Frequency dependence of Poisson's ratio using the method of reduced variables

1996 ◽  
Vol 24 (1) ◽  
pp. 75-85 ◽  
Author(s):  
R. Caracciolo ◽  
M. Giovagnoni
Keyword(s):  
Frequency Dependence ◽  
Poisson’S Ratio ◽  
Poisson's Ratio

Effect of fluids and frequencies on Poisson’s ratio of sandstone samples

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. D183-D195 ◽  
Author(s):  
Lucas Pimienta ◽  
Jérôme Fortin ◽  
Yves Guéguen
Keyword(s):  
Frequency Dependence ◽  
Poisson’S Ratio ◽  
Poisson's Ratio ◽  
Fluid Viscosity ◽  
S Wave ◽  
Standard Samples ◽  
Frequency Dependent ◽  
S Waves ◽  
Fontainebleau Sandstone ◽  
Dry Conditions

Poisson’s ratio [Formula: see text] is an important parameter when interpreting measured geophysical and seismic data. For an isotropic medium, it directly relates to the ratio of P- and S-wave velocities. We have measured [Formula: see text] as a function of pressure and frequency in fluid-saturated sandstones. The method of measuring [Formula: see text] was first tested as a function of pressure and frequency using standard samples. The phase shift [Formula: see text] between radial and axial strains was also measured. For all standard samples, such as the linear viscoelastic Plexiglas, the data indicated that [Formula: see text] correlated with [Formula: see text] and related to a dissipation on [Formula: see text]. Then, [Formula: see text] and [Formula: see text] were measured as a function of pressure and frequency for two dry and fluid-saturated Fontainebleau sandstone samples. Under dry conditions, no frequency dependence and very small pressure dependence were observed. Unusual behaviors were observed under fluid-saturated conditions. In particular, [Formula: see text] of one sample indicated a frequency-dependent bell-shaped dispersion under water and glycerin saturation that correlated with peaks in [Formula: see text]. Plotting the measurements as a function of apparent frequency (i.e., normalizing by the fluid viscosity) indicated a good fit between the water- and glycerin-saturated measurements. The bell-shaped dispersion in [Formula: see text] that was observed for one particular sandstone held for all effective pressures. These variations fully correlated with the peaks of [Formula: see text] observed. Our results can be interpreted using fluid flow and effective medium theories in the case of a porous microcracked rock. Drained/undrained and relaxed/unrelaxed transitions have frequency and magnitude of variations that are consistent with the measurements. The rock sample microcrack density strongly affects this frequency dependence. The inferred [Formula: see text] ratio at low effective pressures also indicates a large frequency-dependent bell-shaped dispersion. The parameter [Formula: see text] is a clear indicator of the frequency-dependent dissipation of [Formula: see text] and relates to the attenuation of P- and S-waves.

Frequency Dependence of Poisson's Ratio of Viscoelastic Elastomer Foam

2011 ◽  
Vol 30 (6) ◽  
pp. 277-286 ◽  
Author(s):  
L. Dong ◽  
R.S. Lakes
Keyword(s):  
Shear Modulus ◽  
Bulk Modulus ◽  
Frequency Dependence ◽  
High Frequency ◽  
Poisson’S Ratio ◽  
Low Frequency ◽  
Poisson's Ratio ◽  
Viscoelastic Response ◽  
Polymer Foams ◽  
Solid Polymers

All polymer foams are viscoelastic; for foams used in earplugs, this response is essential to their function. Poisson's ratio was inferred from viscoelastic response of earplug foam in torsion and bending. Poisson's ratio increases with frequency as a result of stress induced air flow. Air within the pores contributes to the overall bulk modulus at high frequency, but not at low frequency at which it easily flows when the foam is deformed. Such behaviour is the opposite of that of solid polymers in which the shear modulus varies with frequency much more than the bulk modulus.

The Model for Calculating Elastic Modulus and Poisson's Ratio of Coal Body

2013 ◽  
Vol 6 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Ai Chi ◽  
Li Yuwei
Keyword(s):  
Elastic Modulus ◽  
Poisson’S Ratio ◽  
Fractured Rock ◽  
Rock Block ◽  
Poisson's Ratio ◽  
Linear Elastic ◽  
Study Results ◽  
The Face ◽  
Block Face ◽  
Coal Rock

Coal body is a type of fractured rock mass in which lots of cleat fractures developed. Its mechanical properties vary with the parametric variation of coal rock block, face cleat and butt cleat. Based on the linear elastic theory and displacement equivalent principle and simplifying the face cleat and butt cleat as multi-bank penetrating and intermittent cracks, the model was established to calculate the elastic modulus and Poisson's ratio of coal body combined with cleat. By analyzing the model, it also obtained the influence of the parameter variation of coal rock block, face cleat and butt cleat on the elastic modulus and Poisson's ratio of the coal body. Study results showed that the connectivity rate of butt cleat and the distance between face cleats had a weak influence on elastic modulus of coal body. When the inclination of face cleat was 90°, the elastic modulus of coal body reached the maximal value and it equaled to the elastic modulus of coal rock block. When the inclination of face cleat was 0°, the elastic modulus of coal body was exclusively dependent on the elastic modulus of coal rock block, the normal stiffness of face cleat and the distance between them. When the distance between butt cleats or the connectivity rate of butt cleat was fixed, the Poisson's ratio of the coal body initially increased and then decreased with increasing of the face cleat inclination.

scholarly journals A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks

2019 ◽  
Vol 11 (19) ◽  
pp. 5283 ◽  
Author(s):  
Gowida ◽  
Moussa ◽  
Elkatatny ◽  
Ali
Keyword(s):  
Poisson’S Ratio ◽  
Accurate Determination ◽  
Oil And Gas Industry ◽  
Poisson's Ratio ◽  
Elastic Behavior ◽  
Percentage Error ◽  
Ann Model ◽  
Field Development ◽  
Well Completion

Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion design. The elastic behavior of rocks can be studied by determining two main parameters: Young’s modulus and Poisson’s ratio. Accurate determination of the Poisson’s ratio helps to estimate the in-situ horizontal stresses and in turn, avoid many critical problems which interrupt drilling operations, such as pipe sticking and wellbore instability issues. Accurate Poisson’s ratio values can be experimentally determined using retrieved core samples under simulated in-situ downhole conditions. However, this technique is time-consuming and economically ineffective, requiring the development of a more effective technique. This study has developed a new generalized model to estimate static Poisson’s ratio values of sandstone rocks using a supervised artificial neural network (ANN). The developed ANN model uses well log data such as bulk density and sonic log as the input parameters to target static Poisson’s ratio values as outputs. Subsequently, the developed ANN model was transformed into a more practical and easier to use white-box mode using an ANN-based empirical equation. Core data (692 data points) and their corresponding petrophysical data were used to train and test the ANN model. The self-adaptive differential evolution (SADE) algorithm was used to fine-tune the parameters of the ANN model to obtain the most accurate results in terms of the highest correlation coefficient (R) and the lowest mean absolute percentage error (MAPE). The results obtained from the optimized ANN model show an excellent agreement with the laboratory measured static Poisson’s ratio, confirming the high accuracy of the developed model. A comparison of the developed ANN-based empirical correlation with the previously developed approaches demonstrates the superiority of the developed correlation in predicting static Poisson’s ratio values with the highest R and the lowest MAPE. The developed correlation performs in a manner far superior to other approaches when validated against unseen field data. The developed ANN-based mathematical model can be used as a robust tool to estimate static Poisson’s ratio without the need to run the ANN model.

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