Seismic Characterization of Reservoirs

8/10/2019 Seismic Characterization of Reservoirs

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rmeability Prediction and its Impact on Reservoir Modelin

at Postle Field, Oklahoma

A permeability model was

developed for a thin valley-fill

reservoir at Postle Field, Oklahoma,

with the objective of increasing the

accuracy of flow simulation.

The process involves carbon

dioxide flooding which is fast

becoming the dominant mechanism

of Tertiary recovery. To optimize the

recovery efficiency of this process,

it is important to understand theinfluence of permeability

heterogeneity and its role on fluid

flow.


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Permeability modeling based on

multiple permeability distributions, or

flow units, produced a more reliable

reservoir model to simulate CO2flooding within the fluvial system. The

integrated permeability model was

tested against a binary (sandstone-

shale) model and a base case history

match of liquid production (oil + water)was performed.

High-permeability

zones within some

wells were the cause

of early fluid arrivaland the integrated

permeability model

accurately predicted

the influence of these

zones.

Time-lapse seismic reflects changes in a

reservoir through time and it is a useful tool to

identify areas with changes due to fluidsaturation change and pressure.


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Four cores were used in this study.

Some fully penetrated the A

member of the Morrow sandstone;

it was partial penetration for theothers. Core description was

performed on wells HMU 353,

HMU 354, HMU 613, and HMU

132. The depositional system is

fluvial incised valley fill.

Four facies were identified

from the cores. From facies

4 to facies 1 grain size

decreases; facies 1 is fine-grained, while facies 4 is

conglomeratic in nature.

Four facies were identified

from the cores. From facies

4 to facies 1 grain sizedecreases; facies 1 is fine-

grained, while facies 4 is

conglomeratic in nature


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Permeability modeling

The petrofacies numberdetermines which permeability

distribution will be used to model

permeability for each data point.

The goal is to be as specific as

possible to be able to capture the

essence of the high-permeability

zones represented by

petrofacies

Therefore, the inclusion of

additional logs was used to

provide more information for theprediction process resulting in an

increased accuracy in the

prediction.

Integrated permeability model showing

a high-permeability

zone connecting wells HMU 18-1 and

HMU 17-3.

In 1995 the field was underwater flood, so the time- lapse

anomaly produced when

subtracting these two surveys

should show fluid and

pressure changes from waterinjection to CO2 injection.


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Figure a. shows an example of the

matrix of input parameters and their

respective output. Five input

parameters served as training data:gamma ray, sonic logs, porosity,

spontaneous potential, and the

petrofacies number obtained

previously.

The petrofacies number

assigns a specific permeability

distribution for each data point;

therefore, enabling a more

accurate prediction. Theintegrated permeability model

is based on geologic

information because the main

input was the lithofacies

classification.


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The goal of the comparison was to evaluate the accuracy whenperforming a liquid history match of the study area. The result

showed a dramatic improvement in the history match to

production for some of the wells.

The high-permeability pathway connects HMU 18-1 and HMU

17-3 and coincides in extent and orientation with the anomalyobserved in the time-lapse seismic.

An important tool in the validation of the results was time-lapse

seismic because it represents actual changes within the

reservoir through time.

With multicomponent seismic data, this ambiguity is reduced

because S-wave data are sensitive to pressure changes only,

and it is a better connector to permeability.

Results

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Seismic Characterization of Reservoirs