Seismic data may be inspected and interpreted on its own without inversion, but this does not provide the most detailed view of the subsurface and can be misleading under certain conditions. Because of its efficiency and quality, most oil and gas companies now use seismic inversion to increase the resolution and reliability of the data and to improve estimation of rock properties including porosity and net pay.Pendrel, J., "Seismic Inversion—A Critical Tool in Reservoir Characterization", Scandinavian Oil-Gas Magazine, No. 5/6, 2006, p. 19-22 . Seismic data is band-limited, reducing resolution and quality. To extend the frequency band available, low-frequency data is derived from log data, pre-stack depth or time migrated velocities and/or a regional gradient.Latimer, R., Davison, R., van Riel, P., "An Interpreter’s Guide to Understanding and Working with Seismic-Derived Acoustic Impedance Data", The Leading Edge, March 2000, p. 242-256. High frequency can be derived from well control or geostatistical analysis. The inversion algorithm works by first estimating angle-dependent P-wave reflectivities for the input-partial stacks. Next, these are used with the full Zoeppritz equations (or approximations, such as Aki-Richards , for some algorithms) to find band-limited elastic reflectivities. These are in turn merged with their low-frequency counterparts from the model and integrated to elastic properties. This approximate result is then improved in a final inversion for P-impedance, S-impedance and density, subject to various hard and soft constraints. One constraint can control the relation between density and compressional velocity; this is necessary when the range of angles is not great enough to be diagnostic of density. The resulting elastic parameters are real-rock properties that can be directly related to reservoir properties. The more advanced algorithms use the full Knott-Zoeppritz equations and there is full allowance for amplitude and phase variations with offset. This is done by deriving unique wavelets for each input-partial stack. The elastic parameters themselves can be directly constrained during the seismic inversion and rock-physics relationships can be applied, constraining pairs of elastic parameters to each other. Final elastic-parameter models optimally reproduce the input seismic, as this is part of the seismic inversion optimization. Further reading Caulfield, C., Feroci, M., Yakiwchuk, K. "Seismic Inversion for Horizontal Well Planning in Western Saskatchewan", Evolving Geophysics Through Innovation, p. 213-214. Chakrabarty, C., Fossey, J., Renard, G., Gadelle, C. "SAGD Process in the East Senlac Field: From Reservoir Characterization to Field Application", No. 1998.192. Contreras, A., Torres-Verdin, C., Chesters, W., Kvien, K., Globe, M., "Joint Stochastic Inversion of Petrophysical Logs and 3D Pre-Stack Seismic Data to Assess the Spatial Continuity of Fluid Units Away from Wells: Application to a Gulf-of-Mexico Deepwater Hydrocarbon Reservoir", SPWLA 46th Annual Logging Symposium, June 26–29, 2005. De Barros, Dietrich, M., "Full Waveform Inversion of Shot Gathers in Terms of Poro-elastic Parameters", EAGE, London, June 2007. Deutsch, C., Geostatistical Reservoir Modeling, New York: Oxford University Press, 2002, 376 pages. Francis, A., "Limitations of Deterministic and Advantages of Stochastic Seismic Inversion", CSEG Recorder, February 2005, p . 5-11. Hasanusi, D., Adhitiawan, E., Baasir, A., Lisapaly, L., van Eykenhof, R., "Seismic Inversion as an Exciting Tool to Delineate Facies Distribution in Tiaka Carbonate Reservoirs, Sulawesi – Indonesia", Proceedings, Indonesian Petroleum Association, Thirty-First Annual Convention and Exhibition, May 2007. Russell, B., Hampson, D., "The Old and the New in Seismic Inversion", CSEG Recorder, December 2006, p. 5-11. Stephen, K., MacBeth, C., "Reducing Reservoir Prediction Uncertainty by Updating a Stochastic Model Using Seismic History Matching", SPE Reservoir Evaluation & Engineering, December 2008. Vargas-Meleza, L., Megchun, J., Vazquez, G., "Petrophysical Properties Estimation by Integrating AVO, Seismic Inversion and Multiattribute Analysis in a 3-D Volume of Playuela, Veracruz", AAPG International Conference: October 24–27, 2004, Cancun, Mexico. Wang, X., Wu, S., Xu, N., Zhang, G., "Estimation of Gas Hydrate Saturation Using Constrained Sparse Spike Inversion: Case Study from the Northern South China Sea", Terr. Atmos. Ocean. Sci., Vol. 17, No. 4, 799-813 , December 2006. Watson, I., Lines, L., "Seismic Inversion at Pike’s Peak, Saskatchewan", CREWES Research Report, Volume 12, 2000. Whitfield, J., "The Relation of Net Pay to Amplitude Versus Offset Gradients: A Gulf of Mexico Case Study", University of Houston Master's Thesis, 1993. Zou, Y., Bentley, L., Lines, L., "Integration of Reservoir Simulation with Time-Lapse Seismic Modeling", 2004 CSEG National Convention.
