ERM 2005 ERM 2005 Morgantown, W.V. Morgantown, W.V. SPE Paper # - - PowerPoint PPT Presentation

Reservoir Characterization Using Reservoir Characterization Using Intelligent Seismic Inversion Intelligent Seismic Inversion

Emre Artun, WVU Shahab D. Mohaghegh, WVU Jaime Toro, WVU Tom Wilson, WVU Alejandro Sanchez, Anadarko

September 15, 2005

ERM 2005 ERM 2005

Morgantown, W.V. Morgantown, W.V.

SPE Paper # 98012 SPE Paper # 98012

motivation > motivation > Reservoir Modeling Workflow

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

• f 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 > motivation > Reservoir Characterization

Reservoir Characterization

Ten-feet One of inches Fraction of inches

Integrating all different types

• f data in an accurate and

high-resolution reservoir model

SEISMIC WELL LOGS CORES

motivation > motivation > Reservoir Characterization

Reservoir Characterization

motivation > motivation > Reservoir Characterization

Reservoir Characterization

• 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 Statement of the Problem

Previous Work Previous Work

• In this study; vertical seismic profile (VSP) is incorporated

into the study as the intermediate scale instead of cross-well seismic.

neural network

Cross-well seismic Gamma ray logs

neural network

Surface seismic

neural network

Surface seismic

neural network

Well logs

Chawathe et. al (1997) Reeves et. al (2002)

VSP

Vertical Seismic Profile (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 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 Two-step Correlation Methodology

Surface Seismic Well Logs VSP

L

• w fre que nc y

H igh fre que nc y Medium frequenc y Ste p 1 Ste p 2

Two steps of correlation 1) Correlation of surface seismic with VSP 2) Correlation of VSP with well logs

Case 1 Case 1

Synthetic Model 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 Description of the Model

Trace 20 Trace 50 ( VSP well ) Trace 80

Description of the Model Description of the Model

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

Available Data 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 Seismic Amplitude Distribution

Case 1 – Case 1 – Synthetic Model ynthetic Model

Step 1

Correlation of surface seismic with VSP

Step 2

Correlation of VSP with well logs

Step 1

Correlation of surface seismic with VSP

Case 1 _ Step 1 (Surface seismic VSP) Case 1 _ Step 1 (Surface seismic VSP)

T r a c e 3 2 Trace 57

Neural network design:

Case 1 _ Step 1( Surface seismic VSP) Case 1 _ Step 1( Surface seismic VSP)

neural network

Inputs Output

Time + 7 surface seismic attributes Single VSP attribute

Case 1 _ Step 1 (Surface seismic VSP) Case 1 _ Step 1 (Surface seismic VSP)

Correlation Map Correlation Map

Surface Seismic Well Logs VSP

Ste p 1 Ste p 2

Mo de l fo und Surface Seismic Virtual VSP Virtual Well 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.

Case 1 _ Step 1 (Surface seismic VSP) Case 1 _ Step 1 (Surface seismic VSP)

Actual Predicted

FREQUENCY

Case 1 _ Step 1 (Surface seismic VSP) Case 1 _ Step 1 (Surface seismic VSP)

Actual Predicted

PHASE

Case 1 _ Step 1 (Surface seismic VSP) Case 1 _ Step 1 (Surface seismic VSP)

Actual Predicted

HI LBERT TRANSFORM

Case 1 _ Step 1 (Surface seismic VSP) Case 1 _ Step 1 (Surface seismic VSP)

Actual Predicted

ENVELOPE

Correlation Map Correlation Map

Surface Seismic Well Logs VSP

Ste p 1 Ste p 2

Mo de l fo und Surface Seismic Virtual VSP Virtual Well Logs Step 1 - ACCOMPLISHED !.. Step 1 - ACCOMPLISHED !.. Virtual VSP

Case 1 – Case 1 – Synthetic Model ynthetic Model

Step 1

Correlation of surface seismic with VSP

Step 2

Correlation of VSP with well logs

Step 2

Correlation of VSP with well logs

• Density log has been selected as the target log, and data
• f 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 ) 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

Case 1 _ Step 2 ( VSP Well Logs ) Case 1 _ Step 2 ( VSP Well Logs )

