CO                                         <sub>2</sub> saturation, distribution and seismic response in 2D dimensional permeability model

Behzadi, Hamid; Alvarado, Vladimir; Padhi, Amit; Mallick, Subhashis

doi:10.1190/1.3628085

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…It has also been combined with production data for reservoir management (Nalonnil and Marion, 2010;Liang et al, 2011;Suman et al, 2011) as well as history matching (Jin et al, 2011;Kazemi and Stephen, 2010) and stratigraphic trap identification (Lertlamnaphakul et al, 2012). Trial studies have been carried out to explore the possibility of predicting permeability from seismic data (Behzadi et al, 2011;Liang et al, 2011). The latter is a major motivation for this study.…”

Section: Conventional Approach To Permeability Predictionmentioning

confidence: 99%

Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models

Anifowose

Adeniye

Abdulraheem

et al. 2016

Journal of Petroleum Science and Engineering

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Section: Conventional Approach To Permeability Predictionmentioning

confidence: 99%

Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models

Anifowose

Adeniye

Abdulraheem

et al. 2016

Journal of Petroleum Science and Engineering

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“…In general, the modulus and elastic properties (such as AI) of the solid phase and dry rock and fluid phase may be calculated via the Voigt-Reuss-Hill model (Mavko et al, 2009), Wood's equation (Mavko et al, 2009), and the Hertz-Mindlin model (Dadashpour et al, 2007), for given reservoir parameters such as porosity, mineral content, and water saturation. Compressional wave velocity (P wave) is a function of bulk and shear moduli, as well as bulk density (Behzadi et al, 2011). Below, we briefly describe the RPMs used in this work.…”

Section: Rpmmentioning

confidence: 99%

Inversion of Time‐Lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning‐Based Approach for Estimating Dynamic Reservoir Property Changes

Zhi

Sun

2020

JGR Solid Earth

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Carbon capture and storage is being pursued globally as a geoengineering measure for reducing the emission of anthropogenic CO2 into the atmosphere. Comprehensive monitoring, verification, and accounting programs must be established for demonstrating the safe storage of injected CO 2. One of the most commonly deployed monitoring techniques is time‐lapse seismic reservoir monitoring (also known as 4‐D seismic), which involves comparing 3‐D seismic survey data taken at the same study site but over different times. Analyses of 4‐D seismic data volumes can help improve the quality of storage reservoir characterization, track the movement of injected CO 2 plume, and identify potential CO 2 spillover/leakage from the storage reservoirblue. However, the derivation of high‐resolution CO 2 saturation maps from 4‐D seismic data is a highly nonlinear and ill‐posed inverse problem, often requiring significant computational effort. In this research, we apply a physics‐based deep learning method to facilitate the solution of both the forward and inverse problems in seismic inversion while honoring physical constraints. A cycle generative adversarial neural network (CycleGAN) model is trained to learn the bidirectional functional mappings between the reservoir dynamic property changes and seismic attribute changes, such that both forward and inverse solutions can be obtained efficiently from the trained model. We show that our CycleGAN‐based approach not only improves the reliability of 4‐D seismic inversion but also expedites the quantitative interpretation. Our deep learning‐based workflow is generic and can be readily used for reservoir characterization and reservoir model updates involving the use of 4‐D seismic data.

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“…Domenico (1976) suggested that mixed gas-oil-brine saturations can also be modeled with Gassmann's relations, if the mixture of phases is replaced by a single fluid phase with effective (equivalent) bulk modulus. The effective bulk modulus of immiscible pore fluids is not well defined and remains an active topic of the research (e.g., Amalokwu et al, 2017;Behzadi et al, 2011;Brie et al, 1995;Cadoret, 1993;Domenico, 1976;Dvorkin & Wollner, 2017;Monachesi et al, 2020;Müller et al, 2008;Papageorgiou et al, 2016;Papageorgiou et al, 2018;Rozhko & Bauer, 2019;Rozhko, 2019;Wollner & Dvorkin, 2016;Wollner & Dvorkin, 2018a, 2018b. According to Mavko and Mukerji (1998), it is possible to define the upper and lower bounds for the effective bulk modulus of the pore fluid mixture.…”

Section: Introductionmentioning

confidence: 99%

Effective Fluid Bulk Modulus in the Partially Saturated Rock and the Amplitude Dispersion Effects

Rozhko

2020

JGR Solid Earth

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Frequency dispersion is a well‐known effect in geophysics, which means that waves of different wavelengths propagate at different velocities. Amplitude dispersion is a less‐known effect, which means that waves of different amplitudes propagate at different velocities. Herewith, we consider the alteration of the interfacial energy during wave‐induced two‐phase fluid flow in a partially saturated rock and demonstrate that this leads to a nonlinear amplitude dispersion effect. When the wave amplitude is small, seismic waves cause bending of the interface menisci between immiscible fluids at the pore scale. However, when the wave amplitude is sufficiently large, the interface menisci will slip at the pore scale, causing attenuation of the elastic energy by the contact line friction mechanism. At the zero frequency limit, all viscous dissipation models predict zero attenuation of the elastic wave energy, while this approach predicts a nonzero attenuation due to a static contact angle hysteresis effect. Herein, we extend the Gassmann's theory with three extra terms, which can be obtained from standard laboratory tests: pore‐size distribution and interfacial tension between immiscible fluids and rock wettability (advancing and receding contact angles). We derive closed‐form analytical expressions predicting the effective fluid modulus in partially saturated rock, which falls between Voigt and Reuss averages. Next, we demonstrate that the nonlinear amplitude dispersion effect leads to energy transfer between different frequencies. This may explain the low‐frequency microtremor anomalies, frequently observed above hydrocarbon reservoirs, when the low‐frequency energy of ocean waves (0.1–1 Hz) is converted to higher frequencies (2–6 Hz) by partially saturated reservoirs.

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CO ₂ saturation, distribution and seismic response in 2D dimensional permeability model

Cited by 7 publications

References 11 publications

Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models

Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models

Inversion of Time‐Lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning‐Based Approach for Estimating Dynamic Reservoir Property Changes

Effective Fluid Bulk Modulus in the Partially Saturated Rock and the Amplitude Dispersion Effects

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CO 2 saturation, distribution and seismic response in 2D dimensional permeability model

Cited by 7 publications

References 11 publications

Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models

Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models

Inversion of Time‐Lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning‐Based Approach for Estimating Dynamic Reservoir Property Changes

Effective Fluid Bulk Modulus in the Partially Saturated Rock and the Amplitude Dispersion Effects

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CO ₂ saturation, distribution and seismic response in 2D dimensional permeability model