| Titre | Pore pressure prediction and brittleness index estimation from well logs for identifying sweet spots |
| Auteur | Liu, Y; Chen, Z |
| Séries alt. | Secteur des sciences de la Terre, Contribution externe 20140389 |
| Éditeur | GeoConvention |
| Réunion | GeoConvention 2015; Calgary; CA; mai 4-8, 2015 |
| Document | publication en série |
| Lang. | anglais |
| Media | papier; en ligne; numérique |
| Formats | pdf |
| Sujets | ressources pétrolières; schiste, derive; schistes; gaz; puits de gaz; pressions du reservoir; productivité; minerais argileux; matières organiques; Dévonien |
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| Programme | Évaluation des ressources pétrolières pour les schistes, Les géosciences pour les nouvelles sources d'énergie |
| Liens | Online - En ligne
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| Résumé | (disponible en anglais seulement)
Resource occurrence in shale reservoir is pervasive, but commercial production from shale reservoirs requires extensive artificial stimulation. Therefore shale
gas development is characterized with low geological risk, but high commercial production risk. As wellhead price for natural gas stays low, economic margin of shale gas development could be thin and early identification of sweet spot is crucial to
economic success. Study from North American shale gas development suggests that a static sweet spot of high resource abundance may not be necessarily coincident with a dynamic sweet spot of high productivity. In addition to resource abundance and
reservoir quality, shale brittleness and reservoir pressure could be the other two major geological factors that affect productivity. In our recent efforts of shale gas resource characterization, well logs and seismic data are used to help identify
the dynamic sweet spots. This paper discusses the use of petrophysical data to estimate shale brittleness and predict formation pressure. In a separate paper ¿Identify shale gas sweet spots from seismic data¿, an effort is made to tackle the problem
from the perspective of seismic processing.
In this study, we propose a petrophysical model that consists of four components, i.e., non-clay grains, clay minerals, organic matter and porosity and use it to represent the shale reservoir volume. The
Eaton equation was applied to calculate the pore pressure from sonic logs, and Passey method (Passey et al., 1990) or support vector regression statistical methods (Liu et al., 2013) was employed to estimate TOC. An inverse-distance weighted method
was then utilized to evaluate mineral compositions after correcting the impacts of clay mineral and kerogen contents on the well responses. Finally the brittleness index was estimated based on mineral compositions and geomechanical properties derived
from dipole sonic log for wells that data are available. The potential dynamic sweet spots could be the area with high resource abundance, high brittleness index and high pore pressure. This paper discusses the petrophysical model, mineral
composition estimation using the proposed inversed distance method, shale brittleness index estimation from dipole sonic data, and the relationship between the estimated mineral compositions and rock brittleness through examples from the Devonian
Duvernay Shale in the Western Canadian Sedimentary Basin. |
| Résumé | (Résumé en langage clair et simple, non publié)
L'exploitation du gaz de schiste est caractérisée par un faible risque géologique, mais par un risque élevé quant à la production commerciale.
Dans la présente étude, nous proposons un modèle pétrophysique pour aider à repérer les zones idéales au moyen de données pétrophysiques. Les zones idéales dynamiques potentielles pourraient être les zones ayant une grande abondance de ressources, un
indice de friabilité élevé et une pression interstitielle élevée. Le présent article aborde le modèle pétrophysique, l'estimation de la composition minérale, l'estimation de l'indice de friabilité du schiste à partir de données soniques dipôles, et
la relation entre la composition minérale et la friabilité de la roche par des exemples du schiste de Duvernay du Dévonien, dans le bassin sédimentaire de l'Ouest canadien. |
| GEOSCAN ID | 295730 |
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