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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

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.

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

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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Disclaimer

The technical offer, recommendations and conclusions contained in this proposal are based on the data provided to us and our understanding. The proposal was prepared according to the standard of skill and care that is expected of a professionally qualified and experienced experts. However, Grenergy LLC does not warrant the accuracy or correctness of any results, conclusions or recommendations and accept no liability for any loss, damage or costs that may be incurred or sustained through the use of the results, conclusions or recommendations of this proposal.

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