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Enregistrement W4310869811 · doi:10.2118/212302-pa

Improvement to Gravity Drainage Recovery by Repressurization as a Criterion to Screen and Rank Naturally Fractured Reservoirs for Gas Injection

2022· article· en· W4310869811 sur OpenAlexaff
N. Alimohammadi, M. Pooladi‐Darvish, Behzad Rostami, Maryam Khosravi

Notice bibliographique

RevueSPE Reservoir Evaluation & Engineering · 2022
Typearticle
Langueen
DomaineEngineering
ThématiqueHydraulic Fracturing and Reservoir Analysis
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésPetroleum engineeringEnhanced oil recoveryOil in placeGeologyPermeability (electromagnetism)Fossil fuelWater injection (oil production)Natural gas fieldDrainageNatural gasEnvironmental scienceEngineeringWaste managementPetroleumChemistry

Résumé

récupéré en direct d'OpenAlex

Summary Many of the naturally fractured carbonate reservoirs of the Middle East exhibit low natural-depletion recoveries. The reason is that most of their oil reserves are stored in the low-permeability host rocks and are left behind by the fast advancing gas/oil contact (GOC) and water/oil contact (WOC) in fractures. Producing the remaining oil in the large gas-invaded zone of these reservoirs has been a crucial reservoir management issue. We show in this study using experimental observations, analytical calculations, and numerical investigations that repressurizing naturally fractured reservoirs (NFRs) by crestal immiscible gas injection has the potential to produce a large portion of this remaining oil by improving gravity drainage (GD) through two main mechanisms. One is that at higher pressures, the gas-oil interfacial tension (IFT) and hence the capillary forces that control recovery by GD are lessened, allowing additional recovery. This mechanism is aided by the other one, which is swelling of the oil at higher pressures. In this way, repressurization is thought to be not only a means for pressure maintenance but also a methodology for enhanced-oil recovery (EOR). This is confirmed by both laboratory studies and field performance of large-scale gas injection projects. Despite the desire for implementation of projects of repressurization, gas availability and cost of these projects are important concerns, requiring a cost-benefit analysis. Screening and ranking methodologies have been previously presented for some EOR techniques but not for repressurization by gas in NFRs. Evaluating the performance of gas injection in NFRs is often done using methodologies such as numerical simulations, which are in-depth, costly, and tedious. The methodology developed here is simple, requiring spreadsheet calculations. To develop the methodology, we first obtain simple relations to calculate additional GD recovery by considering the interplay of capillary and gravity forces in a matrix block subjected to pressurization by equilibrium gas injection and then use experimental data from literature to show that these relations can predict primary and secondary GD recoveries to a good approximation. We also show by mechanistic studies using a history-matched numerical model that IFT reduction and oil swelling are the main mechanisms contributing to additional oil recovery. Then, we propose a methodology to screen and rank candidate NFRs for gas injection that uses commonly available reservoir data and is based upon two criteria, these being additional oil recovered from a matrix block by pressurization and required volume of gas to produce an additional barrel of oil. We then implement this methodology to more than 20 Iranian NFRs and identify six reservoirs with potential for additional recovery of more than 20%. By quantifying and including the uncertainties associated with the reservoir data, we illustrate that for the reservoirs under study, capillary pressure parameters along with matrix-block height are the main parameters affecting GD recovery and should be characterized more accurately.

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)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,201
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
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 Opus0,009
Tête enseignante GPT0,257
Écart entre enseignants0,248 · 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.

Devis d'étudeSimulation ou modélisation
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
Publié2022
Routes d'admission1
Résumé présentoui

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