scholarly journals The ARMR Classification System and the Modified Hoek-Brown Failure Criterion Compared to Directional Shear Strength Models for Anisotropic Rock Masses

2019 ◽  
Author(s):  
Neil Bar ◽  
Charalampos Saroglou
Keyword(s):  
Shear Strength ◽  
Rock Mass ◽  
Failure Criterion ◽  
Classification System ◽  
Failure Criteria ◽  
Rock Masses ◽  
Anisotropic Rock ◽  
Anisotropic Rock Mass ◽  
Degree Of Anisotropy ◽  
Strength Models

The anisotropic rock mass rating classification system, ARMR, has been developed in conjunction with the Modified Hoek-Brown failure to deal with varying shear strength with respect to the orientation and degree of anisotropy within an anisotropic rock mass. Conventionally, ubiquitous-joint or directional shear strength models have assumed a general rock mass strength, typically estimated using the Hoek-Brown failure criterion, and applied a directional weakness in a given orientation depending on the anisotropic nature of the rock mass. Shear strength of the directional weakness is typically estimated using the Barton-Bandis failure criterion, or on occasion, the Mohr-Coulomb failure criteria. Directional shear strength models such as these often formed the basis of continuum models for slopes and underground excavations in anisotropic rock masses. This paper compares ARMR and the Modified Hoek-Brown failure criterion to the conventional directional shear strength models using a case study from Western Australia.

scholarly journals LSSVM-Based Rock Failure Criterion and Its Application in Numerical Simulation

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Changxing Zhu ◽  
Hongbo Zhao ◽  
Zhongliang Ru
Keyword(s):  
Rock Mass ◽  
Failure Criterion ◽  
Nonlinear Problems ◽  
Failure Criteria ◽  
Rock Failure ◽  
Support Vector ◽  
Failure Behavior ◽  
Circular Tunnel ◽  
Vector Machines

A rock failure criterion is very important for the prediction of the failure of rocks or rock masses in rock mechanics and engineering. Least squares support vector machines (LSSVM) are a powerful tool for addressing complex nonlinear problems. This paper describes a LSSVM-based rock failure criterion for analyzing the deformation of a circular tunnel under differentin situstresses without assuming a function form. First, LSSVM was used to represent the nonlinear relationship between the mechanical properties of rock and the failure behavior of the rock in order to construct a rock failure criterion based on experimental data. Then, this was used in a hypothetical numerical analysis of a circular tunnel to analyze the mechanical behavior of the rock mass surrounding the tunnel. The Mohr-Coulomb and Hoek-Brown failure criteria were also used to analyze the same case, and the results were compared; these clearly indicate that LSSVM can be used to establish a rock failure criterion and to predict the failure of a rock mass during excavation of a circular tunnel.

Assessing Wellbore Stability With a Modified Lade Failure Criterion

2020 ◽  
Vol 142 (8) ◽  
Author(s):  
Babak Bahrami ◽  
Ali Sadatshojaie ◽  
David A Wood
Keyword(s):  
Neural Network ◽  
Rock Mass ◽  
Failure Criterion ◽  
Oil And Gas ◽  
Poisson Ratio ◽  
Failure Criteria ◽  
Wellbore Stability ◽  
Depth Interval ◽  
Gas Reservoirs ◽  
Operational Processes

Abstract The importance of evaluating wellbore stability in analyzing and estimating the efficiency of drilling directionally into oil and gas reservoirs is well known. Geomechanical data and failure criterion can be used to model and control rock mass behavior in response to the stresses imposed upon it. Understanding and managing the risks of rock mass deformation significantly improve operational processes such as wellbore stability, sand production, and hydraulic fracturing. The modified Lade failure criterion is established as the most precise failure criterion based on previous studies. By combining it with tensions around the wellbore, a novel relationship is derived for determining the stable mud window. To investigate the accuracy of the new relationship, two geomechanical models (neural network and empirical correlations) for a one-directional wellbore are developed and their performance compared with two other failure criteria (Hoek–Brown and Mogi–Coulomb). The geomechanical parameters (Young’s modulus, Poisson ratio, uniaxial compressive strength, and internal friction coefficient) obtained from the models show that neural network configurations perform better than those built with the empirical equation. The horizontal minimum and maximum stress values across the depth interval of interest (2347–2500 m) are established for a case study reservoir. The model provides an accurate prediction of wellbore instability when applying the modified Lade criterion; the stable mud weight is derived with improved precision compared to the other failure criteria evaluated. A key advantage of the developed method is that it does not require input knowledge of the reservoir’s structural boundaries (e.g., the fault regime) or core test data.

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