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Enregistrement W2954535201 · doi:10.2118/191644-pa

Geochemical Productivity Index (<i>Igp</i>): An Innovative Way To Identify Potential Zones With Moveable Oil in Shale Reservoirs

2019· article· en· W2954535201 sur OpenAlexaff
Jaime Piedrahita, Roberto Aguilera

Notice bibliographique

RevueSPE Reservoir Evaluation & Engineering · 2019
Typearticle
Langueen
DomaineEngineering
ThématiqueHydrocarbon exploration and reservoir analysis
Établissements canadiensUniversity of Calgary
Organismes subventionnairesEcopetrol
Mots-clésOil shalePetrophysicsPetroleum engineeringGeologyMaturity (psychological)Tight oilSoil sciencePorosityMineralogyGeotechnical engineering

Résumé

récupéré en direct d'OpenAlex

Summary In this paper we present a method for identifying intervals in shale oil reservoirs that contain moveable hydrocarbons with a novel geochemical productivity index (PI), Igp. This index merges three important rock properties that must always be considered for sound shale oil reservoir characterization: vitrinite reflectance (%Ro), oil–saturation index (OSI), and free–water porosity (ϕFW). Integrating this index with other petrophysical properties and geomechanical parameters defines intervals with high moveable oil content. Shale oil is both a source rock and an unconventional reservoir rock. Hence, it is critical to know both its organic–matter (OM) maturity and its oil/water flow capacity. The introduced Igp considered these features simultaneously; maturity was evaluated by discretizing %Ro from 0 to 1, depending on whether the rock was immature or not; free oil flow capacity modeled the normalizing OSI between 0 and 1 on the basis of results from the Rock-Eval VI pyrolysis (REP) obtained in the laboratory or by electric logs; and water flow capacity was estimated from ϕFW, obtained using a nuclear–magnetic–resonance (NMR) log, which was transformed into an index between 0 and 1. Flow oil capacity was defined as the amount of moveable oil that exceeded the sorption capacity of the source rock. Using the Igp is explained with real data from a vertical well that penetrates several stacked shale oil reservoirs. However, the same approach can be used in any other type of wellbore architecture (i.e., deviated, horizontal, geosteered). Initially, a correlation between vertical depth and %Ro was developed. This resulted in a continuous OM–maturity curve along the well section. Next, OSI was simulated by using a bin porosity from an NMR log, where T2 was between 33 and 80 milliseconds and was correlated with OSI data from REP. As a result, a good match between the simulated and the real OSI data was achieved. Similar to OSI, ϕFW was also calculated from the NMR log, but it used a bin porosity when T2 was greater than 80 milliseconds. These three parameters were then transformed to fractional indices, which were combined into a unique index, Igp. When the index was greater than 0.66, there was a good chance that the three conditions mentioned above would be met. For the example well considered in this study, it was found that almost 30% of the total vertical section had good moveable oil potential. This corresponded to 10 intervals in the well. The key novelty of this paper is that we have developed a continuous curve of an index that is easy to use and is powerful for identifying intervals with moveable hydrocarbon potential. This is true even in those intervals without laboratory data because of the continuity of the Igp curve, in addition to the Igp integrated criteria that are usually applied independently. The Igp index is a simple–to–use approach. However, because it is a new method, an explorationist should validate it against real oil production information.

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)0,000
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 enseignants0,075
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,016
Tête enseignante GPT0,272
Écart entre enseignants0,256 · 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

Citations4
Publié2019
Routes d'admission1
Résumé présentoui

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