Study on Rock Mechanical Properties and Quality Classification of Surrounding Rock of Tunnel Group in Gui’an Tencent Data Center

2021 ◽  
Vol 10 (02) ◽  
pp. 132-141
Author(s):  
跃新 余
Keyword(s):  
Mechanical Properties ◽  
Data Center ◽  
Surrounding Rock ◽  
Quality Classification ◽  
Rock Mechanical Properties

scholarly journals Variation characteristics of coal-rock mechanical properties under varying temperature conditions for Shanxi Linfen coalbed methane well in China

2021 ◽  
Vol 11 (7) ◽  
pp. 2905-2915
Author(s):  
Xin Li ◽  
Jie Zhang ◽  
Cuinan Li ◽  
Ben Li ◽  
Haoyang Zhao ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Coalbed Methane ◽  
Surrounding Rock ◽  
Fracture Angle ◽  
Temperature Conditions ◽  
Change Characteristics ◽  
Variation Characteristics ◽  
Coal Rock ◽  
Rock Mechanical Properties ◽  
Primary Fracture

AbstractIn the actual exploitation process of coalbed methane (CBM), as the fluid in the wellbore continues to circulate, the surrounding rock of the CBM well will continuously exchange heat with the fluid in the wellbore, resulting in continuous changes in the temperature of the surrounding rock itself. Linfen, Shanxi is the main exploitation area for CBM in China. This paper aims further to improve the exploitation efficiency of CBM in this area and conducts experimental research on the change characteristics of coal-rock mechanical properties under varying temperature conditions. The experimental results show that under constant pressure conditions, the higher the temperature, the lower the stress value when the coal-rock breaks. In the process of reaching peak strength, the higher the temperature, the higher the proportion of coal-rock plastic deformation in its entire deformation stage. The compressive strength, elastic modulus, and main crack length of coal-rock will decrease with temperature. The Poisson's ratio and primary fracture angle will increase with the increase of experimental temperature.

Integration of Chemofacies and Rock Mechanical Properties Using Machine Learning Algorithms: Implications for Geomechanics and Hydraulic Fracture Stimulations in Paleozoic Formations, Saudi Arabia

2021 ◽  
Author(s):  
Maaruf Hussain ◽  
Abduljamiu Amao ◽  
Khalid Al-Ramadan ◽  
Sunday Olatunji ◽  
Ardiansyah Negara
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Saudi Arabia ◽  
Spatial Distribution ◽  
Chemical Composition ◽  
Rock Strength ◽  
Machine Learning Algorithms ◽  
Unconventional Reservoirs ◽  
Geochemical Properties ◽  
Rock Mechanical Properties

Abstract The knowledge of rock mechanical properties is critical to reducing drilling risk and maximizing well and reservoir productivity. Rock chemical composition, their spatial distribution, and porosity significantly influenced these properties. However, low porosity characterized unconventional reservoirs as such, geochemical properties considerably control their mechanical behavior. In this study, we used chemostratigraphy as a correlation tool to separate strata in highly homogenous formations where other traditional stratigraphic methods failed. In addition, we integrated the chemofacies output and reduced Young's modulus to outline predictable associations between facies and mechanical properties. Thus, providing better understanding of lithofacies-controlled changes in rock strength that are useful inputs for geomechanical models and completions stimulations.

scholarly journals Approximate Dynamic Programming Based Control of Proppant Concentration in Hydraulic Fracturing

Mathematics ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 132 ◽  
Author(s):  
Harwinder Singh Sidhu ◽  
Prashanth Siddhamshetty ◽  
Joseph Kwon
Keyword(s):  
Mechanical Properties ◽  
Dynamic Programming ◽  
Hydraulic Fracturing ◽  
Young’S Modulus ◽  
Large Scale ◽  
Approximate Dynamic Programming ◽  
Young's Modulus ◽  
Computational Cost ◽  
Control Technique ◽  
Rock Mechanical Properties

Hydraulic fracturing has played a crucial role in enhancing the extraction of oil and gas from deep underground sources. The two main objectives of hydraulic fracturing are to produce fractures with a desired fracture geometry and to achieve the target proppant concentration inside the fracture. Recently, some efforts have been made to accomplish these objectives by the model predictive control (MPC) theory based on the assumption that the rock mechanical properties such as the Young’s modulus are known and spatially homogenous. However, this approach may not be optimal if there is an uncertainty in the rock mechanical properties. Furthermore, the computational requirements associated with the MPC approach to calculate the control moves at each sampling time can be significantly high when the underlying process dynamics is described by a nonlinear large-scale system. To address these issues, the current work proposes an approximate dynamic programming (ADP) based approach for the closed-loop control of hydraulic fracturing to achieve the target proppant concentration at the end of pumping. ADP is a model-based control technique which combines a high-fidelity simulation and function approximator to alleviate the “curse-of-dimensionality” associated with the traditional dynamic programming (DP) approach. A series of simulations results is provided to demonstrate the performance of the ADP-based controller in achieving the target proppant concentration at the end of pumping at a fraction of the computational cost required by MPC while handling the uncertainty in the Young’s modulus of the rock formation.

A multiscale topography analysis of ground stainless steel and titanium alloys

2007 ◽  
Vol 221 (9) ◽  
pp. 1407-1420 ◽  
Author(s):  
S Giljean ◽  
M Bigerelle ◽  
K Anselme
Keyword(s):  
Mechanical Properties ◽  
Stainless Steel ◽  
Titanium Alloys ◽  
Roughness Parameter ◽  
Roughness Parameters ◽  
Observation Scale ◽  
316L Austenitic Stainless Steel ◽  
The Mean ◽  
Best Parameter

This study aims to perform a multiscale analysis of abraded surfaces of 316L austenitic stainless steel and titanium alloys (TiAl6V4) grinded at different paper grades. The authors propose to answer the following question: For a given distribution of silicon carbide grains of the paper, what is the best roughness parameter and at which scale must it be evaluated better to discriminate the effect of the mechanical properties of the materials? Paper grades from 80 to 4000 were used under identical pressure and erosion time. It can be concluded that the values of the amplitude roughness parameters depend on the observation scale. It is outlined that the abrasion process is very reproducible. A statistical analysis is then proposed, first, to define a classification of the relevance of the roughness parameters for each grain size distribution, and second, to determine at which scale the mechanical properties of the bulk are more influenced for all paper grades. Finally, at relevant scales, the Abbott amplitude parameters roughness kernel (RK) is the best parameter to discriminate the paper grade effect. The mean distance between asperities (SM) is the preferred method for determining the wear effect on materials and the linear mean normalizing autocorrelation (AMNLN) is the preferred method for determining the interaction between paper grade and materials.

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