...12th Asian Rock Mechanics Symposium, Hanoi, Vietnam - The 2022 ISRM Sponsored Regional Symposium ©2022 ISRM, VSRM. ISBN: 978-604-357-103-5 Do Nhu Trang, Pham Quoc Tuan et al. (Eds.) Using Different Artificial Neural Network Models to Predict Rate of Penetration for Oil and Gas Wells Do Quang Khanha...

... intelligence methods, including artificial neural networks, genetic algorithms, and fuzzy expert systems have been conducted to predict the PPV value (Dehghani and Ataee Pour, 2011; Monjezi et al., 2011; Amnieh et al., 2012; Faradonbeh et al., 2016). The new Badaling tunnel is excavated below the Badaling...

... heterogeneity, where hydraulic property of 3D fracture network with heterogeneous aperture distribution remains poorly understood. fluid modeling equation of state hydraulic fracturing Upstream Oil & Gas Modeling & Simulation neural network roughness fracture network discrete fracture...

... and statistical analysis with dominant factors of density, which allows constructing the correlative relationship between thermal conductivity and stiffness. Artificial Neural Network (ANN) which learns relationships between data predicts thermal conductivity of rock based on collected physical properties using...

... examined the applicability of a neural network model for settlement prediction using measurements in the early stage after construction. Simulations using a basic network model showed that when the measurement data used for teaching the neural network accumulated, the prediction was in good agreement...

... effort to identify a superior prediction model. The main objective of this paper is to compare two TBM performance estimation models with especial task of determining Rate of Penetration (ROP), using Artificial Neural Network (ANN) and multiple linear regression. These approaches were applied...

... Artificial Intelligence deveatoric stress Upstream Oil & Gas joint property stress-strain response Rock mechanics neural network Agra sandstone experimental measurement machine learning strength Jamrani sandstone rock property MPa ann analysis artificial neural network...

... Artificial Intelligence specification machine learning Engineering Upstream Oil & Gas neural network TBM penetration rate Rock mechanics artificial neural network University strength classification system neuron service tunnel Amirkabir University performance prediction rock...

...1 ISRM International Symposium 2010 and 6th Asian Rock Mechanics Symposium - Advances in Rock Engineering, 23-27 October, 2010, New Delhi, India PREDICTING PENETRATION RATE OF A TUNNEL BORING MACHINE USING ARTIFICIAL NEURAL NETWORK M. EFTEKHARI AND A. BAGHBANAN Mining Engineering Department...

... Artificial Intelligence neural network neural network model metals & mining underground structure peak particle velocity regression analysis predicator equation monitoring close proximity pillar JHA PPV vibration predictor equation lajkura ocm machine learning Upstream Oil...

...ISRM International Symposium 2010 and 6th Asian Rock Mechanics Symposium - Advances in Rock Engineering, 23-27 October, 2010, New Delhi, India SENSITIVITY ANALYSIS OF THE EFFECTIVE PARAMETERS DURING LASER PERFORATION IN SANDSTONE: A NEURAL NETWORK APPROACH REZA KESHAVARZI Member of Young...

... the quality and the quantity of information that can be gathered and transmitted to us in this traditional method, are very limited in many ways. Artificial Intelligence neural network laser pointer application displacement construction site creativity & intelligence information Akutagawa...

... and engineering modeling and analysis. It starts with development of expert systems and neural network models for rock mechanics and engineering problems. Now, it has been extended to methods for Intelligent bask analysis of rock mechanical parameters, Intelligent recognition of nonlinear mechanical models...

.... In this paper potential of both artificial neural network (AANs) and geostatistics has been used to predict permeability whit regards to Lugeon test results at Ag-chaie dam site on northwestern of Iran. ANNs are computer systems composed of processing elements that are interconnected in particular topology...

... reliable if used for low porosity formations. For the studied formation, in addition to the above mentioned methods, the trainable methods in particular neural networks, were also applied (fig 3). 0 0.25 Color: Maximum of NPHI 0. 00 0 0. 00 0 0. 02 5 0. 02 5 0. 05 0 0. 05 0 0. 07 5 0. 07 5 0. 10 0 0. 10 0...

...ISRM International Symposium 2008 5th Asian Rock Mechanics Symposium (ARMS5), 24-26 November 2008 Tehran, Iran 267 DETERMINATION OF THE FAILURE MODE IN ROCK BRIDGE USING NEURAL NETWORK TOOLS A. H. Ghazvinian1, S. Setayeshi2, V. Sarfarazi3 and S. A. Moosavi4 1 Academic Member, Rock Mechanics...

... Artificial Intelligence displacement evolutionary algorithm Engineering neural network Rock mechanics machine learning Upstream Oil & Gas numerical analysis Support Vector Machine optimization genetic algorithm excavation intelligent recognition intelligent rock mechanics...

... recently. such as data mining. learning basedon neural network. determining unknown parameter values ~sln~ gc~etic algorithm, search of unkno.wn structure of mathematical mo.d~1in global. optimum sp~c~ using genetic p:ogramll.ling. pphcatlons of these methods to assess risks of rock burst at great depth...

...Inverse problems and neural network approach in modeling the constitutive relations of rock and soil WANG JINGTAO & DING MEIYING School of Civil Engineering, Huazhong University of Sci.& Tech. Wuhan, China ABSTRACT: selling up the constitutive model of rock and soil is essentially an inverse...