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Enregistrement W2159078782 · doi:10.2118/100063-pa

EOR Field Experiences in Carbonate Reservoirs in the United States

2007· article· en· W2159078782 sur OpenAlexaff
Eduardo Manrique, Viviana Eugenia Muci, Mariano Gurfinkel

Notice bibliographique

RevueSPE Reservoir Evaluation & Engineering · 2007
Typearticle
Langueen
DomaineEngineering
ThématiqueEnhanced Oil Recovery Techniques
Établissements canadiensQuest University Canada
Organismes subventionnairesUniversity of Texas at AustinU.S. Department of Energy
Mots-clésCarbonatePetroleum engineeringEnhanced oil recoveryOil in placeGeologyOil fieldFossil fuelCompletion (oil and gas wells)PetroleumWaste managementEngineeringMaterials science

Résumé

récupéré en direct d'OpenAlex

Summary A considerable portion of the world's hydrocarbon endowment is in carbonate reservoirs. Carbonate reservoirs usually exhibit low porosity and may be fractured. These two characteristics along with oil-to-mixed wet rock properties usually result in lowered hydrocarbon recovery rates. When enhanced oil recovery (EOR) strategies are pursued, the injected fluids will likely flow through the fracture network and bypass the oil in the rock matrix. The high permeability in the fracture network and the low equivalent porous volume result in early breakthrough of the injected fluids. Infill drilling programs and well conformance strategies—mostly gas and water shutoff—have been effectively used to mitigate the early breakthrough and increase oil recovery. In most cases, however, 40 to 50% of the original oil in place (OOIP) is not produced. A large number of EOR field projects in carbonate reservoirs have been referenced in the literature since the early 1970s. These field projects demonstrate the technical feasibility of various EOR methods in carbonate reservoirs. However, because of the collapse in oil prices, most of the aforementioned project plans have been abandoned. This paper presents a comprehensive compilation of EOR (Gas, Chemical, and Thermal methods) field experiences in carbonate reservoirs within the US, as an attempt to identify key variables and project design parameters for future evaluation and revitalization of mature carbonate reservoirs. Carbon dioxide flooding [continuous or water-alternating gas (WAG)] is the dominant EOR process used in the US This is because of the high availability of low-cost CO2. CO2 EOR in particular represents the logical first step towards viable geologic carbon storage and sequestration. EOR chemical methods in carbonate reservoirs, especially polymer flooding, have been widely tested in US carbonate reservoirs. However, EOR chemical methods have made a marginal contribution, relatively, in terms of total oil recovered. Our study includes a brief overview of current laboratory (e.g. wettability changes and novel chemical additives) and field (e.g. injectivity enhancement) experiences in EOR chemical methods in carbonate formations. A brief discussion surrounding the screening methods used to identify viable EOR opportunities in carbonate fields based on past and present experiences is also included. Introduction Carbonate reservoirs are naturally-fractured geologic formations characterized by heterogeneous porosity and permeability distributions. In the case of low porosity and low permeability carbonate rocks (more specifically rock matrices), the fluid flow in the reservoir can be completely dependent on the fracture network while the matrix only plays a source role (analogous to tight sand formations and natural gas flow). In the case of porous carbonate rocks, fracture networks can cause uneven sweeping of the reservoir, leading to early breakthrough of injected fluids in the producing wells and resulting in low recovery factors. The abundance of carbonate reservoirs has been the subject of numerous studies attempting to characterize their heterogeneities, classify different types of fractured reservoirs, and determine how rock and fluid properties have an impact on ultimate recovery (Roehl et al. 1985; Allan and Qing Sun 2003; Carr et al. 2001; Grave et al. 2000; Benson et al. 1998; Wardlaw 1996). The TORIS database (maintained by the US Department of Energy) indicates 22% of the OOIP in the US is contained in shallow-shelf carbonate reservoirs. Currently in the US, these types of reservoirs exist in more than 14 states with over 70% of the OOIP located in reservoirs in Texas and New Mexico, mostly concentrated in the Permian Basin (Nuckols 1992, Xie et al. 2005). Over the last three decades, primary production, waterflooding, and CO2 floods, combined with infill drilling programs, have been the most widely used recovery methods. However, other EOR strategies have been tested in the past and there are currently several active research programs seeking alternatives to increase the recovery factor of these mostly light crude-oil reservoirs economically. Although the Permian Basin (west Texas and southeast New Mexico) can be considered mature, its potential for improved oil recovery is still very high. A recent study reports that there is an estimated 30 billion barrels of mobile oil in the Permian Basin, reiterating the strategic importance of EOR technologies for carbonate reservoirs and their impact on US oil production (Nuckols 1992; Xie et al. 2005; Seethepalli et al. 2004; Moritis 2004; Cole 2003; Moritis 2003; Dutton et al. 2004).

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: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,043
Score d'incertitude au seuil0,085

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,026
Tête enseignante GPT0,300
Écart entre enseignants0,274 · 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'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

Citations280
Publié2007
Routes d'admission1
Résumé présentoui

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