Electromagnetic precursor of rock failure

1994 ◽  
Vol 30 (3) ◽  
pp. 238-239
Author(s):  
D. V. Alekseev ◽  
P. V. Egorov
Keyword(s):  
Rock Failure ◽  
Electromagnetic Precursor

EXACT METHODS OF LINEAR PROGRAMMING FOR CHOOSING ROCK FAILURE CONDITIONS.

2013 ◽  
Vol 7 (11) ◽  
pp. 52-57
Author(s):  
Oleg Markovich Terentiev ◽  
◽  
Anton Iosifovich Kleshchov ◽  
Keyword(s):  
Linear Programming ◽  
Rock Failure ◽  
Exact Methods ◽  
Failure Conditions

scholarly journals Slope Failure in a Period of Increased Landslide Activity: Sennwald Rock Avalanche, Switzerland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 331
Author(s):  
Selçuk Aksay ◽  
Susan Ivy-Ochs ◽  
Kristina Hippe ◽  
Lorenz Grämiger ◽  
Christof Vockenhuber
Keyword(s):  
Slope Failure ◽  
Rock Avalanche ◽  
Eastern Alps ◽  
Rock Failure ◽  
Rhine River ◽  
Stratigraphic Sequence ◽  
Exposure Age ◽  
Wet Climate ◽  
Cosmogenic Nuclide Dating ◽  
Additional Support

The Säntis nappe is a complex fold-and-thrust structure in eastern Switzerland, consisting of numerous tectonic discontinuities and a range of hillslopes prone to landsliding and large slope failures that modify the topography irreversibly. A slope failure, namely the Sennwald rock avalanche, occurred in the southeast wall of this fold-and-thrust structure due to the rock failure of Lower Cretaceous Helvetic limestones along the Rhine River valley. In this research, this palaeolandslide is examined in a multidisciplinary approach for the first time with detection and mapping of avalanche deposits, dynamic run-out modelling and cosmogenic nuclide dating. During the rock failure, the avalanche deposits were transported down the hillslope in a spreading-deck fashion, roughly preserving the original stratigraphic sequence. The distribution of landslide deposits and surface exposure age of the rock failure support the hypothesis that the landslide was a single catastrophic event. The 36Cl surface exposure age of avalanche deposits indicates an age of 4.3 ± 0.5 ka. This time coincides with a notably wet climate period, noted as a conditioning factor for landslides across the Alps in the mid-Holocene. The contemporaneity of our event at its location in the Eastern Alps provide additional support for the contention of increased regional seismic activity in mid-Holocene.

Transient electric signals prior to rock failure under uniaxial compression

1994 ◽  
Vol 21 (16) ◽  
pp. 1687-1690 ◽  
Author(s):  
V. Hadjicontis ◽  
C. Mavromatou
Keyword(s):  
Uniaxial Compression ◽  
Rock Failure ◽  
Electric Signals

Quasi-brittle rock failure model

2013 ◽  
Vol 49 (4) ◽  
pp. 576-582 ◽  
Author(s):  
V. M. Kornev ◽  
A. A. Zinov’ev
Keyword(s):  
Rock Failure ◽  
Brittle Rock ◽  
Failure Model

Fluid-solid-thermal coupling analysis of rock failure under the joint action of cutting and jet impingement

Geothermics ◽  
2021 ◽  
Vol 94 ◽  
pp. 102092
Author(s):  
Xianwei Dai ◽  
Zhongwei Huang ◽  
Xiaoguang Wu ◽  
Heqian Zhao ◽  
Huaizhong Shi
Keyword(s):  
Joint Action ◽  
Jet Impingement ◽  
Rock Failure ◽  
Thermal Coupling ◽  
Coupling Analysis

Risk-Controlled Wellbore Stability Criterion Based on Machine Learning Assisted Finite Element Model

2021 ◽  
Author(s):  
Hussain AlBahrani ◽  
Nobuo Morita
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Finite Element ◽  
Finite Element Model ◽  
Stability Criterion ◽  
Element Model ◽  
Rock Failure ◽  
Wellbore Stability ◽  
Wellbore Instability ◽  
Conventional Methods

Abstract In many drilling scenarios that include deep wells and highly stressed environments, the mud weight required to completely prevent wellbore instability can be impractically high. In such cases, what is known as risk-controlled wellbore stability criterion is introduced. This criterion allows for a certain level of wellbore instability to take place. This means that the mud weight calculated using this criterion will only constrain wellbore instability to a certain manageable level, hence the name risk-controlled. Conventionally, the allowable level of wellbore instability in this type of models has always been based on the magnitude of the breakout angle. However, wellbore enlargements, as seen in calipers and image logs, can be highly irregular in terms of its distribution around the wellbore. This irregularity means that risk-controlling the wellbore instability through the breakout angle might not be always sufficient. Instead, the total volume of cavings is introduced as the risk control parameter for wellbore instability. Unlike the breakout angle, the total volume of cavings can be coupled with a suitable hydraulics model to determine the threshold of manageable instability. The expected total volume of cavings is determined using a machine learning (ML) assisted 3D elasto-plastic finite element model (FEM). The FEM works to model the interval of interest, which eventually provides a description of the stress distribution around the wellbore. The ML algorithm works to learn the patterns and limits of rock failure in a supervised training manner based on the wellbore enlargement seen in calipers and image logs from nearby offset wells. Combing the FEM output with the ML algorithm leads to an accurate prediction of shear failure zones. The model is able to predict both the radial and circumferential distribution of enlargements at any mud weight and stress regime, which leads to a determination of the expected total volume of cavings. The model implementation is first validated through experimental data. The experimental data is based on true-triaxial tests of bored core samples. Next, a full dataset from offset wells is used to populate and train the model. The trained model is then used to produce estimations of risk-controlled stability mud weights for different drilling scenarios. The model results are compared against those produced by conventional methods. Finally, both the FEM-ML model and the conventional methods results are compared against the drilling experience of the offset wells. This methodology provides a more comprehensive and new solution to risk controlling wellbore instability. It relies on a novel process which learns rock failure from calipers and image logs.

scholarly journals Dynamic Fracture Toughness and Dynamic Tensile Strength of the Rock from Different Depths of Beijing Datai Well

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Ke Man ◽  
Xiaoli Liu
Keyword(s):  
Fracture Toughness ◽  
Tensile Strength ◽  
Dynamic Fracture ◽  
Empirical Relation ◽  
Dynamic Fracture Toughness ◽  
Rock Failure ◽  
Test Method ◽  
Strength Increase ◽  
Dynamic Tensile Strength ◽  
Different Depths

From the standard test method suggested by ISRM and GB/T50266-2013, the uniaxial static tensile strength, dynamic tensile strength, and dynamic fracture toughness of the same basalt at different depths have been measured, respectively. It is observed that there may be an empirical relation between dynamic fracture toughness and dynamic tensile strength. The testing data show that both the dynamic fracture toughness and dynamic tensile strength increase with the loading rate and the dynamic tensile strength increases a little bit more quickly than the dynamic fracture toughness. With an increasing depth, the dynamic tensile strength has much more influence on the dynamic fracture toughness, as which it is much liable to bring out the unexpected catastrophes in the engineering projects, especially during the excavation at deep mining. From the rock failure mechanisms, it is pointed out that the essential reason of the rock failure is the microcrack unstable propagation. The crack processes growth, propagation, and coalescence are induced by tensile stress, not shear stress or compressive stress. The paper provides estimation of the dynamic fracture toughness from the dynamic tensile strength value, which can be measured more easily.

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