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Keywords: neural network
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Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1446
... is needed. artificial intelligence segmentation grout part machine learning upstream oil & gas nakashima deep learning specimen cement grout fracture fusionnet automatic segmentation deep learning algorithm neural network x-ray ct imaging ct image x-ray ct ct value histogram...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1025
.... With the influence of irregular shape and the wall effect, the settling behavior of particles in fracture is complex to be described accurately by the traditional data fitting methods. Artificial neural network (ANN) is a biological inspired machine learning method, which has significant advantages in solving...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1669
... model carbonate formation wellbore integrity neural network sonic log application neuron sonic travel time ARMA 21 1669 Data Driven In-Situ Sonic Log Synthesis in Carbonate Reservoirs Hadi, F.A. University of Baghdad, Baghdad, IQ Nygaard, R. University of Oklahoma, Norman, OK, US Copyright...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1888
... learning reservoir geomechanics log analysis artificial intelligence neural network wellbore integrity wellbore design reservoir characterization well logging upstream oil & gas correlation static young kpa montney shale formation study area laboratory test data coefficient static...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1907
... based on the knowledge of the dynamic Young's Modulus (E dyn ). Nowadays, Edyn is estimated from the shear and compressional velocities and bulk density, which in many cases may not be available. This study introduces an empirical equation developed based on an optimized artificial neural networks (ANN...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1226
..., the graph methods still suffer from limitations. One of them is the poor generalization ability of these graphs. Hence, in this paper, Artificial Neural Network (ANN) based graphs are proposed as alternative tools capable of relating accurately the dilution to the stope dimensions and the rock mass quality...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1237
... workflow log analysis wellbore design mud slowness algorithm inversion neural network prioul lei anisotropy illustration ARMA 21 1237 Automatic Borehole Sonic Processing for High-Resolution Mechanical Properties Ting Lei, Lin Liang, Michiko Hamada, Romain Prioul, Adam Donald, Edgar Ignacio...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1243
... stochastic hydro-thermal simulations and neural network development. A generic reservoir with a doublet system is used for the numerical simulations with Monte Carlo sampling of input parameters from the uniform distributions of formation properties and operations. The simulation results are used to extract...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1764
... works approaching pore pressure estimation with the advantages of machine learning algorithms have been increasing in recent years. Hu et al., 2013 show the learning powerful capability of a back-propagation neural network for pore pressure with high accuracy. Naeini et al., 2019 present the use...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1418
... reflect the continuous variation trend of stratigraphic mineralogical characteristics. Well logging data can provide effective mineralogical information and have the advantages of large amount of information, continuous measurement and high vertical resolution. A multilayer perceptron neural network...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1703
...% [15-18] . flow in porous media engineering pore shape machine learning upstream oil & gas neural network model artificial intelligence fluid dynamics international journal evolution machine learning method permeability model reservoir permeability data neural network pore...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1915
... no longer can meet the needs of modeling. As a new sequence data analysis technology, Recurrent Neural Network (RNN) can fit the complex logical and nonlinear relations in the sequence data, and can predict the next data. In this study, 72 groups of rock stress-strain curve data in the target block...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1454
... before drilling. Long and short-term memory artificial neural network LSTM is a kind of cyclic neural network, which is often used for the prediction of various time series problems. Deep series and time series are essentially the same, that is, the previous data point and the next data point have...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1600
... and great potential of using the DL techniques as a fast and robust alternative of FEA analysis in the geotechnical and mining industries, and is particularly well matched with the modern technologies for remote sensing and rock characterization. neural network machine learning deep learning...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1987
... for pore pressure prediction depend on well logging, formation properties, and combination of logging and drilling parameters. These data are not available for all wells in all sections. The objective of this paper is to use artificial neural networks (ANNs) to develop a model to predict the formation...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-2027
... earthquake recurrence interval dataset neural network field application transfer learning approach target dataset standalone model xgboost ARMA 21-2027 Learning from laboratory earthquakes: A transfer learning approach to active source seismic monitoring Alireza Sepehrinezhad, A.S. Pennsylvania...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1914
... an artificial neural network (ANN). The data used was collected during drilling different formations with a complex lithology. A cleaned data set (2,926 measurements) was used for building the ANN model. The model was trained, tested, and optimized to provide high accuracy prediction for UCS. The results showed...
Proceedings Papers

Paper presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, June 18–25, 2021
Paper Number: ARMA-2021-1916
... drilling fluid management & disposal drilling operation upstream oil & gas accuracy king fahd university elkatatny exhibition neural network petroleum & mineral drilling parameter machine learning fuzzy logic abdulraheem correlation ecd artificial neural network model saudi...
Proceedings Papers

Paper presented at the 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-0036
... empirical model for compressional and shear wave transit times using artificial intelligence techniques for unconventional reservoirs. For this purpose, well logs data was used from a tight sandstone formation to predict the transit times. Artificial neural networks (ANN) was used in this study. The ANN...
Proceedings Papers

Paper presented at the 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-0243
... correlation is subjected to several shortcomings, including the fact that they cannot be generalized and in many cases quality cores are not available to conduct the calibration. In this study, we used the Artificial neural network (ANN) to estimate the elastic properties of the Bakken Formation. A total...

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