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Keywords: neural network
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Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0257
... instability correlation coefficient fluid factor wellbore integrity wellbore stability prediction accuracy neural network independent variable reservoir geomechanics geological subdiscipline well diameter expansion rate coefficient expansion rate objective function kernel function correlation...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0293
... geology geologist asia government upstream oil & gas drilling operation europe government bit selection directional drilling norway government reservoir geomechanics algorithm china government drill bit geological subdiscipline neural network model optimization intelligent...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0326
... operation asia government pipe neural network lstm neuron classification problem drilling fluid management & disposal drill string different network model deep learning drilling fluids and materials united states government machine learning drilling equipment artificial intelligence...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0327
... ABSTRACT Data-driven models are used extensively for predicting rate of penetration (ROP). However, what data-driven algorithm is best suited to ROP prediction is currently undecided. In this paper, the data-driven model based on back propagation neural network (BP-ANN) and random forest (RF...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0254
... to predict Vs effectively and accurately. The characteristics of shear wave velocity prediction methods in different ultra-deep sandstone formations are compared, and an optimized neural network model is proposed. Training data is derived from real logging and logging interpretation results. The input...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0405
... and a novel dual-input neural network structure is designed. Sequential data such as hook load and rotary torque are input into Gated recurrent unit’ (GRU) network, and non-sequential data such as bit type are input into back-propagation neural network(BP), which is called Dual Input GRU(Di-GRU) ROP...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0482
... geology artificial intelligence united states government metals & mining upstream oil & gas sensor machine learning wave velocity dataset seismic wave velocity algorithm productivity geologist seismicity velocity model neural network reservoir characterization diameter...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0473
.... This study proposes a methodology for assessing the damage zones widths using artificial neural networks (ANNs). The database used to train the ANNs was built considering several numerical models which adopt geomechanical parameters, fault length, and maximum displacements as input variables to determine...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0394
... evaluation using logging big data and unsupervised clustering algorithms. Firstly, self-organizing mapping (SOM) neural network is used to cluster logging data. The formation is then divided into six drillability grades by analyzing the rate of penetration (ROP) distribution of each type of logging data. We...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0660
... geology neural network upstream oil & gas hyperparameter well logging deep learning europe government artificial intelligence log analysis geologist united states government geological subdiscipline porosity volve oil field zhang estimator norway government variation...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0650
... sedimentary rock geology neural network reservoir geomechanics sand production geologist accuracy upstream oil & gas asia government sandstone united states government rock type reservoir characterization prediction empirical formula machine learning algorithm production...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0598
... artificial neural networks (ANNs) and support vector machine (SVM). The logging data and drilling data were collected from the field. According to the correlation analysis between influencing factors and wellbore enlargement rate, 16 parameters were extracted, such as mud density, formation density porosity...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0755
... fluid dynamics upstream oil & gas mineral artificial intelligence geologist neural network contact angle chemical flooding methods united states government rock type enhanced recovery machine learning co 2 reservoir geomechanics climate change dataset geological subdiscipline...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0756
...), Decision Trees (DT), artificial neural networks (ANN), gradient boosting (GB), and adaptive gradient boosting (Adaboost)—were employed to construct the prediction models. With the optimal settings for the ML models, the breakdown pressure of the tight formations was accurately predicted with a 99% accuracy...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0757
... network was applied to predict the CO 2 trapping efficiency. The results showed that the deep neural network model can predict the trapping indices with a coefficient of determination above 0.95 and average absolute percentage error below 5%. INTRODUCTION Carbon capture and storage (CCS...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0760
... ABSTRACT This study presents a novel approach to Physics Informed Neural Networks (PINNs) called Variational Interface PINNs (VI-PINNs) for modeling physical systems with material interfaces. In conventional PINNs, a loss functional obtained from the residual of the strong form of governing...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0889
... government artificial intelligence discontinuity discontinuity trace mapping rock surface geology geologist neural network deep learning machine learning rock mass point cloud data south korea government deep learning model rock discontinuity trace information rock discontinuity trace mapping...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0860
... is deemed necessary through prediction models. The study aims to develop reliable machine learning models for predicting S-wave velocity in shale formation using conveniently available wireline logs. Six robust machine learning techniques such as decision tree (DT), artificial neural networks (ANN), K...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0831
..., the wide range of length and time scales involved in these sub-processes presents a significant computational burden. geologist upstream oil & gas artificial intelligence neural network geology reservoir characterization machine learning muckpile formation itasca consulting group...
Proceedings Papers

Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25–28, 2023
Paper Number: ARMA-2023-0905
... to the complex nature of rockburst as a seismic event and the non-linearity of data. To overcome these limitations, this paper presents a more reliable model for predicting rockburst damage potential (RDP). An Artificial Neural Network was established, and its parameters were optimized using the Adam optimizer...

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