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Keywords: neural network
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Proceedings Papers

Paper presented at the 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-0036
... robust 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...
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 of...
Proceedings Papers

Paper presented at the 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-0362
... of the natural fracture. In some cases, stress shadowing effects can even close the hydraulic fracture. Stress rotation around the natural fracture can make hydraulic fracture geometry complex, demanding more computational capacity and time to run numerical simulations. The neural network is a tool...
Proceedings Papers

Paper presented at the 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-0433
..., and are compiled into a database. The synthetic database can be used to train and test an initial deep neural network (DNN) representation of the subsurface, which can then be optimized by incorporating any available field measurements through transfer learning. This hybrid, physics informed DNN model...
Proceedings Papers

Paper presented at the 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-1863
... with the advantages of neural network algorithm in data fault tolerance and prediction, and the ability to extrapolate more data based on existing data, the basic principles of neural network are established. The multi-layer back-propagating neural network uses the existing drilling and fracturing data...
Proceedings Papers

Paper presented at the 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-2105
... between the diameter of the bridge particle and the fracture width should be no larger than 1mm; otherwise, the fracture would fail to seal. For the fracture greater than 4mm, secondary bridging particles should be added to reduce the sealing time. A neural network model was developed and further verified...
Proceedings Papers

Paper presented at the 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-2187
... wellbore neural network modeling ordos basin minimum horizontal stress fracture production performance hydraulic fracturing Upstream Oil & Gas oil reservoir horizontal stress stimulation fluid type reservoir workflow China sand body 1. INTRODUCTION In Ordos basin, tight oil of the...
Proceedings Papers

Paper presented at the 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-2150
... only a little attention. Here, we have developed an artificial neural network model to determine lithofacies as a function of drilling data (i.e. depth (D), rate of penetration (ROP), drilling mud flow rate (GPM), drill string rotation speed (RPM), weight on bit (WOB), stand pipe pressure (SP), total...
Proceedings Papers

Paper presented at the 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-2181
... abdulraheem machine learning flow in porous media correlation Reservoir Characterization Fluid Dynamics porosity functional network mobility index Symposium neural network estimation permeability society of petroleum engineers Tariq neural function determination carbonate reservoir Upstream...
Proceedings Papers

Paper presented at the 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-2174
... approach is the unidentified percentage of uncertainty associated with converting dynamic Young's modulus and Poisson's ratio values to static values. well logging static moduli porosity compressive strength compressional wave velocity Reservoir Characterization neural network machine...
Proceedings Papers

Paper presented at the 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-0509
... ABSTRACT: Operators and service companies are always interested to have a clear insight about the rate of penetration (ROP) since it will provide a good estimate for the time and the cost of the drilling operations. The aim of this work is to use recurrent neural networks (RNNs) to accurately...
Proceedings Papers

Paper presented at the 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-0511
... purpose of this work is to utilize dynamic networks to accurately predict V S for carbonate formations and to compare between the conventional neural network and the dynamic networks. Dynamic and conventional neural networks were created to predict V S from compressional wave velocity (V P ), bulk...
Proceedings Papers

Paper presented at the 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-1628
... results. Bayesian Inference Yagiz Artificial Intelligence TBM penetration rate assessment international journal neural network machine learning artificial neural network penetration rate tunnel underground space technology neuron adoko Rock mechanics Upstream Oil & Gas...
Proceedings Papers

Paper presented at the 52nd U.S. Rock Mechanics/Geomechanics Symposium, June 17–20, 2018
Paper Number: ARMA-2018-247
... which were carried out on 45 Asmari and Sarvak limestone core specimens are used. Then, as an artificial intelligence method, artificial neural networks were developed to correlate E s and E d data. After comparing the results of the suggested method with correlations which were established between...
Proceedings Papers

Paper presented at the 52nd U.S. Rock Mechanics/Geomechanics Symposium, June 17–20, 2018
Paper Number: ARMA-2018-491
.... Artificial Intelligence Upstream Oil & Gas simulation of human behavior neural network node paluszny complex reservoir stress intensity factor initial fracture fracture network finite element-based simulation mesh paris law geometry hydraulic fracturing fracture pattern fracture growth...
Proceedings Papers

Paper presented at the 52nd U.S. Rock Mechanics/Geomechanics Symposium, June 17–20, 2018
Paper Number: ARMA-2018-744
... mathematical model neural network bulk density Exhibition functional network travel time Mahmoud s-wave travel time prediction application static poisson society of petroleum engineers 1. INTRODUCTION Elasticity is the material property which tends to oppose deformation in shape and volume. Hooke...
Proceedings Papers

Paper presented at the 52nd U.S. Rock Mechanics/Geomechanics Symposium, June 17–20, 2018
Paper Number: ARMA-2018-1098
... ABSTRACT: In this study, an artificial neural networks (ANN) model as an artificial intelligence (AI) technique is proposed to determine the formation pore pressure from data of two critical drilling parameters named mechanical specific energy and drilling efficiency. These parameters (MSE...
Proceedings Papers

Paper presented at the 52nd U.S. Rock Mechanics/Geomechanics Symposium, June 17–20, 2018
Paper Number: ARMA-2018-905
... not measured during well logging for cost and time-saving purposes. For this reason, various prediction methods including regression analysis and artificial neural network (ANN) can be used for predicting the shear wave velocity. This study was conducted on dataset taken from a producing section in SE...
Proceedings Papers

Paper presented at the 52nd U.S. Rock Mechanics/Geomechanics Symposium, June 17–20, 2018
Paper Number: ARMA-2018-105
... then establish the risk prediction model of lost circulation while drilling. Geological characteristics and operational drilling parameters are both taken into consideration, and the risk level corresponds to loss rate. After collecting numerous data related to lost circulation, BP neural network...
Proceedings Papers

Paper presented at the 52nd U.S. Rock Mechanics/Geomechanics Symposium, June 17–20, 2018
Paper Number: ARMA-2018-1064
... deformation modulus estimation anfis model rock mass equation coefficient neural network 1. INTRODUCTION In general, in rock engineering projects the design analysis of a wide range of rock structures such as mine drifts, tunnel, dam foundations and other rock excavations and supports, is an...

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