présentoui

Explorer davantage

Même sujetReservoir Engineering and Simulation MethodsTravaux en français237 207