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Enregistrement W2068289789 · doi:10.1016/j.egypro.2011.02.261

Northwest McGregor field CO2 Huff ‘n’ Puff: A case study of the application of field monitoring and modeling techniques for CO2 prediction and accounting

2011· article· en· W2068289789 sur OpenAlexaboutno aff
James A. Sorensen, Darren D. Schmidt, Damion J. Knudsen, Steven A. Smith, Charles D. Gorecki, Edward N. Steadman, John A. Harju

Notice bibliographique

RevueEnergy Procedia · 2011
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueCO2 Sequestration and Geologic Interactions
Établissements canadiensnon disponible
Organismes subventionnairesNational Energy Technology LaboratoryU.S. Department of Energy
Mots-clésCarbon sequestrationEnhanced oil recoveryPetroleum engineeringCarbonateGeologyStructural basinPlumeOil fieldCarbon dioxideEnvironmental scienceGeomorphologyMaterials science

Résumé

récupéré en direct d'OpenAlex

The Plains CO 2 Reduction (PCOR) Partnership has conducted field and laboratory activities to determine the effects of injecting carbon dioxide (CO 2 ) into an oil field in the U.S. portion of the Williston Basin. These activities were conducted as part of Phase II of the U.S. Department of Energy’s Regional Carbon Sequestration Partnership program. The purpose of the activities was to evaluate the potential dual purpose of CO 2 storage and enhanced oil recovery (EOR) in carbonate rocks deeper than 2400 m. Activities were conducted to (1) establish the baseline geological characteristics of the injection site, (2) determine the effect that CO 2 has on the ability of the oil reservoir to store CO 2 and produce incremental oil, and (3) evaluate the ability of Schlumberger’s Reservoir Saturation Tool (RST) and Vertical Seismic Profile (VSP) technologies to detect a small-volume CO 2 plume in deep carbonate reservoirs. While the CO 2 -based EOR operations at the Weyburn and Midale fields in Saskatchewan, Canada, are good examples of economically and technically successful injection of CO 2 for simultaneous EOR and sequestration, the depths of injection in those fields are relatively shallow (ca. 1400 m) and not necessarily representative of many large Williston Basin oil fields. One of the primary goals of the PCOR Partnership Phase II Williston Basin Field Validation Test was to evaluate the effectiveness of CO 2 for EOR and sequestration in oil fields at depths greater than 2400 m. To achieve that goal, a CO 2 huff ‘n’ puff (HnP) test was conducted on a well that is currently producing oil from the Mission Canyon Formation at a depth of approximately 2450 m in the Northwest McGregor oil field in Williams County, North Dakota. During the test, 440 tonnes of CO 2 was injected into a single well and allowed to “soak” for 2 weeks, after which the well was put back into production. Unique elements of the Northwest McGregor Mission Canyon reservoir as compared to other HnP operations in the literature include the following: (1) at a depth of 2450 m, it would be among the deepest, (2) pressure (approximately 20 MPa) and temperature (approximately 80 °C) would be among the highest for a HnP, and (3) most HnPs in the literature are in clastic reservoirs, while the Northwest McGregor Mission Canyon reservoir is a carbonate reservoir. Using a petrophysical model of the reservoir, iterative dynamic simulations of the fate of CO 2 in the target reservoir were developed. Characterization and modeling in support of dynamic simulations included normalizing all logs and performing an error-minimizing stochastic multimineral petrophysical analysis. Neural networks were used to produce matrix permeability to gas and liquids, vertical permeability to gas, irreducible fluid saturations, fracture intensity, and missing zones or logs in the study area. Petrophysical results were verified with Qemscan ® , x-ray diffraction, petrographic analysis, and cutting and core descriptions. This produced the main components for a macrofacies/microfacies and fluid model, with the major lithofacies being limestones, dolomites, and anhydrites. To gain a regional understanding of the producing interval, large-scale trend modeling used a traditional sequential indicator and Gaussian simulations, while small downscaled injection models used discrete and continuous multiple point statistics guided by inverted seismic data. The dynamic response of the injection zone was evaluated for changes over the course of the project using two-dimensional VSP projected into three dimensions, temporally resolute RST logs in sigma mode, and produced fluid analyses that were used to history-match fluid and gas saturations. The static and dynamic modeling activities were conducted in an iterative manner, with each iteration based on the acquisition of new data over the course of the baseline characterization, injection, and postinjection activities. These simulations were compared to actual postinjection reservoir conditions as monitored over the duration of the study period. The simulations demonstrated the importance of considering the effects of fracture networks on CO 2 movement when predicting CO 2 mobility and fate. The results of the RST indicated that the CO 2 migrated approximately 15 meters vertically into the reservoir. The results of the VSP provided valuable data regarding the horizontal nature of the Mission Canyon reservoir and sealing lithofacies identified by the RST and core studies, but its ability to identify a CO 2 plume within the reservoir was determined to be inconclusive. Productivity of the oil well was observed to more than double over the course of a 3-month production period, increasing from a baseline oil production rate of 1.5 stock tank barrels (STBs) a day to 3 to 7 STBs a day. Overall, the results of the field demonstration indicate that (1) CO 2 -based HnP operations may be a viable option for EOR in deep carbonate oil reservoirs, (2) the RST and VSP technologies are effective tools for baseline characterization, and (3) the RST may be an effective MVA tool for deep carbonate oil reservoirs, but the ability of the VSP technology to identify small volumes of CO 2 in a deep carbonate reservoir could not be conclusively determined.

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)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,828
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,024
Tête enseignante GPT0,261
Écart entre enseignants0,237 · 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'étudeObservationnel
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

Citations3
Publié2011
Routes d'admission1
Résumé présentoui

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