... and future work are discussed in the paper. prediction training data prediction accuracy prediction error neural network test data line tension simulation data machine learning dataset artificial intelligence lng static simulation data liquified natural gas validation simulation mooring...

... model pump fault state pump state cross validation training data upstream oil & gas probability monitoring hyperparameter fault state different fault state discharge pressure data triplex pump machine learning artificial intelligence loss function accuracy suction Deep...

... fraction hydrate risk process variable field data classification model gas hydrate training data artificial intelligence offshore technology conference algorithm regression hydrate neural network srivastava machine learning upstream oil & gas pipeline Baghban , A. , Ahmadi...

... the operations of their upstream assets. artificial intelligence history dataset equipment problem drilling comment digital knowledge layer spacy knowledge graph accuracy training data upstream oil & gas algorithm database stanford core nlp precision machine learning offshore technology...

... performance. artificial intelligence neural network video analytic accuracy machine learning algorithm video video data deep learning model architecture rig deep learning connection process sequence drilling connection computer vision training data classification information upstream...

... & gas monitoring operator operation maintenance algorithm hyperparameter failure prediction application assessment learner xgboost classification prediction training data estimator Hoffmann , G. A. Failure Prediction in Complex Computer Systems: A Probabilistic Approach...

... sensor data more important than others, such as temperature versus pressure? Is flow meter data necessary, or can VFM be used where no flow meters exist?? What is expected or necessary to train VFM models? How frequently is training needed? Can a VFM be applied outside of its training data set...

... . Artificial neural networks(ANN) have recently attracted a great deal of attention for their ability to "learn," or "estimate" the mapping operator from training data which describe the mapping relationship. Among neural networks for function approximation, the feed forward network trained by error back...

...) into a selected number of classes without any further guidance. Qualitative classification: Supervised classification methods that make use of a set of training data or ref- erence values from pre-existing reservoir knowledge manually introduced to guide classification. Quantitative classification: Supervised...