scholarly journals Correlation of uniaxial compressive strength with the dynamic elastic modulus, P - wave velocity and S - wave velocity of different rock types

2019 ◽  
pp. 11-25
Author(s):  
Jelena Majstorović ◽  
Miloš Gligorić ◽  
Suzana Lutovac ◽  
Milanka Negovanović ◽  
Luka Crnogorac
2021 ◽  
Vol 11 (22) ◽  
pp. 10653
Author(s):  
Jingwei Gao ◽  
Chao Xu ◽  
Yan Xi ◽  
Lifeng Fan

This study investigated the effects of freezing temperature under freeze-thaw cycling conditions on the mechanical behavior of sandstone. First, the sandstone specimens were subjected to 10-time freeze-thaw cycling treatments at different freezing temperatures (−20, −40, −50, and −60 °C). Subsequently, a series of density, ultrasonic wave, and static and dynamic mechanical behavior tests were carried out. Finally, the effects of freezing temperature on the density, P-wave velocity, stress–strain curves, static and dynamic uniaxial compressive strength, static elastic modulus, and dynamic energy absorption of sandstone were discussed. The results show that the density slightly decreases as temperature decreases, approximately by 1.0% at −60 °C compared with that at 20 °C. The P-wave velocity, static and dynamic uniaxial compressive strength, static elastic modulus, and dynamic energy absorption obviously decrease. As freezing temperature decreases from 20 to −60 °C, the static uniaxial compressive strength, static elastic modulus, dynamic strength, and dynamic energy absorption of sandstone decrease by 16.8%, 21.2%, 30.8%, and 30.7%, respectively. The dynamic mechanical behavior is more sensitive to the freezing temperature during freeze-thawing cycling compared with the static mechanical behavior. In addition, a higher strain rate can induce a higher dynamic strength and energy absorption.


2019 ◽  
Vol 17 (2) ◽  
Author(s):  
M. Wahdanadi Haidar ◽  
Reza Wardhana ◽  
M. Iskan ◽  
M. Syamsu Rosid

The pore systems in carbonate reservoirs are more complex than the pore systems in clastic rocks. There are three types of pores in carbonate rocks: interparticle pores, stiff pores and cracks. The complexity of the pore types can lead to changes in the P-wave velocity by up to 40%, and carbonate reservoir characterization becomes difficult when the S-wave velocity is estimated using the dominant interparticle pore type only. In addition, the geometry of the pores affects the permeability of the reservoir. Therefore, when modelling the elastic modulus of the rock it is important to take into account the complexity of the pore types in carbonate rocks. The Differential Effective Medium (DEM) is a method for modelling the elastic modulus of the rock that takes into account the heterogeneity in the types of pores in carbonate rocks by adding pore-type inclusions little by little into the host material until the required proportion of the material is reached. In addition, the model is optimized by calculating the bulk modulus of the fluid filler porous rock under reservoir conditions using the Adaptive Batzle-Wang method. Once a fluid model has been constructed under reservoir conditions, the model is entered as input for the P-wave velocity model, which is then used to estimate the velocity of the S-wave and the proportion of primary and secondary pore types in the rock. Changes in the characteristics of the P-wave which are sensitive to the presence of fluid lead to improvements in the accuracy of the P-wave model, so the estimated S-wave velocity and the calculated ratio of primary and secondary pores in the reservoir are more reliable.


2021 ◽  
Vol 74 (4) ◽  
pp. 521-528
Author(s):  
André Cezar Zingano ◽  
Paulo Salvadoretti ◽  
Rafael Ubirajara Rocha ◽  
João Felipe Coimbra Leite Costa

2020 ◽  
Vol 10 (13) ◽  
pp. 4565
Author(s):  
Manuel Saldaña ◽  
Javier González ◽  
Ignacio Pérez-Rey ◽  
Matías Jeldres ◽  
Norman Toro

In the rock mechanics and rock engineering field, the strength parameter considered to characterize the rock is the uniaxial compressive strength (UCS). It is usually determined in the laboratory through a few statistically representative numbers of specimens, with a recommended minimum of five. The UCS can also be estimated from rock index properties, such as the effective porosity, density, and P-wave velocity. In the case of a porous rock such as travertine, the random distribution of voids inside the test specimen (not detectable in the density-porosity test, but in the compressive strength test) causes large variations on the UCS value, which were found in the range of 62 MPa for this rock. This fact complicates a sufficiently accurate determination of experimental results, also affecting the estimations based on regression analyses. Aiming to solve this problem, statistical analysis, and machine learning models (artificial neural network) was developed to generate a reliable predictive model, through which the best results for a multiple regression model between uniaxial compressive strength (UCS), P-wave velocity and porosity were obtained.


Sign in / Sign up

Export Citation Format

Share Document