geomodellers newsletter 1 stationarity analysis
DESCRIPTION
Before starting static geological modeling, data qc and anaylsis have to be performed to understand the characteristics of the reservoir. Stationarity analysis is one of the most impartant step in property modelingTRANSCRIPT
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Grenergy LLC16192 Coastal Hwy. Lewes, DE 19958 USAPhone : +1 707 215 1585 Fax : +1 707 215 1585 www.grenergyllc.com [email protected]
Geomodeller’s 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
Stationarymeans the spatially separated parts of the data that iseffectively controlled by the same underlying system,or come from the same probability distribution.
If the reservoir has multiple stations, it implies thatcertain parts of the data should be treated differentlywith respect to the other parts in property modelingworkflow. A certain statistical analysis would revealstations in the dataset of interest.
Benefit of the analysis
Stationarity 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 colorcoding demonstrate the distributionof porosity and the triangles showspermeability. The average values ofwell data from red to pink decreasesfor both properties. Polygons showgas oil contact in green and oilwater contact in blue.East side of the field manifest higherproperties than the west side.Moreover a depth trend from crestto flank decimates the rock quality.Yet, the trend is different in differentdirection. In such a case a singletrend 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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