Stationarity Analysis
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Transcript of Stationarity Analysis
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Geomodellers Forum Newsletter #1
In this issue: What is the this
stationarity analysis How can this
analysis help? Which data type can
be used in this analysis? Examples of
analysis
December 14, 2014Stationarity Analysis
Stationary
means the spatially separated parts of the data that is
effectively controlled by the same underlying system,
or come from the same probability distribution.
If the reservoir has multiple stations, it implies that
certain parts of the data should be treated differently
with respect to the other parts in property modeling
workflow. A certain statistical analysis would reveal
stations in the dataset of interest.
Benefit of the analysisStationarity analysis is used to identify continuity of geological features and the presence of lateral and/or vertical trends in data set. The importance of this behavior can be reflected in the method for predictions.
Q: What is the right time to perform this analysis?A: After loading all data, modeler has to perform this analysis
Q: What kind of data can be used?A: Any and all static properties and even some of the dynamic data can be used for this analysis.
Q: What is the consequence of missing this step?A: Property modeling results would be more uncertain especially in those area that should be treated differently.
Successful property prediction system relies on the accuracy of input data and existence of captured stations. When the
investigation of stationarity is performed, the results may show spatially existing trends in entire reservoir or in certain
direction and region...
You win, we win
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Methodology to perform stationary analysis:Spatial distribution of properties indicates the existence of stationary characteristics of the data. Areal distribution of the input data reveal the evidence. You can generate following plots to make stationarity analysis: Map view of average porosity values at well location per sub-zones Cross plot of
porosity vs depth Porosity vs X-location Porosity vs Y-location Permeability vs depth and locations Calculating or generating local mean of property distribution
In this map view, circles with color
coding demonstrate the distribution
of porosity and the triangles shows
permeability. The average values of
well data from red to pink decreases
for both properties. Polygons show
gas oil contact in green and oil
water contact in blue.
East side of the field manifest higher
properties than the west side.
Moreover a depth trend from crest
to flank decimates the rock quality.
Yet, the trend is different in different
direction. In such a case a single
trend can not be used for modeling.
Reservoir property variations exhibiting certain features over a wide range of
scales are crucial to be identified from the macro-scale of well log up to inter well
locations and finally to field scale. Connectivity, isotrophy, volumetrics and local
sweep efficiency are investigated generally through static modeling and dynamic
simulation work. Early indications of both transmissibility and storage capacity of
the rock around the field can be obtained through stationarity analysis.
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Above log porosity versus depth plot color coded by the location (pink to red
designates north to south direction) indicates that the data in the south part of the
field (red points) shows totally different behavior then the north part (pink to blue).
Areal distribution of the input data reveals the evidence for the stationary
characteristics of the data. Distribution of average porosity values at well location in
each sub-zones demonstrates the fact about the stations. Different areas and
directions (North, Middle Flank and South) obviously show different trends
Average permeability map on the
left indicates existence of no trend
or stationaries. Low and high
permeability points are scattered
around the field. This might be due
to faults and fractures or abrupt
facies change. Instead of spatial
relation, for permeability modeling,
another correlation has to be used
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Stationary analysis can be applied various data in various domain. General practice
in the industry is to analyze static data spatially to define stations. Data set mixing
both the spatial and the time dimensions can be also used in this type of analysis.
Stationarity assumptions along the spatial plane do not translate into time domain.
Special care is therefore necessary when dealing with dynamic data. For instance,
production data constitute a 2-dimensional spatial plane per well and also varies
timely.
Instead of mapping production data to understand the reservoir, mapping change in
production or pressure drop would be more meaningful to manifest depletion of the
reservoir at well locations. The ability to define connectivity, sweep and potential
areas of improvement, change in dynamic parameters can be displayed on 2D
special geometry.
The result of average values for a given zone can be displayed as continues value
points or discrete colored blocks which might help more to see stations with same
colors. This can be achieved to generate large size grids then upscaling the logs into
3D model grids.
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