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Keywords: artificial neural network
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Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3722944-MS
... for the Potash Area using Artificial Neural Network (ANN) and Multilinear Regression machine learning models. The decision on what model should be used was guided by the efficiency and accuracy of the models. The study utilized drilling and well logs parameters such as Deep & Shallow Laterolog Resistivities...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, June 20–22, 2022
Paper Number: URTEC-3725863-MS
... of porosity and permeability. Different machine learning algorithms have been developed including Linear Regression (LR), Artificial Neural Network (ANN), Random Forest Regressor (RFR), Extreme Gradient Boosting (XGBoost), Adaptive Booster Regressor (AdaBoost), and Support Vector Regression (SVR), to predict...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 20–22, 2020
Paper Number: URTEC-2020-2751-MS
... be answered under the premise of maximizing profitability. In this study, we combine the recently developed artificial neural network (ANN) model with a global sensitivity analysis method to present a reduced-order model for addressing these questions. We developed ANN models to predict the oil and gas...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-598-MS
... was then divided into training and test sets. The test set was set aside for validation to prevent any training bias. Data visualization and statistical analysis was carried out, which revealed patterns and features within the training data. Three separate artificial neural networks (ANNs) were then constructed...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 22–24, 2019
Paper Number: URTEC-2019-897-MS
... characteristics. Our results indicate that K-Means clustering yields best performance on data classification than all other tested methods while the elastic moduli estimation from Artificial Neural Network (ANN)is most accurate than Support Vector Machine (SVM), Multivariate Linear Regression (MLR...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, July 23–25, 2018
Paper Number: URTEC-2877021-MS
... of clay. The data was randomly partitioned into a 70:30 split for training and validation data set respectively. Model competition among a suite of machine learning algorithms such as Linear Regression, Artificial Neural Networks (ANNs), Decision Trees, Gradient Boosting and Random Forest was used...
Proceedings Papers

Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, August 12–14, 2013
Paper Number: URTEC-1619574-MS
... and compositional properties. Finally, we use the depth-by-depth estimates of porosity, Total Organic Content (TOC), fluid saturations, volumetric concentrations of mineral constituents, and elastic properties to classify rock types in the reservoir using unsupervised artificial neural network. We successfully...

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