... Abstract Rate of penetration (ROP) is an important parameter that determines the success and cost of a drilling operation. In this paper, the research approach is based on different artificial neural network (ANN) models to predict ROP for oil and gas wells in Nam Con Son basin. First...

... not suffice to serve as an instant on-site evaluation tool. In this regard, a direct relationship between the random seeds of positioning errors and seepage flow rate is expected to be explored, and based on this, a surrogate model using the AI approach is established. A novel physical-based Neural Network...

... interpretation. With the advent of convolutional neural networks (CNN), the deep learning method have made promising advances in the field of image recognition [1]. Many researchers have applied deep learning methods to recognize GPR images. Lameri et al. designed a landmine detection algorithm based on CNN...

..., namely the parametric and teacher models and the SGAN. The parametric model is designed to generate a 3D underground space model by controlling the key parameters. The teacher model rates the visual comfort level of the generated model and trains the SGAN and could be a neural network or another...

...11th Conference of Asian Rock Mechanics Society IOP Publishing IOP Conf. Series: Earth and Environmental Science 861 (2021) 072001 doi:10.1088/1755-1315/861/7/072001 Image-based crack recognition of tunnel lining using residual U-Net convolutional neural network S K Hou1, Z G Ou1,2, P X Qin3, Y L...

... of the developed genetic algorithm optimization of artificial neural network model when predicting uniaxial compressive strength using measurement-while-drilling data. Firstly, the objective tunnel is divided into four sections based on the geological conditions of the rock mass. Secondly, prediction model...

... model of CNN-LSTM neural network combining convolutional neural network (CNN) and long-short-term memory neural network (LSTM) is proposed. This model uses the good feature extraction capabilities of CNN and the special memory prediction function of LSTM neural network to achieve accurate prediction...

... Abstract In this paper, an artificial neural network-based dynamic prediction model of penetration rate (PR) is proposed. Four tunnel boring machine (TBM) operational parameters, including cutterhead rotational speed (RPM), cutterhead torque (T), total thrust (F), and advance rate (AR...

... 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...

... fluid modeling equation of state hydraulic fracturing Upstream Oil & Gas Modeling & Simulation neural network roughness fracture network discrete fracture network heterogeneous aperture distribution aperture heterogeneity fluid flow DFN model isrm international symposium...

... 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...