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Stochastic Modeling of Underground Reservoir Fades (SMURF)

Authors: H.H. Haldorsen; C.J. MacDonald;

Stochastic Modeling of Underground Reservoir Fades (SMURF)

Abstract

ABSTRACT This paper presents algorithms for generating idealized large-scale reservoir architecture representations. Discontinuous sands suspended in mud are discussed first. The accessible and floodable fractions of total pay are evaluated in 2-D and 3-D for specified conditions (well spacing, [N/G]-ratio,..). Next, a hybrid approach to the modelling of discontinuous permeability barriers such as shales is presented. Finally, stochastic modelling of faults, aquifers, and fractures is discussed. The authors conclude that a need exists for devicing approximate, yet realistic, computer-based methods for generating idealized reservoir representations for subsequent evaluation of the chance of success of proposed development schemes. Unless the reservoir architecture and hence the reservoir plumbing system is described in a quantitative (geometrical) manner, forecasting tools cannot be expected to yield realistic recovery factor estimates. The stochastic approach must always be guided by a geological/sedimentological model, as well as core, log, test, production, outcrop and seismic data.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
19
Average
Top 10%
Top 10%
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