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Keywords: neural network
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Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-2204
... neural network reservoir characterization random forest algorithm carbonate heterogeneity engineering regression resolution improved upscaling method multiple-point statistics mostaghimi subjective evaluation ARMA 22 2204 Improved Upscaling Methods for Carbonate Rock Image Data Anup Kumar...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-2310
... is low and the cost is high. Therefore, a fast and accurate method for lithology identification is urgently needed. decision tree learning structural geology neural network well logging artificial intelligence lithology identification machine learning log analysis lithology upstream oil...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0339
... artificial intelligence machine learning correlation shear wave velocity data analysis algorithm reservoir characterization neural network slowness regression log analysis journal williston basin shear slowness prediction linear regression ARMA 22 339 Prediction of Shear Wave Velocity...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0351
... will be significantly reduced. Therefore, it is necessary to obtain the wear condition of the drill bit in time to ensure the drilling speed. [2] neural network cutter deep learning bit selection opencv upstream oil & gas artificial intelligence detection bit design machine learning beijing china...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0358
...-driven prediction matrix petroleum science neural network log analysis hyperparameter journal shear wave velocity optimization lstm ARMA 22 0358 Intelligence-driven Prediction of Shear Wave Velocity Based on Gated Recurrent Unit Network Xuechen Li, Xinfang Ma, Fengchao Xiao, Cong Xiao, Fei...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0364
... and is difficult to obtain the global optimal solution. In this paper, an optimization method of hydraulic fracturing design for horizontal shale gas well based on artificial neural network (ANN) and genetic algorithm (GA) is proposed. On the basis of collecting geology, engineering, and production data...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0375
... is low. artificial intelligence prediction rop constraint neural network drillability journal drillability constraint engineering upstream oil & gas machine learning reliability algorithm evaluation rock drillability constraint penetration prediction china university wang...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0408
... are usually interpreted manually with associated low efficiency and high cost, and to overcome these deficiencies here we propose a high performance combined de-noising procedure based on the median filter and deep learning techniques. We investigate the use of a convolutional neural network (CNN) method...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0409
... can adjust the drilling parameters in time to avoid the occurrence of stick-slip. The Recurrent neural network (RNN) has unique advantages in time series prediction. (Meor et al., 2021) established hook load prediction model based on RNN, which effectively prevented the occurrence of the sticking...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0415
... of the 1D flow with chemical reaction was developed by (Panfilov et al., 2016). This model was verified qualitatively by the result of laboratory experiments for the uranyl sulfate. uranium mining machine learning prediction neural network metals & mining morlet wavelet transformation...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0031
... and using empirical correlations to estimate these properties may not solve the problem. In this work, an artificial neural network algorithm was developed using MATLAB software to predict the porosity, the volume of shale and water saturation logs of well F14 from the Volve Field. Through multiple...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0052
..., and neural network) are tested to classify the event based on forecasted pressure data. The proposed architecture is thoroughly explored and successfully implemented to forecast and predict the likelihood of a TSO event 30-second ahead of time with an accuracy of 90%. This solution readily shows...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0441
... be estimated from drilling activity; however, in low permeability formations such as shales, estimating the pore pressure is complicated. drillstem/well testing neural network artificial intelligence reservoir geomechanics chemmakh wellbore design algorithm log analysis upstream oil & gas...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0461
... model and an artificial neural network model have been trained using conventional well logs obtained from the Volve oil field in the Norwegian North Sea then the prediction accuracy of the two models have been tested using several cases from the Eagle Ford and the Midland basins. The testing results...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0478
... artificial intelligence simulation of human behavior below criterion loading upstream oil & gas simulation neural network indenter maxwell viscosity reservoir characterization pierre ii shale cuttings creep poisson lab-based simulation bingham viscosity illustration kelvin viscosity...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0485
... and "Difficult topic" in current research. annular pressure drilling upstream oil & gas drilling fluids and materials drilling operation drilling fluid management & disposal artificial intelligence neural network prediction equation machine learning characteristic reliability huang...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0501
... reservoirs by integrating coupled reservoir and geomechanics simulations, cash flow, and neural networks. Injection stops when a fault approaches the shear yield limit at a distance fixed by a safety factor. We apply the method to a reservoir geometry inspired in a faulted sand in the Gulf of Mexico Coast...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0261
... formation and the other from a heterogenous rock formation. Out of all models, the proposed Convolutional Neural Network coupled with Monte Carlo dropout provides the most robust results with adequate quantification of prediction uncertainty. artificial intelligence machine learning neural network...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0279
..., and 28 days. The chemical-mineralogical synthesis of the cement and fineness factor significantly affects the cement strength for the well’s life cycle to avoid unwanted fluid leakage. This study aims to develop two Artificial Intelligence algorithms: Artificial Neural Networks (ANN) and Support Vector...
Proceedings Papers

Paper presented at the 56th U.S. Rock Mechanics/Geomechanics Symposium, June 26–29, 2022
Paper Number: ARMA-2022-0775
.... This paper establishes the shear wave prediction model based on two artificial intelligence methods (Probabilistic Neural Network (PNN) and Deep Feed-forward Neural Networks (DFNN)) using Wellington oil field data in south central Kansas. Neural networks are potentially superior to linear or multi-linear...

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