Rock physics modelling and inversion for saturation‐pressure changes in time‐lapse seismic studies

Lang, Xiaozheng; Grana, Dario

doi:10.1111/1365-2478.12797

Cited by 13 publications

(13 citation statements)

References 63 publications

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“…We note that the model in [47] assumes that the coefficients in (25), which typically are calibrated against a few rock samples, are valid everywhere and independent of porosity. In [49], the author proposed an improved model, where they account for heterogeneous porosity, initial saturation, and pressure.…”

Section: Skade Formation and Synthetic Data Generationmentioning

confidence: 99%

Combining CSEM or gravity inversion with seismic AVO inversion, with application to monitoring of large-scale CO2 injection

et al. 2020

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A sequential inversion methodology for combining geophysical data types of different resolutions is developed and applied to monitoring of large-scale CO 2 injection. The methodology is a two-step approach within the Bayesian framework where lower resolution data are inverted first, and subsequently used in the generation of the prior model for inversion of the higher resolution data. For the application of CO 2 monitoring, the first step is done with either controlled source electromagnetic (CSEM) or gravimetric data, while the second step is done with seismic amplitude-versus-offset (AVO) data. The Bayesian inverse problems are solved by sampling the posterior probability distributions using either the ensemble Kalman filter or ensemble smoother with multiple data assimilation. A model-based parameterization is used to represent the unknown geophysical parameters: electric conductivity, density, and seismic velocity. The parameterization is well suited for identification of CO 2 plume location and variation of geophysical parameters within the regions corresponding to inside and outside of the plume. The inversion methodology is applied to a synthetic monitoring test case where geophysical data are made from fluid-flow simulation of large-scale CO 2 sequestration in the Skade formation. The numerical experiments show that seismic AVO inversion results are improved with the sequential inversion methodology using prior information from either CSEM or gravimetric inversion.

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Section: Skade Formation and Synthetic Data Generationmentioning

confidence: 99%

Combining CSEM or gravity inversion with seismic AVO inversion, with application to monitoring of large-scale CO2 injection

et al. 2020

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“…The termP eff;0 is a reference pressure, typically the maximal pressure. The second velocity-pressure dependency is based on the work of Lang and Grana (2019), further referred to as pressure dependence PL. Within this description, after defining ΔP ¼ P 1 − P 0 and for the gas saturation ΔS g ¼ S g 1 − S g 0 , V P and V S are dependent on ΔS g and ΔP showing a quadratic dependence on ϕ, whereas ρ is only dependent on ϕ and ΔS g ,…”

Section: Pressure Dependencementioning

confidence: 99%

“…Pride et al (1992) present explicit equations of motion as well as stress/strain relations in a dynamic two-phase porous medium consisting of a fluid and matrix. Extending the work from Landrø (2001), Lang and Grana (2019) present a Bayesian rock-physics inversion discriminating pore pressure and fluid effects. The twophase fluid distribution is frequently described by the Gassmann (1951)…”

Section: Introductionmentioning

confidence: 95%

Deep Learning a Poro-Elastic Rock Physics Model for Pressure and Saturation Discrimination

Weinzierl¹,

Wiese²

2020

First EAGE Digitalization Conference and Exhibition

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Determining saturation and pore pressure is relevant for hydrocarbon production as well as natural gas and CO 2 storage. In this context, seismic methods provide spatially distributed data used to determine gas and fluid migration. A method is developed that allows the determination of saturation and reservoir pressure from seismic data, more accurately from the rock-physics attributes of velocity, attenuation, and density. Two rock-physics models based on Hertz-Mindlin-Gassmann and Biot-Gassmann are developed. Both generate poroelastic attributes from pore pressure, gas saturation, and other rockphysics parameters. The rock-physics models are inverted with deep neural networks to derive saturation, pore pressure, and porosity from rock-physics attributes. The method is demonstrated with a 65 m deep unconsolidated high-porosity reservoir at the Svelvik ridge, Norway. Tests for the most suitable structure of the neural network are carried out. Saturation and pressure can be meaningfully determined under the condition of a gas-free baseline with known pressure and data from an accurate seismic campaign, preferably cross-well seismic. Including seismic attenuation increases the accuracy. Although training requires hours, predictions can be made in only a few seconds, allowing for rapid interpretation of seismic results.

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Section: Introductionmentioning

confidence: 95%

“…The structure of the paper follows the three steps of network selection, feasibility, and reservoir application, in which the state of the art is consecutively enhanced by our developments. pressure variation effects on the poroelastic attributes are typically not included in the rock-physics models, they are introduced into the appropriate formulations (Avseth et al, 2010;Lang and Grana, 2019).…”

Section: Introductionmentioning

confidence: 99%

Deep learning a poroelastic rock-physics model for pressure and saturation discrimination

Weinzierl¹,

Wiese²

2021

GEOPHYSICS

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Determining saturation and pore pressure is relevant for hydrocarbon production as well as natural gas and CO2 storage. In this context seismic methods provide spatially distributed data used to determine gas and fluid migration. A method is developed that allows to determine saturation and reservoir pressure from seismic data, more precisely from rock physical attributes that are velocity, attenuation and density. Two rock physical models based on Hertz-Mindlin-Gassmann and Biot-Gassmann are developed. Both generate poroelastic attributes from pore pressure, gas saturation and other rock-physical parameters. The rock physical models are inverted with deep neural networks to derive e.g. saturation, pore pressure and porosity from rock physical attributes. The method is demonstrated with a 65 m deep unconsolidated high porosity reservoir at the Svelvik ridge, Norway. Tests for the most suitable structure of the neural network are carried out. Saturation and pressure can be meaningfully determined under condition of a gas-free baseline with known pressure and data from an accurate seismic campaign, preferably cross-well seismic. Including seismic attenuation increases the accuracy. The training requires hours, predictions just a few seconds, allowing for rapid interpretation of seismic results.

show abstract

Rock physics modelling and inversion for saturation‐pressure changes in time‐lapse seismic studies

Cited by 13 publications

References 63 publications

Combining CSEM or gravity inversion with seismic AVO inversion, with application to monitoring of large-scale CO2 injection

Combining CSEM or gravity inversion with seismic AVO inversion, with application to monitoring of large-scale CO2 injection

Deep Learning a Poro-Elastic Rock Physics Model for Pressure and Saturation Discrimination

Deep learning a poroelastic rock-physics model for pressure and saturation discrimination

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