MétaCan
Menu
Retour à la cohorte
Enregistrement W1550913446 · doi:10.1306/13371587st643550

Overview of Heavy Oil, Seeps, and Oil (Tar) Sands, California

2013· book-chapter· en· W1550913446 sur OpenAlexaffabout
Frances J. Hein

Notice bibliographique

RevueAmerican Association of Petroleum Geologists eBooks · 2013
Typebook-chapter
Langueen
DomaineChemistry
ThématiquePetroleum Processing and Analysis
Établissements canadiensCanada Energy Regulator
Organismes subventionnairesNational Institute on AgingCalifornia Department of Conservation
Mots-clésOil sandsEnvironmental sciencetar (computing)GeologyPetroleum engineeringOil spillMining engineeringArchaeologyGeographyAsphalt

Résumé

récupéré en direct d'OpenAlex

Abstract California has one of the largest reserves for heavy oil in the world, second only to Venezuela. Recent declines in conventional resources and reserves during the last decade have prompted other jurisdictions to examine their prospective unconventional resources, such as heavy oil and oil sands, in a more favorable technological and economic setting. However, this has not been done universally in the United States, where thermal enhanced oil-recovery technologies (mostly used to produce heavy oil) have experienced a decline in production, concomitant with the downturn in conventional production. In California, the seep and oil-sand deposits are mostly unconsolidated sands bound together by biodegraded bitumen. Source rocks for both modern seeps and oil sands and ancient heavy-oil deposits are mainly the Miocene Monterey diatomites and equivalent diatomaceous mudstones and organic shales. In California, most of the seeps and oil sands overlie or are updip from underlying heavy-oil reservoirs. The seep and oil-sand deposits occur in areas where cap-rock integrity was compromised for the underlying heavy-oil reservoirs, breeched mainly by faults or fractures. Hydrocarbons migrated updip into basin-marginal settings or in structural areas of compromised cap rock and then pooled to form the seeps, later hardening into oil-sand deposits. Hydrocarbons accumulated in a wide variety of depositional environments from deep-sea fans, lobes, and submarine channels to fluvial-lacustrine deltas and incised valleys and every other sedimentary environment in between. This makes it difficult to identify type examples for the California accumulations, although case examples are given. In the past, steam-flood, condensed water drive, cyclic steam stimulation (CSS), and fireflood were used to produce the California heavy-oil reservoirs. Currently, significant California CSS projects underway include Belridge, Cymric, and northern Midway Sunset fields to stimulate intermediate-gravity hydrocarbons in the Monterey, Reef Ridge, and Etchegoin diatomite lithologies. Elsewhere, for example, in Canada, in-situ bitumen and extra-heavy-oil sands are commonly developed using CSS or steam-assisted gravity drainage (SAGD). Combined application of CSS along with SAGD from horizontal wells may recover bypassed pay in heavy-oil reservoirs and may be used to recover bitumen from associated oil sands, and multistage multifracing technologies may recover oil from the deeper unconventional (Monterey) source rocks. These technological developments, along with improved computing techniques (i.e., three-dimensional [3-D] geologic modeling/visualization), allow for real-time exploration and development of unconventional reservoirs. A significant effort exists in California to improve recovery from Pleistocene, Pliocene, and Miocene heavy-oil deposits; for example, at present, 70 to 80% recovery from heavy-oil steam drives is seen in Pleistocene Tulare Formation fluvial and alluvial sands. Full 3-D models anchored by extensive coring and logging programs have reaped benefits in many older oil fields (e.g., South Belridge, Midway Sunset, Cymric, Lost Hills, Kern River). Bypassed pay and new production from associated shallow oil sands and deeper source rocks may ultimately be a key to attainment of increases in secure unconventional energy reserves in North America. In the future, full integration of new technologies, along with technology sequencing, may be applied to old California oil fields for production of bypassed pay in heavy-oil fields.

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 candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,975
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
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 Opus0,014
Tête enseignante GPT0,239
Écart entre enseignants0,225 · 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'étudeSans objet
Domainenon disponible
GenreAutre

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

En bref

Citations12
Publié2013
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueAmerican Association of Petroleum Geologists eBooksMême sujetPetroleum Processing and AnalysisTravaux en français237 207