Integrating data at different scales to characterise petroleum reservoirs

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Copyright: Tyson, Stephen
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Abstract
This thesis is an investigation into the problems of scale in petroleum reservoirs. It is useful to understand how variables that were measured at different scales can be compared. This will then improve the way that data from different scales can be used together. A technique has been developed based on the continuous wavelet transform to compute a coefficient of linear correlation as a function of the scale of measurement of signal data such as well logs and seismic traces. This technique appears to suggest opportunities to determine scale ranges over which measurements of different variables may be compared. Research is continuing in this area but is considered to be outside the scope of this thesis During the course of these investigations it became apparent that the traditional changes of scale in the geological modelling workflow would give poor results. Better determination of effective properties at different scales is shown to require greater scale contrasts than is currently believed. The need for greater scale contrasts indicates a need for geological models that are much more detailed if they are to be upscaled to flow simulation models. But the data structures that are presently used for representing reservoirs have performance limits that constrains their overall size to a few million cells. Honouring the new requirement for greater resolution contrasts means that geological models with hundreds of millions or billions of cells are needed. A new type of grid is described which is able to fit these requirements. The architecture of the storage of the geometry and the gridded properties is defined and some performance issues are addressed. This contribution to the field of reservoir engineering will allow the construction of geological models that can capture well log resolutions vertically and seismic resolutions laterally.
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Author(s)
Tyson, Stephen
Supervisor(s)
Rahman, Sheikh
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Publication Year
2013
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
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