SPE-154215Combining Absorption and AVO Seismic

Attributes Using Neural Networks to High -Grade Gas Prospects

A. Rahimi Zeynal, University of Southern CaliforniaF. Aminzadeh, University of Southern CaliforniaA. Clifford, Saratoga Resources, Inc.

identifying possible commercial shallow gas targets for drilling

Statement of Problem:

Naturally-occurring gas seeps have long been known to exist at Grand Bay field located in South -

Statement of Problem:

Bay field located in South -Eastern Louisiana.

Need for improvement:

- Well logs: small investigation radius, high resolut ion

- Seismic data: large investigation radius, low resol ution

- 1989, (S.R. Rutherford), AVO variations in gas sands

Solution Methods:

- 1989, (S.R. Rutherford), AVO variations in gas sands

- 2004, (J. Walls) Reducing hydrocarbon indicator risk

- 2011, (A. Clifford) Detecting gas using AVO attributeanalysis

• AVO Attributes

• Absorption Attributes Gas Volume

Suggested approach:

• Absorption Attributes Gas Volume

• Well Logs

workflow for gas detection:

First Step

Create AVO attribute Create AVO attribute

(Amplitude Variations with Offset)

AVO Attribute Analysis:

AVO is a hydrocarbon indicator that is widely accepted

as a means of detecting gas saturated sandstones.

Sharp drop in Vp with small with small increase of gas saturation

Offset

- Brine saturated case will show a decaying amplitude with offset

AVO Attribute Analysis:

- Gas saturated case will show an increasing amplitude with offset

AVO Attribute Analysis:

Near Offsets

Mid Offsets

AVO Attribute Analysis:

Far Offsets

900’ Sand Pre-Stack Time Migrated (PSTM) far minus nears AVO anomaly

1050’ Sand Pre -Stack Time Migrated (PSTM) far minus nears AVO anom aly1050’ Sand Pre -Stack Time Migrated (PSTM) far minus nears AVO anom aly

Second Step

Calculate frequency dependent

workflow for gas detection:

Calculate frequency dependent

attributes

Frequency Attribute Analysis:

High frequency content of seismic response attenuates more

extensively as it propagates through gas-bearing reservoirs.

ATTRIBUTE FUNCTION

Average Frequency Squared (AFS) Magnifies high frequency loss

Frequency Slope Fall (FSF) Highlights flattening of spectrum

Spectrum Area Beyond MDA (SAB) A measure of high frequency loss

Dominant Frequency (MDA) Reduces the impact of noise

Avbsorption Quality Factor (AQF) Overall measure of absorption

Frequency Attribute Analysis:

MSA*Dominant Dominant Absorption MSA*Dominant Frequency

Dominant Frequency

Average Frequency Squared

Frequency Slope Fall

Absorption Quality Factor

Frequency Attribute Analysis:

AQF is defined as the area of the power spectrum

beyond the dominant frequency.

AQF attribute anomalies for 900’ Sand, Grand Bay Fi eld.

AQF attribute anomalies for 1050’ Sand, Grand Bay F ieldAQF attribute anomalies for 1050’ Sand, Grand Bay F ield

AVO

AQF

AQF

AVO

Third Step

Create required logs

workflow for gas detection:

Create required logs

from well data

Gas has a very marked effect on both density and

neutron logs. It will result in a lower bulk density,

and a lower apparent neutron porosity

Well logs and gas effects:

Fourth Step

Train a neural network

workflow for gas detection:

Train a neural network

based on the attributes and well logs

Neural Network Training:

The Neural Network were trained based on seed points from

well control data, AVO and absorption related attributes.

Neural Network Training:

- Gas and background pick sets from AQF and

AVO gas bearing pointsTraining Inputs

- Using neutron, density and gamma ray logs

- Gas Cube

Inputs

Output

Fifth Step

Create the gas probability volume

workflow for gas detection:

Create the gas probability volume

The real power of the reliability of results comes

from combining absorption and AVO anomalies plus

log data in evaluating the impact of different

concepts on the output.

Neural network-based methods

Neural Network property prediction:

Neural network-based methods

Solve the non-linear relationship between the seismic

data and reservoir properties

ANN log training based on multi-log combinations pl us AVO/frequency attributes for 900’ sand.

ANN log training based on multi -log combinations plus AVO/frequency ANN log training based on multi -log combinations plus AVO/frequency attributes for 1050’ sand

1- Artificial Neural Network is a successful tool to detect shallow

gas sands with 3D seismic data.

2- Training the Neural Network with AVO & Absorption Quality

Factor (AQF) attributes results in better gas sand identification.

Conclusion:

3- Neural Network yielded highest resolution when using AVO and

AQF attribute in conjunction with Well logs.

4- The analysis supports the presence of numerous undeveloped

pockets of shallow gas in the field as well as identifying new

possible leads.

The authors would like to express their gratitude f or use of dGB’sOpendTect and SMT’s Kingdom Suite software to generate the results in this paper.

Thanks for YourYour

attention