Global ETD Search
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Showing 1 to 2 of 2 for “"Spatial-temporal feature engineering"”.
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Deep Neural Networks for Rate of Penetration Prediction: A Subsurface-Centric Approach Using Well Logs in the Volve Oil Field
… scores, elbow method, t-SNE, and PCA. Additional features—including cluster labels, cluster probabilities, and a spatial-temporal metric capturing ROP-depth trends—were engineered and integrated into the DNN model. This refinement reduced training loss by 78.05% and validation loss by 76.61%, …
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Deep Neural Networks for Rate of Penetration Prediction: A Subsurface-Centric Approach Using Well Logs in the Volve Oil Field
… scores, elbow method, t-SNE, and PCA. Additional features—including cluster labels, cluster probabilities, and a spatial-temporal metric capturing ROP-depth trends—were engineered and integrated into the DNN model. This refinement reduced training loss by 78.05% and validation loss by 76.61%, …