Abstract
This chapter presents methods for constructing reservoir-model frameworks. A reservoir model framework is a representation of reservoir architecture, and it incorporates geological variables that segregate large heterogeneities in the reservoir. A framework without using faults is termed unfaulted framework and its main inputs are stratigraphic elements. A framework constructed with faults is termed faulted framework and it incorporates both stratigraphic elements and faults. A reservoir-model framework is also termed geocellular model because the 3D model is composed of discretized cells that are subsequently filled with reservoir properties. Heterogeneities of petrophysical properties of a reservoir cannot be accurately described without a 3D geocellular model framework.
This chapter also presents methods for mapping well-log data into a 3D model framework. This is because well data must be collocated with other data to constrain the distributions of the reservoir properties in the 3D model. The data mapping can be more complex than it appears to be because it often involves a change of support (scale).
Form follows function
Luis Sullivan
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ma, Y. Z. (2009). Simpson’s paradox in natural resource evaluation. Mathematical Geosciences, 41(2), 193–213. https://doi.org/10.1007/s11004-008-9187-z.
Ma, Y. Z., Gomez, E., Young, T. L., Cox, D. L., Luneau, B., Iwere, F.. 2011. Integrated reservoir modeling of a Pinedale tight-gas reservoir in the Greater Green River Basin, Wyoming. In Y. Z. Ma & P. LaPointe (Eds.), AAPG Memoir 96, Tulsa.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ma, Y.Z. (2019). Constructing 3D Model Framework and Change of Support in Data Mapping. In: Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling. Springer, Cham. https://doi.org/10.1007/978-3-030-17860-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-17860-4_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17859-8
Online ISBN: 978-3-030-17860-4
eBook Packages: EnergyEnergy (R0)