Elastic Wave Velocity Versus Saturation and Pore Fluid Distribution Relationship

2003 ◽  
Vol 46 (1) ◽  
pp. 192-198 ◽  
Author(s):  
Ge SHI ◽  
Wenlue SHEN ◽  
Dongquan YANG
Keyword(s):  
Elastic Wave ◽  
Wave Velocity ◽  
Elastic Wave Velocity ◽  
Pore Fluid ◽  
Fluid Distribution ◽  
Velocity Versus

On the Transition from Homogeneous to Bubbling Fluidization

1997 ◽  
Vol 62 (11) ◽  
pp. 1698-1709
Author(s):  
Miloslav Hartman ◽  
Zdeněk Beran ◽  
Václav Veselý ◽  
Karel Svoboda
Keyword(s):  
Elastic Wave ◽  
Froude Number ◽  
Wave Velocity ◽  
Density Ratio ◽  
Elastic Wave Velocity ◽  
Solid Density ◽  
Archimedes Number ◽  
Group A ◽  
Experimental Findings

The onset of the aggregative mode of liquid-solid fluidization was explored. The experimental findings were interpreted by means of the dynamic (elastic) wave velocity and the voidage propagation (continuity) wave velocity. For widely different systems, the mapping of regimes has been presented in terms of the Archimedes number, the Froude number and the fluid-solid density ratio. The proposed diagram also depicts the typical Geldart's Group A particles fluidized with air.

scholarly journals Elastic wave velocity and electrical conductivity in a brine-saturated rock and microstructure of pores

2019 ◽  
Vol 71 (1) ◽  
Author(s):  
Tohru Watanabe ◽  
Miho Makimura ◽  
Yohei Kaiwa ◽  
Guillaume Desbois ◽  
Kenta Yoshida ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Elastic Wave ◽  
Wave Velocity ◽  
High Pressures ◽  
Seismic Velocity ◽  
Volume Fraction ◽  
Elastic Wave Velocity ◽  
Fluid Volume ◽  
Sem Observation ◽  
Wide Aperture

AbstractElastic wave velocity and electrical conductivity in a brine-saturated granitic rock were measured under confining pressures of up to 150 MPa and microstructure of pores was examined with SEM on ion-milled surfaces to understand the pores that govern electrical conduction at high pressures. The closure of cracks under pressure causes the increase in velocity and decrease in conductivity. Conductivity decreases steeply below 10 MPa and then gradually at higher pressures. Though cracks are mostly closed at the confining pressure of 150 MPa, brine must be still interconnected to show observed conductivity. SEM observation shows that some cracks have remarkable variation in aperture. The aperture varies from ~ 100 nm to ~ 3 μm along a crack. FIB–SEM observation suggests that wide aperture parts are interconnected in a crack. Both wide and narrow aperture parts work parallel as conduction paths at low pressures. At high pressures, narrow aperture parts are closed but wide aperture parts are still open to maintain conduction paths. The closure of narrow aperture parts leads to a steep decrease in conductivity, since narrow aperture parts dominate cracks. There should be cracks in various sizes in the crust: from grain boundaries to large faults. A crack must have a variation in aperture, and wide aperture parts must govern the conduction paths at depths. A simple tube model was employed to estimate the fluid volume fraction. The fluid volume fraction of 10−4–10−3 is estimated for the conductivity of 10−2 S/m. Conduction paths composed of wide aperture parts are consistent with observed moderate fluctuations (< 10%) in seismic velocity in the crust.

scholarly journals NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir Rock

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1513 ◽  
Author(s):  
Naser Golsanami ◽  
Xuepeng Zhang ◽  
Weichao Yan ◽  
Linjun Yu ◽  
Huaimin Dong ◽  
...  
Keyword(s):  
Elastic Wave ◽  
Wave Velocity ◽  
Transverse Relaxation Time ◽  
Seismic Attributes ◽  
Elastic Wave Velocity ◽  
Reservoir Rock ◽  
Elastic Response ◽  
Hydrocarbon Reservoir ◽  
Statistical Ensembles ◽  
Pore Types

Seismic data and nuclear magnetic resonance (NMR) data are two of the highly trustable kinds of information in hydrocarbon reservoir engineering. Reservoir fluids influence the elastic wave velocity and also determine the NMR response of the reservoir. The current study investigates different pore types, i.e., micro, meso, and macropores’ contribution to the elastic wave velocity using the laboratory NMR and elastic experiments on coal core samples under different fluid saturations. Once a meaningful relationship was observed in the lab, the idea was applied in the field scale and the NMR transverse relaxation time (T2) curves were synthesized artificially. This task was done by dividing the area under the T2 curve into eight porosity bins and estimating each bin’s value from the seismic attributes using neural networks (NN). Moreover, the functionality of two statistical ensembles, i.e., Bag and LSBoost, was investigated as an alternative tool to conventional estimation techniques of the petrophysical characteristics; and the results were compared with those from a deep learning network. Herein, NMR permeability was used as the estimation target and porosity was used as a benchmark to assess the reliability of the models. The final results indicated that by using the incremental porosity under the T2 curve, this curve could be synthesized using the seismic attributes. The results also proved the functionality of the selected statistical ensembles as reliable tools in the petrophysical characterization of the hydrocarbon reservoirs.

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