Reservoir Characterization Using Intelligent Seismic...

Reservoir Characterization Using Intelligent Seismic Inversion

Emre Artun, WVU Shahab D. Mohaghegh, WVU

Jaime Toro, WVU Tom Wilson, WVU

Alejandro Sanchez, Anadarko

September 15, 2005

ERM 2005 Morgantown, W.V. SPE Paper # 98012

motivation > Reservoir Modeling Workflow

Exploration: Seismic Surveys

Exploration Drilling

Reservoir Characterization

Reservoir Simulation

A structural model of the reservoir can be attained.

Some data can be obtained from wells ( i.e. well logs, cores, well tests … )

Geostatistical variogram models can be developed with the available data to interpolate / extrapolate available well data to the entire field.

Flow in that 3D reservoir can be modeled with commercial reservoir simulators to predict reservoir performance.

Field Development

- Issues about the data and problems regarding data analysis must be considered carefully in reservoir characterization.

- Geostatistical models become insufficient in dealing with issues like uncertainty, large variety of scales, immense size of data, etc.

- As an alternate; our industry has realized the power of soft computing tools, which are capable of dealing with uncertainty, imprecision, and partial truth.

motivation > Reservoir Characterization

Ten-feet

One of inches

Fraction of inches

Integrating all different types of data in an accurate and high-resolution reservoir model

SEISMIC

WELL LOGS

CORES



- Due to its low resolution, seismic data is used only to attain a structural view of the reservoir.

- However, its 3D coverage over a large area attracts engineers to merge it more detailed characterization studies.

- Inverse modeling of reservoir properties from the seismic data is known as seismic inversion.

SEISMIC LOGS

1. Does a relationship exist between seismic data and reservoir characteristics, beyond the structural relationship?

2. If such a relationship exists, can it be extracted through the use of soft computing tools, such as artificial neural networks?

3. How that tool should be designed to develop the most reliable correlation models? i.e. neural network algorithm, number and type of seismic attributes

that should be included... etc.

Statement of the Problem

Previous Work

- In this study; vertical seismic profile (VSP) is incorporated into the study as the intermediate scale instead of cross-well seismic.

neural networkCross-well

seismic Gamma ray

logs

neural networkSurface seismic

neural networkSurface seismic

neural network

Well logs

Chawathe et. al (1997)

Reeves et. al (2002)

VSP

Vertical Seismic Profile (VSP)

VSP resolution ≈

2 * Surface seismic resolution

Source

Receivers (Geophones)

surface

- Signal receivers are located in the borehole instead of surface, both down-going and up-going signals are received.- Signal receivers are located in the borehole instead of surface, both down-going and up-going signals are received.- Signal receivers are located in the borehole instead of surface, both down-going and up-going signals are received.

Well

rock layer boundary

Statement of the Problem

- Using artificial neural networks is proposed to find a desirable correlation between well logs and seismic data. Generalized regression neural network (GRNN) algorithm is used.

- Vertical seismic profile (VSP) is incorporated into the study as the intermediate scale data.

- Another unique feature of this study was to develop and work on a synthetic model, before dealing with real data.

Two-step Correlation Methodology

Surface Seismic

Well Logs

VSP

Low frequency High frequencyMedium frequency

Step 1 Step 2

Two steps of correlation

1) Correlation of surface seismic with VSP

2) Correlation of VSP with well logs

Case 1

Synthetic Model

- The model represents the Pennsylvanian stratigraphy of the Buffalo Valley Field in New Mexico, including the gas-producing Atoka and Morrow formations.

- The geological complexity increases with depth; 0.8 – 1.124 sec. (6,600 – 9,000 ft) interval has been used.

- Surface seismic and VSP responses have been computed through a synthetic seismic line of 100 traces.

Description of the Model

Trace 20

Trace 50

( VSP well )

Trace 80

Description of the Model

A synthetic seismic line with 100 traces, having 3 wells @ traces 20, 50, and 80.

Available Data

1. Density and acoustic velocity distributions.

2. Surface seismic and VSP responses in the form of the following seismic attributes:

- Amplitude- Average energy- Envelope- Frequency- Hilbert transform- Paraphase- Phase

Seismic Amplitude Distribution

Case 1 – Synthetic Model

Step 1 Correlation of surface seismic with VSP

Step 2 Correlation of VSP with well logs


Case 1 _ Step 1 (Surface seismic VSP)

Trace 32Trace 57

Neural network design:

Case 1 _ Step 1( Surface seismic VSP)

neural network

Inputs Output

Time+

7 surface seismic

attributes

Single VSP attribute

Correlation Map

Surface Seismic

Well Logs

VSP

Step 1 Step 2

Model found

Surface Seismic

VirtualVSP

VirtualWell Logs

Seven separate prediction models have been developed for seven VSP attributes

with the data of traces 32 + 57.

Now, let’s apply these models to the other traces to have the predicted distributions.

Seven separate prediction models have been developed for seven VSP attributes

with the data of traces 32 + 57.

Now, let’s apply these models to the other traces to have the predicted distributions.


