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Keywords: deep learning
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Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205556-MS
...Abstract Abstract This paper proposes a deep learning-based framework for proxy flow modeling to predict gridded dynamic petroleum reservoir properties (like pressure and saturation) and production rates for wells in a single framework. It approximates the solution of a full physics-based...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205571-MS
.... artificial intelligence accuracy neural network information machine learning upstream oil & gas spe annual technical conference vibration prediction result recurrent neural network operation lstm network lstm recurrent neural network drilling deep learning prediction vibration level...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205627-MS
... autoencoder (biLSTM-VAE) to project raw drilling data into a latent space in which the real-time bit-wear can be estimated. The proposed deep neural network was trained in an unsupervised manner, and the bit-wear estimation is demonstrated as an end-to-end process. machine learning deep learning...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205677-MS
... increased prior to the pipe sticking in some cases (thereby partly confirming our hypothesis) and were sensitive to large variations in the drilling parameters. machine learning deep learning neural network artificial intelligence time series data upstream oil & gas normal drilling operation...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205687-MS
...Abstract Abstract Screw pumps have been widely used in many oilfields to lift the oil from wellbore to ground. The pump failure and delayed repair means well shut and production loss. A deep learning model is constructed to quickly identify the working status and accurately diagnose the failure...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205772-MS
... & gas artificial intelligence reservoir simulation optimization production logging sensor data security correlation neural network ir 4 drilling operation automation production enhancement deep learning platform internet of things industry 4 algorithm iot petroleum industry...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 12–14, 2021
Paper Number: SPE-205720-MS
... learning fuzzy logic deep learning complex reservoir well logging structural geology directional drilling shale oil drillstem/well testing history matching fluid dynamics equation of state prediction exhibition usa unconventional reservoir algorithm neural network drilling operation oil...
Proceedings Papers

Paper presented at the SPE Asia Pacific Oil & Gas Conference and Exhibition, November 17–19, 2020
Paper Number: SPE-202271-MS
... reservoir characterization flow in porous media asset and portfolio management coalbed methane coal seam gas fluid loss control drilling operation annular pressure drilling coal bed methane drilling fluids and materials neural network deep learning drilling fluid selection and formulation...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 29–31, 2019
Paper Number: SPE-196542-MS
... slim-tube experiment results. The FCNN model proposed in this paper has extremely low time cost and high accuracy to predict CO 2 MMP, which is of great significance for CO 2 -EOR. machine learning deep learning Upstream Oil & Gas neural network accuracy dropout experiment flooding...
Proceedings Papers

Paper presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, October 29–31, 2019
Paper Number: SPE-196404-MS
... machine learning Artificial Intelligence Upstream Oil & Gas selection regression completion neural network forecasting deep learning accuracy application deep learning algorithm target variable presentation random forest learning algorithm representation dataset production...
Proceedings Papers

Paper presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition, October 23–25, 2018
Paper Number: SPE-191906-MS
... bias in this process can lead to significantly different results. An alternative approach based on deep learning is proposed. Convolutional Neural Networks (CNN) are utilized to rapidly predict several porous media properties from 2D greyscale micro-computed tomography images in a supervised learning...

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