Wave Propagation in Infinituple‐Porosity Media

Zhang, Lin; Ba, Jing; Carcione, José M.

doi:10.1029/2020jb021266

Cited by 48 publications

(21 citation statements)

References 94 publications

(155 reference statements)

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“…Figure 10 shows that the static bulk modulus is almost lower than the dynamic bulk modulus for unconsolidated sands, regardless of the room-dry or brine-saturated state. However, the magnitude of the static-dynamic difference is highly associated with the differential pressure, saturation, frequency, and sample porosity [11,18,21,23,28,53]. To quantify the effects of the above factors on the relationship between static and dynamic bulk modulus, we derive the fitted static-to-dynamic bulk modulus ratios.…”

Section: Relationship Between Static and Dynamic Bulk Modulusmentioning

confidence: 99%

Static and Dynamic Bulk Moduli of Deepwater Reservoir Sands: Influence of Pressure and Fluid Saturation

Wang

Han

et al. 2022

Lithosphere

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The static bulk modulus of unconsolidated sands is essential for predicting the in situ effective pressure to reduce drilling risks in deepwater reservoirs; however, the dynamic bulk modulus is often more broadly available from seismic or well logging data. Therefore, it is tempting to investigate the relationship between static and dynamic bulk moduli. We perform a series of ultrasonic velocity measurements on 21 deepwater reservoir sands from the Gulf of Mexico (GoM) to study the static and dynamic bulk moduli simultaneously. Both room-dry and brine-saturated ultrasonic velocities are measured under hydrostatic stress conditions to derive the dynamic bulk moduli. Under brine-saturated conditions, if the pore pressure is kept constant, the pore volume change with the confining pressure can be monitored accurately by a digital pump, which is subsequently used to estimate the static bulk modulus. The experimental results suggest that both the static and dynamic bulk moduli decrease upon pressure unloading. The pressure-dependent bulk moduli are modeled using the Hertz-Mindlin contact theory at the critical porosity and combined with the modified Hashin-Shtrikman lower bound for other porosities. The results suggest that the theoretical estimates can serve as the lower bound for the dynamic bulk modulus and the upper bound for the static bulk modulus. Under room-dry conditions, the static-to-dynamic modulus ratio decreases from a value approaching 0.8 to approximately 0.25 with decreasing differential pressure. Moreover, the effects of brine saturation on the relationship between the static and dynamic moduli are investigated using Gassmann’s equation. The brine saturation substantially reduces the difference between the static and dynamic bulk moduli, making the static-to-dynamic modulus ratio approach unity, which may be relevant to the in situ reservoir rock properties.

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Section: Relationship Between Static and Dynamic Bulk Modulusmentioning

confidence: 99%

Static and Dynamic Bulk Moduli of Deepwater Reservoir Sands: Influence of Pressure and Fluid Saturation

Wang

Han

et al. 2022

Lithosphere

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“…(2017) and Zhang et al. (2021) presented a double double‐porosity model, where the heterogeneities include the pore structure and patchy saturation, and W. Sun et al. (2018) proposed a three‐layer ellipsoidal fluid patch based on the BR model.…”

Section: Introductionmentioning

confidence: 99%

Data‐Driven Design of Wave‐Propagation Models for Shale‐Oil Reservoirs Based on Machine Learning

Xiong

Gei

et al. 2021

JGR Solid Earth

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The exploration and exploitation of shale oil is an important aspect in the oil industry. Seismic properties and well‐log data are essential to establish wave‐propagation models. Specifically, the description of wave dispersion and attenuation under complex geological conditions needs proper lithological and petrophysical information. This complex physical mechanism has to be considered if a traditional modeling approach is adopted. In this sense, machine learning (ML) techniques provide new possibilities for this purpose. We compare two deep‐neural‐network (DNN)‐based wave propagation models. In the first (pure data‐driven), a DNN is trained to connect seismic attributes, such as wave velocities, to multivariate functions of rock‐physics properties. By training DNNs with different initial parameters, the uncertainty of the proposed method can be quantified. The second method assumes the form of the wave equations. Then, the elastic constants of the constitutive relations are predicted by DNNs. The resulting dynamical equations describe the dispersion and attenuation and wavefield simulations can be performed to obtain more information. On the basis of a test, the two kinds of wave‐propagation models yield acceptable estimations of the seismic properties, with the second approach showing a broader application because the DNN is trained without S wave data. The methodologies illustrate that the new wave‐propagation model based on ML has high precision and can be general in terms of rheological description.

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“…Cheng [16] studied the effect of effective pressure and fluids. Zhang et al [17,18] proposed a differential poroelastic model to describe wave propagation and dissipation in fluid-saturated rocks which contain inclusions at multiple scales.…”

Section: Introductionmentioning

confidence: 99%

Microcrack Porosity Estimation Based on Rock Physics Templates: A Case Study in Sichuan Basin, China

Ruan

Carcione

et al. 2021

Energies

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Low porosity-permeability structures and microcracks, where gas is produced, are the main characteristics of tight sandstone gas reservoirs in the Sichuan Basin, China. In this work, an analysis of amplitude variation with offset (AVO) is performed. Based on the experimental and log data, sensitivity analysis is performed to sort out the rock physics attributes sensitive to microcrack and total porosities. The Biot–Rayleigh poroelasticity theory describes the complexity of the rock and yields the seismic properties, such as Poisson’s ratio and P-wave impedance, which are used to build rock-physics templates calibrated with ultrasonic data at varying effective pressures. The templates are then applied to seismic data of the Xujiahe formation to estimate the total and microcrack porosities, indicating that the results are consistent with actual gas production reports.

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Wave Propagation in Infinituple‐Porosity Media

Cited by 48 publications

References 94 publications

Static and Dynamic Bulk Moduli of Deepwater Reservoir Sands: Influence of Pressure and Fluid Saturation

Static and Dynamic Bulk Moduli of Deepwater Reservoir Sands: Influence of Pressure and Fluid Saturation

Data‐Driven Design of Wave‐Propagation Models for Shale‐Oil Reservoirs Based on Machine Learning

Microcrack Porosity Estimation Based on Rock Physics Templates: A Case Study in Sichuan Basin, China

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