Actual Predicted

FREQUENCY


Actual Predicted

PHASE


Actual Predicted

HILBERT TRANSFORM


Actual Predicted

ENVELOPE

Correlation Map

Surface Seismic

Well Logs

VSP

Step 1 Step 2

Model found

Surface Seismic

VirtualVSP

VirtualWell Logs

Step 1 - ACCOMPLISHED !..Step 1 - ACCOMPLISHED !..

VirtualVSP

Case 1 – Synthetic Model


Step 2 Correlation of VSP with well logsStep 2 Correlation of VSP with well logs

- Density log has been selected as the target log, and data of t-50 have been used in building network models.

- Instead of using actual values, the problem was converted to a classification problem, because of observable averaged values of density log of t-50.

Case 1 _ Step 2 ( VSP Well Logs )

Class 1

ρ ≈ 1.9 g/cc

Class 2

ρ ≈ 2.3 g/cc

Class 3

ρ ≈ 2.65 g/cc

Class 1

Class 2

Class 3


Neural network design:

neural network

Inputs Outputs

Time+

7 VSP attributes

Three Classes of

Density


r2 = 0.82Class 1

ρ ≈ 1.9 g/cc

Class 2

ρ ≈ 2.3 g/cc

Class 3

ρ ≈ 2.65 g/cc


Class 1 Class 2

Class 3

Class 4

ρ ≈ 2.09 g/cc

Class 4r2 = 0.94


Correlation Map

Surface Seismic

Well Logs

VSP

Step 1 Step 2

Model found

Surface Seismic

VirtualVSP

VirtualWell Logs

The prediction model for density has been developed with the data of trace 50.

Now, we can generate the cross-sectional density distribution.

The prediction model for density has been developed with the data of trace 50.

Now, we can generate the cross-sectional density distribution.

Model found

Actual Predicted

DENSITY


Correlation Map

Surface Seismic

Well Logs

VSP

Step 1 Step 2

Model found

Surface Seismic

VirtualVSP

VirtualWell Logs

Model found

VirtualWell Logs


Case 2

Real CaseThe Buffalo Valley Field

The Buffalo Valley Field, New Mexico

Available Data

- Paper logs from around 40 wells within a 3D seismic survey area have been digitized.

- Only one well had a VSP survey, i.e. it’s the only well to build network models.

- Seismic data were loaned by WesternGeco; a total of 27 seismic attributes were available.

Map of Wells and Seismic Survey Area

VSP well

Seismic Amplitude Distribution

Well #1 ( VSP well )

Well #2 Well #3 Well #4 Well #5

Case 2 – Real Case: The B.Valley Field


Step 2 Correlation of VSP with well logs


Correlation Map

Surface Seismic

Well Logs

VSP

Step 1 Step 2

Model found

Surface Seismic

VirtualVSP

VirtualWell Logs

VirtualVSP

Case 2 – Real Case: The B. Valley Field


Step 2 Correlation of VSP with well logsStep 2 Correlation of VSP with well logs

- After a quality check of available logs, gamma ray and neutron porosity logs were selected as target logs, considering their availability, and quality.


- Data from all available wells were used in developing the neural network models.

- A ‘Key Performance Indicators’ (KPI) study was conducted to see influences of each seismic attribute on the target log.


Key Performance Indicators (KPI)

Intelligent Reservoir Characterization and Analysis (IRCA) software:

- Most influent attributes were selected due to large number of available attributes.

Gamma Ray Log

Well #1 r = 0.76

Well #2 r = 0.86

Well #3 r = 0.81

Well #4 r = 0.90

Well #5 r = 0.90

Gamma Ray Log

Neutron Porosity Log

Well #1 r = 0.98

Well #2 r = 0.97

Neutron Porosity Log

Correlation Map

Surface Seismic

Well Logs

VSP

Step 1 Step 2

Model found

Surface Seismic

VirtualVSP

VirtualWell Logs

Model found

VirtualWell Logs


Conclusions

- The proposed two-scale-step, intelligent seismic inversion methodology has been successfully developed on a synthetic model. The same methodology has then been applied to real data of the Buffalo Valley Field in New Mexico.

- Density logs for the synthetic model, and gamma ray logs for the field data have been produced from seismic data.

Conclusions

- The complex and non-linear relationships have been extracted with the power of artificial neural networks with both classification and prediction.

- A novel approach has been presented to solve an important data integration problem in reservoir characterization.

- The same methodology can be applied to a 3D seismic block to obtain 3D distributions of reservoir properties.

Acknowledgements

- This study was supported by the U.S. Department of Energy. Help and support of Mr. Thomas Mroz (project manager) is appreciated.

- Seismic data were used with the courtesy of WesternGeco.

- Mrs. Janaina Pereira’s help in digitizing well logs is also appreciated.

Acknowledgements

- This study was supported by the U.S. Department of Energy. Help and support of Mr. Thomas Mroz (project manager) is appreciated.

- Seismic data were used with the courtesy of WesternGeco.

- Mrs. Janaina Pereira’s help in digitizing well logs is also appreciated.

Reservoir Characterization Using Intelligent Seismic Inversion

ERM 2005 Morgantown, W.V. SPE Paper # 98012

September 15, 2005

Reservoir Characterization Using Intelligent Seismic...

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