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Keywords: artificial 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-2021-1668
... abdulraheem exhibition petroleum science arabia reservoir geomechanics structural geology drilling parameter artificial neural network dataset ahmed mahmoud ARMA 21 1668 Bulk Density Prediction While Drilling Complex Lithology Using Functional Networks Model Ahmed, A.H., Elkatatny, S., Gamal, H...
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-2021-1914
... 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 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-0511
... learning Artificial Intelligence Reservoir Characterization predict shear wave velocity conventional neural network artificial neural network neural network narx network shear wave velocity alkinani carbonate formation dynamic network porosity cfbnn intelligent system log analysis Upstream...
Proceedings Papers

Paper presented at the 53rd U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2019
Paper Number: ARMA-2019-1628
... of the 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 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-2019-2174
... learning correlation Artificial Intelligence local correlation western desert Poisson mechanical earth model log analysis Upstream Oil & Gas rock mechanics lab mechanical property unconfined compressive strength artificial neural network moduli case study 1. INTRODUCTION To maximize...
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 Iraq...
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-2018-247
... 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 51st U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2017
Paper Number: ARMA-2017-0771
... of this paper is to develop a robust and an accurate model for estimating static Poisson’s ratio based on 610 core sample measurements and their corresponding wireline logs data using artificial neural network. The obtained results showed that the developed ANN model was able to predict the static Poisson’s...
Proceedings Papers

Paper presented at the 51st U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2017
Paper Number: ARMA-2017-0080
...) and brittleness of rock samples. Rock brittleness is a function of UCS and Brazilian tensile strength (BTS). Thus, collected data of these parameters were hired to develop and train artificial neural networks (ANN) as an artificial intelligence (AI) method for estimation of drilling tool wear using data of rock...
Proceedings Papers

Paper presented at the 50th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2016
Paper Number: ARMA-2016-124
... proportionality between cutting forces and uniaxial compressive strength, and the cutting force not reducing to zero when the pick angle fell to zero. In this paper we use an artificial neural network framework to develop a model for predicting peak cutting forces in shale. Artificial neural networks...
Proceedings Papers

Paper presented at the 47th U.S. Rock Mechanics/Geomechanics Symposium, June 23–26, 2013
Paper Number: ARMA-2013-605
... extraction efficiency and to generate a safe mine working environment. The paper describes two predictive models based on artificial neural network (ANN) and statistical analysis for predicting the height of destressed zone. A suitable dataset including the panel and the roof strata geometrical...
Proceedings Papers

Paper presented at the 46th U.S. Rock Mechanics/Geomechanics Symposium, June 24–27, 2012
Paper Number: ARMA-2012-244
... of various parameters that leads to reduction of the costs. Artificial neural network (ANN) has an efficient capability of combining different parameters to predict different situations. According to ANN structure, it can get the effective parameters as the inputs to predict and evaluate the value...
Proceedings Papers

Paper presented at the 45th U.S. Rock Mechanics / Geomechanics Symposium, June 26–29, 2011
Paper Number: ARMA-11-189
.... Artificial Neural Network (ANN) is a novel approach for solving engineering problems. ANNs, like people, learn by example. They use input–output parameters to be trained to recognize the correct relationship. These methods are able to consider all effective parameters simultaneously and also develop...
Proceedings Papers

Paper presented at the 45th U.S. Rock Mechanics / Geomechanics Symposium, June 26–29, 2011
Paper Number: ARMA-11-533
... of an Artificial Neural Networks (ANN) based model for the prediction of UCS from Schmidt hardness. Schmidt hardness test (SHT) is a nondestructive test method which provides fairly good correlation about the strength of rocks. SHT can be easily and quickly conducted with a portable device known as Schmidt Hammer...
Proceedings Papers

Paper presented at the 44th U.S. Rock Mechanics Symposium and 5th U.S.-Canada Rock Mechanics Symposium, June 27–30, 2010
Paper Number: ARMA-10-353
... ABSTRACT: In recent years, artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. In this paper, an ANN model is developed for predicting standard penetration tests (SPT) by microtremor array results. SPT gives an indication...
Proceedings Papers

Paper presented at the 44th U.S. Rock Mechanics Symposium and 5th U.S.-Canada Rock Mechanics Symposium, June 27–30, 2010
Paper Number: ARMA-10-527
... prominence. Prediction of penetration depth is so important and useful for evaluating the efficiency of laser perforation. In this paper, an artificial neural network has been developed to predict the penetration depth during laser perforation in limestone. The input parameters which are the effective...
Proceedings Papers

Paper presented at the Golden Rocks 2006, The 41st U.S. Symposium on Rock Mechanics (USRMS), June 17–21, 2006
Paper Number: ARMA-06-1165
... using linear rock cutting machine (LCM) developed under NATO-TU Research program. Some predictor equations using regression analysis are developed to estimate the performance of mechanical excavators utilizing point attack tools. Artificial neural network (ANN) analyses are also performed to see whether...

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