Neural network design:

neural network

Inputs Outputs

Time + 7 VSP attributes Three Classes of Density

Case 1 _ Step 2 ( VSP Well Logs ) Case 1 _ Step 2 ( VSP Well Logs )

r2 = 0.82

Class 1 ρ ≈ 1.9 g/cc Class 2 ρ ≈ 2.3 g/cc Class 3 ρ ≈ 2.65 g/cc

Case 1 _ Step 2 ( VSP Well Logs ) Case 1 _ Step 2 ( VSP Well Logs )

Class 1 Class 2 Class 3 Class 4 ρ ≈ 2.09 g/cc Class 4

r2 = 0.94

Case 1 _ Step 2 ( VSP Well Logs ) Case 1 _ Step 2 ( VSP Well Logs )

Correlation Map Correlation Map

Surface Seismic Well Logs VSP

Ste p 1 Ste p 2

Mo de l fo und Surface Seismic Virtual VSP Virtual Well 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. Mo de l fo und

Actual Predicted

DENSI TY

Case 1 _ Step 2 ( VSP Well Logs ) Case 1 _ Step 2 ( VSP Well Logs )

Case 1 _ Step 2 ( VSP Well Logs ) Case 1 _ Step 2 ( VSP Well Logs )

Correlation Map Correlation Map

Surface Seismic Well Logs VSP

Ste p 1 Ste p 2

Mo de l fo und Surface Seismic Virtual VSP Virtual Well Logs Mo de l fo und Virtual Well Logs Step 2 - ACCOMPLISHED !.. Step 2 - ACCOMPLISHED !..

Case 2 Case 2

Real Case Real Case The Buffalo Valley Field The Buffalo Valley Field

The Buffalo Valley Field, New Mexico The Buffalo Valley Field, New Mexico

Available Data 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 Map of Wells and Seismic Survey Area

VSP well

Seismic Amplitude Distribution Seismic Amplitude Distribution

Well #1

( VSP well )

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

Case 2 – Case 2 – Real Case: The B.Valley Field eal Case: The B.Valley Field

Step 1

Correlation of surface seismic with VSP

Step 2

Correlation of VSP with well logs

Step 1

Correlation of surface seismic with VSP

Case 2 _ Step 1 (Surface seismic VSP) Case 2 _ Step 1 (Surface seismic VSP)

Case 2 _ Step 1 (Surface seismic VSP) Case 2 _ Step 1 (Surface seismic VSP)

Correlation Map Correlation Map

Surface Seismic Well Logs VSP

Ste p 1 Ste p 2

Mo de l fo und Surface Seismic Virtual VSP Virtual Well Logs Virtual VSP

Case 2 – Case 2 – Real Case: The B. Valley Field eal Case: The B. Valley Field

Step 1

Correlation of surface seismic with VSP

Step 2

Correlation of VSP with well logs

Step 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.

Case 2 _ Step 2 ( VSP Well Logs ) Case 2 _ Step 2 ( VSP Well Logs )

• 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.

Case 2 _ Step 2 ( VSP Well Logs ) Case 2 _ Step 2 ( VSP Well Logs )

Key Performance Indicators (KPI) Key Performance Indicators (KPI)

Intelligent Reservoir Characterization and Analysis (IRCA) software:

• Most influent attributes were selected due to large number
• f available attributes.

Gamma Ray Log 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 Gamma Ray Log

Neutron Porosity Log Neutron Porosity Log

Well #1

r = 0.98

Well #2

r = 0.97

Neutron Porosity Log Neutron Porosity Log

Correlation Map Correlation Map

Surface Seismic Well Logs VSP

Ste p 1 Ste p 2

Mo de l fo und Surface Seismic Virtual VSP Virtual Well Logs Mo de l fo und Virtual Well Logs Step 2 - ACCOMPLISHED !.. Step 2 - ACCOMPLISHED !..

Conclusions 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 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
• f 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
• f 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 Reservoir Characterization Using Intelligent Seismic Inversion Intelligent Seismic Inversion

ERM 2005 ERM 2005

Morgantown, W.V. Morgantown, W.V.

SPE Paper # 98012 SPE Paper # 98012

September 15, 2005