Seismic data may be inspected and interpreted on its own without inversion, but this does not provide the most detailed view of the subsurface and can be misleading under certain conditions. Because of its efficiency and quality, most oil and gas companies now use seismic inversion to increase the resolution and reliability of the data and to improve estimation of rock properties including porosity and net pay.Pendrel, J., "Seismic Inversion—A Critical Tool in Reservoir Characterization", Scandinavian Oil-Gas Magazine, No. 5/6, 2006, pp. 19–22 . Seismic data is band-limited, reducing resolution and quality. To extend the frequency band available, low-frequency data is derived from log data, pre-stack depth or time migrated velocities and/or a regional gradient.Latimer, R., Davison, R., van Riel, P., "An Interpreter’s Guide to Understanding and Working with Seismic-Derived Acoustic Impedance Data", The Leading Edge, March 2000, pp. 242–256. High frequency can be derived from well control or geostatistical analysis. The inversion algorithm works by first estimating angle-dependent P-wave reflectivities for the input-partial stacks. Next, these are used with the full Zoeppritz equations (or approximations, such as Aki–Richards , for some algorithms) to find band-limited elastic reflectivities. These are in turn merged with their low-frequency counterparts from the model and integrated to elastic properties. This approximate result is then improved in a final inversion for P-impedance, S-impedance and density, subject to various hard and soft constraints. One constraint can control the relation between density and compressional velocity; this is necessary when the range of angles is not great enough to be diagnostic of density. The resulting elastic parameters are real-rock properties that can be directly related to reservoir properties. The more advanced algorithms use the full Knott–Zoeppritz equations and there is full allowance for amplitude and phase variations with offset. This is done by deriving unique wavelets for each input-partial stack. The elastic parameters themselves can be directly constrained during the seismic inversion and rock-physics relationships can be applied, constraining pairs of elastic parameters to each other. Final elastic-parameter models optimally reproduce the input seismic, as this is part of the seismic inversion optimization. Further reading Caulfield, C., Feroci, M., Yakiwchuk, K. "Seismic Inversion for Horizontal Well Planning in Western Saskatchewan", Evolving Geophysics Through Innovation, pp. 213–214. Chakrabarty, C., Fossey, J., Renard, G., Gadelle, C. "SAGD Process in the East Senlac Field: From Reservoir Characterization to Field Application", No. 1998.192. Contreras, A., Torres-Verdin, C., Chesters, W., Kvien, K., Globe, M., "Joint Stochastic Inversion of Petrophysical Logs and 3D Pre-Stack Seismic Data to Assess the Spatial Continuity of Fluid Units Away from Wells: Application to a Gulf-of-Mexico Deepwater Hydrocarbon Reservoir", SPWLA 46th Annual Logging Symposium, June 26–29, 2005. De Barros, Dietrich, M., "Full Waveform Inversion of Shot Gathers in Terms of Poro-elastic Parameters", EAGE, London, June 2007. Deutsch, C., Geostatistical Reservoir Modeling, New York: Oxford University Press, 2002, 376 pages. Francis, A., "Limitations of Deterministic and Advantages of Stochastic Seismic Inversion", CSEG Recorder, February 2005, pp . 5-11. Hasanusi, D., Adhitiawan, E., Baasir, A., Lisapaly, L., van Eykenhof, R., "Seismic Inversion as an Exciting Tool to Delineate Facies Distribution in Tiaka Carbonate Reservoirs, Sulawesi – Indonesia", Proceedings, Indonesian Petroleum Association, Thirty-First Annual Convention and Exhibition, May 2007. Russell, B., Hampson, D., "The Old and the New in Seismic Inversion", CSEG Recorder, December 2006, pp. 5–11. Stephen, K., MacBeth, C., "Reducing Reservoir Prediction Uncertainty by Updating a Stochastic Model Using Seismic History Matching", SPE Reservoir Evaluation & Engineering, December 2008. Vargas-Meleza, L., Megchun, J., Vazquez, G., "Petrophysical Properties Estimation by Integrating AVO, Seismic Inversion and Multiattribute Analysis in a 3-D Volume of Playuela, Veracruz", AAPG International Conference: October 24–27, 2004, Cancun, Mexico. Wang, X., Wu, S., Xu, N., Zhang, G., "Estimation of Gas Hydrate Saturation Using Constrained Sparse Spike Inversion: Case Study from the Northern South China Sea", Terr. Atmos. Ocean. Sci., Vol. 17, No. 4, 799–813 , December 2006. Watson, I., Lines, L., "Seismic Inversion at Pike’s Peak, Saskatchewan", CREWES Research Report, Volume 12, 2000. Whitfield, J., "The Relation of Net Pay to Amplitude Versus Offset Gradients: A Gulf of Mexico Case Study", University of Houston Master's Thesis, 1993. Zou, Y., Bentley, L., Lines, L., "Integration of Reservoir Simulation with Time-Lapse Seismic Modeling", 2004 CSEG National Convention.
R
p. 19-22
pp. 19–22
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p. 242-256.
pp. 242–256.
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Aki-Richards
Aki–Richards
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Knott-Zoeppritz
Knott–Zoeppritz
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p. 213-214.
pp. 213–214.
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p
pp
R
p. 5-11.
pp. 5–11.
R
799-813
799–813