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Keywords: neural network
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Proceedings Papers

Paper presented at the SPE Canadian Energy Technology Conference and Exhibition, March 13–14, 2024
Paper Number: SPE-218116-MS
... geologist neural network artificial intelligence geological subdiscipline application reservoir simulation mineralogy machine learning prediction vector composition mineralogical composition elemental composition neuron formation clarkson canada ghanizadeh yang Introduction...
Proceedings Papers

Paper presented at the SPE Canadian Energy Technology Conference and Exhibition, March 13–14, 2024
Paper Number: SPE-218111-MS
... investigation. This new HDP architecture seamlessly integrates a physics-based equation into the framework of a deep neural network model. The training dataset encompasses a wide array of influencing factors on production rates, encompassing information that may not readily conform to conventional physical...
Proceedings Papers

Paper presented at the SPE Canadian Energy Technology Conference and Exhibition, March 13–14, 2024
Paper Number: SPE-218029-MS
... utilizing Physics-Informed Neural Networks (PINNs) to predict adsorption isotherms across diverse shale cores, integrating Langmuir adsorption theory into a data-driven model. By collecting a limited core dataset and leveraging automatic differentiation techniques, the PINN systematically incorporates...
Proceedings Papers

Paper presented at the SPE Canadian Energy Technology Conference and Exhibition, March 13–14, 2024
Paper Number: SPE-218105-MS
.... geologist clastic rock sedimentary rock multistage fracturing deep learning rock type artificial intelligence sequence fracture china neural network hydraulic fracturing mudstone conductivity hyperparameter optimization multistage hydraulically geology mudrock algorithm information...
Proceedings Papers

Paper presented at the SPE Canadian Energy Technology Conference and Exhibition, March 13–14, 2024
Paper Number: SPE-218050-MS
... and provides valuable insights into consideration of the geological uncertainty in CO 2 storage modeling and design of MMV program for CO 2 storage projects. sustainability neural network geologist fluid dynamics deep learning reservoir simulation structural geology risk and uncertainty...
Proceedings Papers

Paper presented at the SPE Canadian Energy Technology Conference and Exhibition, March 15–16, 2023
Paper Number: SPE-212811-MS
.... upstream oil & gas pvt measurement waterflooding reservoir simulation gas injection method subsurface storage flow in porous media co 2 miscible method fluid dynamics united states government neural network social responsibility artificial intelligence sustainability sustainable...
Proceedings Papers

Paper presented at the SPE Canadian Energy Technology Conference and Exhibition, March 15–16, 2023
Paper Number: SPE-212723-MS
... as the ML method to build the ensemble of models to forecast production. In addition to random forest algorithm, we also tried deep neural networks to generate the forecasting model. It was observed that random forest models are faster to train, provided high accuracy with little tuning of the model, worked...
Proceedings Papers

Paper presented at the SPE Canadian Energy Technology Conference and Exhibition, March 15–16, 2023
Paper Number: SPE-212754-MS
..., the research about comparisons of different deep learning algorithms lacks. In this work, three different deep learning algorithms, including the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Transformer, are applied to forecast the productivities of a three-horizontal-well EGS...
Proceedings Papers

Paper presented at the SPE Canadian Energy Technology Conference and Exhibition, March 15–16, 2023
Paper Number: SPE-212756-MS
... of these samples, a corresponding oil production rate time series is obtained using a reservoir simulation model; this model was built using publicly available data from Norther Alberta SAGD implementations. Afterwards, the base model and correction term are identified using Long-Short Term Memory neural networks...
Proceedings Papers

Paper presented at the SPE Canadian Energy Technology Conference, March 16–17, 2022
Paper Number: SPE-208885-MS
... to the finite element programs. A variety of ANN frameworks were developed for multiscale upscaling of mechanical response and successfully applied to obtain equivalent homogeneous mediums at microscopic scales ( Unger and Könke 2008 , Le et al. 2015 , Vasilyeva et al. 2020 ). Recurrent neural networks (RNN...
Proceedings Papers

Paper presented at the SPE Canadian Energy Technology Conference, March 16–17, 2022
Paper Number: SPE-208962-MS
... that machine learning algorithms have been applied to a SAGD data set of this size. machine learning oil sand enhanced recovery artificial intelligence modeling & simulation bayesian inference optimization problem reservoir simulation neural network production control reservoir surveillance...

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