Wisam AlKawai, Tapan Mukerji and Stephan Graham · Personal Introduction Third year graduate...
Transcript of Wisam AlKawai, Tapan Mukerji and Stephan Graham · Personal Introduction Third year graduate...
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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