weak sandstone
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2021 ◽  
Vol 11 (6) ◽  
pp. 2381-2387
Author(s):  
Omar A. Almisned ◽  
Naif B. Alqahtani

AbstractAs more engineering projects and activities are taking place on and around weak rocks, it is becoming more important to study and characterize them. Since regular practices of rock mechanical testing are not effective for weak rocks, special laboratory tests and measurements were performed to characterize the Alkharj Saudi weak sandstone rock which is a clastic rock dominantly sandy limestone and sandstone. Test results are presented in this paper. Porosity, permeability, and mechanical properties (stress, strain, Poisson's ratio, confined compressive strength and unconfined compressive strength) were obtained and then used to characterize the proposed weak rock. This paper provides a mean of classifying weak soft rocks despite encountered problems in handling and testing such materials.


Author(s):  
Nubia Aurora González Molano ◽  
Jacobo Canal Vila ◽  
Héctor González Pérez ◽  
José Alvarellos Iglesias ◽  
M. R. Lakshmikantha

In this study an extensive experimental program has been carried out in order to characterize the mechanical behavior of two weak sandstone formations of an offshore field for application to sand production modeling. The experimental tests included Scratch tests, Triaxial tests and Advanced thick wall cylinder tests (ATWC) where the sand production initiation and the cumulative sand produced were registered. Numerical simulations of experimental tests were then performed using an advanced elasto-plastic constitutive model. Triaxial tests simulations allowed calibrating the constitutive model parameters. These parameters were employed for the numerical simulation of the ATWC in order to determine the equivalent plastic strain threshold required to the onset of sand production observed in laboratory for sanding assessment. Results obtained highlight the importance to use a realistic representation of the rock behavior focusing on post-yield behavior in order to build confidence in model predictions.


2018 ◽  
Vol 41 (1) ◽  
pp. 061-074
Author(s):  
Orlando Martini de Oliveira ◽  
Rodrigo Bim ◽  
Gabriel Bellina Nunes ◽  
Rafael Augusto dos Reis Higashi
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2017 ◽  
Vol 24 (3) ◽  
pp. 699-707 ◽  
Author(s):  
Jing-yin Wang ◽  
Yu Zhang ◽  
Yun Jia ◽  
Pei-jie Jin ◽  
Li-hong Zhu

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