Wisam AlKawai, Tapan Mukerji and Stephan Graham

Basin and Petroleum System Modeling Industrial Affiliates Program

Basin and Petroleum System Modeling Affiliate Meeting November, 12, 2014

Personal Introduction

Third year graduate student.

Advisors: Stephan Graham and Tapan Mukerji.

Research Interests: Basin and Petroleum System Modeling,

Rock Physics and Quantitative Seismic Interpretation.

B.S. in Geophysics – University of Houston (2010).

Geophysicist – Saudi Aramco (2010-2012).

M.S. in Geological and Environmental Sciences- Stanford

University ( 2012-2014).

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Data and Study Area

Partial subset of the E-Dragon II Data in the Gulf of Mexico.

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Motivation

• Calibrating basin models with seismic attributes (i.e.

seismic velocities) that are spatially extensive.

• Constraining impedance background models for

seismic inversion with basin modeling outputs.

Calibrate How ??

Constrain How ??

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Calibration to Seismic Velocities

Providing complementary calibration that is

spatially extensive beyond the borehole vicinity.

Combining certain basin modeling outputs with

appropriate rock physics models.

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Calibrate How ??

Constraining Impedance Background

Models

Useful when well-log data are very sparse or

absent.

Based on basin modeling estimates of impedance.

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Constrain How ??

Workflow

Rock Physics

Modeling

Vp-Porosity models

Vp-effective stress models

Vp-Vs models

1D Basin Models

Calibration to porosity and

drilling mud weight data

Different rock properties outputs

(i.e. porosity, effective stress and

density)

Seismic velocities outputs

Seismic Inversion

Impedance background model

seismic impedance cube

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Vp-Porosity Modeling I

Vp-Porosity models above 8000 ft at well SS-187 using constant

cement model (Avseth et al., 2001).

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Vp-Porosity Modeling II

Sand facies are modeled using Han’s Model ( Han, 1986).

Shale facies are modeled using friable sand model ( Dvorkin and

Nur, 1996).

Han’s Lines

Friable

Sand

model

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Vp-Effective Stress Modeling

Vp normal compaction trends were modeled for clean sandstone

and shale using the compaction trends by Dutta et al. (2009).

Shale Clean

Sandstone

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Vp-Vs Modeling

Clean Sandstone is best fit with Castagna (1993) relationship:

Vs= 0.8042 Vp – 0.8559 (km/s)

Shaly sandstone and shale are best fit with the mudrock line of Castagna et al.

(1985):

Vs= 0.8621 Vp – 1.1724 (km/s)

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Basin Modeling Input

1D basin models at the location of wells SS-

187 and SS-160.

Age control input is based on

biostratigraphic data and interpretation of

seismic data.

Lithofacies input is based on Vshale.

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Traditional Basin Models Calibration

Calibrating 1D models to porosity and drilling mud

weight data.

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Basin Modeling Default Velocity

Outputs I Default velocity outputs built in the software (Petromod) based on

Terzaghi’s (1923) compressibility model:

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Vp Output I Vs Porosity Output

Plotted at well SS-187 and compared with other established Vp-

porosity models.

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Basin Modeling Velocity Outputs II

Combining porosity outputs with Vp-porosity rock

physics models.

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Basin Modeling Velocity Outputs III

Combining stresses outputs with Vp normal compaction

trends using Eaton’s (1975) equation:

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Seismic Inversion

Partial Angle Stack inversion of near angle data (0˚-16 ˚) at

the Pliocene Zone.

Sparse Spike Algorithm.

Based on Connolly’s (1999) equation:

Where:

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Base Case Inversion Result

Background model from well-log data at two

wells.

average cross correlation coefficient = 0.91

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Near Angle EI Background Model I

Constrained to basin modeling density outputs along

with the default velocity outputs.

average cross correlation coefficient = 0.29

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Near Angle EI Background Models

Model II: conditioned to velocity obtained using Vp-

porosity models

Model III: conditioned to velocity obtained using Vp-

effective stress models

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Inversion Result

Using background model II.

average cross correlation coefficient = 0.85

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Conclusions

Seismic attributes potential tools for complementary

calibration of basin models.

Basin modeling constraints for the background

models for seismic inversion.

Refining the link between basin modeling and

seismic technology requires good rock physics.

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Acknowledgements

Thanks to Saudi Aramco for sponsoring my graduate studies.

Thanks to Stanford Basin and Petroleum System (BPSM), Stanford Center for Reservoir Forecasting (SCRF) and Stanford Rock Physics (SRB) industrial affiliate research programs.

Thanks to WesternGeCo/Schlumberger for providing the seismic data set.

Thanks to IHS “well-log data Copyright (2013) IHS Energy Log Services Inc.”

Thanks to CGG for providing the license of HRS.

Great thanks to David Greeley from BP for his great support.

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References

Avseth, Per, Dvorkin, Jack, Mavko, Gary, & Rykkje, Johannes. 2000. Rock physics diagnostics of North Sea sands: Link between microstructure and seismic properties.Geophys. Res. Lett., 27, 2761–2764.

Castagna, J. P., M. L. Batzle, and R. L. Eastwood, 1985, Relationships between compressional- wave and shear-wave velocities inclastic silicate rocks: Geophysics, 50, 571–581.

Castagna, J. P., Batzle, M. L., and Kan, T. K., 1993, Rock physics--The link between rock properties and AVO response in John P. Castagna and Milo M. Backus, Eds., Offset dependent reflectivity -- theory and practice of AVO analysis: Investigations in Geophysics Series, Soc. Expl. Geophys., 8, 135-171.

Connolly, P., 1999, Elastic impedance: The Leading Edge, 18, 438–452.

Dutta, T., Mavko, G., Mukerji, T. and Lane, T. 2009, Compaction trends for shale and clean sandstone in shallow sediments, Gulf of Mexico: The Leading Edge, 28, No.5, 590-596.

Dvorkin, J., and A. Nur, 1996, Elasticity of high-porosity sandstones: Theory for two North Sea datasets,Geophysics, 61, 1363-1370.

Eaton, B. A., 1975, The equation for geopressure prediction from welllogs: SPE 5544.

Han, D., 1986, Effects of porosity and clay content on acoustic properties of sandstones and Unconsolidated sediments: Ph.D. dissertation, Stanford University.

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Basin and Petroleum System Modeling Industrial Affiliates Program

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