1-16 of 16
Keywords: neural network
Close
Follow your search
Access your saved searches in your account

Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Proceedings Papers

Paper presented at the SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, January 17–18, 2023
Paper Number: SPE-214475-MS
...AI-Physics Model Training the Deep Neural Network (DNN) is the main step for creating an AI-physic model with the available data that comes from: The simulation results Static data such as the well geometry, well location, and static grid properties Time series created from...
Proceedings Papers

Paper presented at the SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, January 17–18, 2023
Paper Number: SPE-214460-MS
... significant evidence of a difference in prediction performance between extreme gradient boost and random forest. asia government upstream oil & gas deep learning united states government neural network artificial intelligence reservoir characterization accuracy well logging hypothesis...
Proceedings Papers

Paper presented at the SPE Symposium Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, January 17–18, 2023
Paper Number: SPE-214468-MS
... solution utilizes the data from the PDC cutter and the scaled-drilling rig structure to identify the optimum range of the drilling parameters depending on the mechanical properties of the rock samples. Artificial Neural Network (ANN) is utilized to predict the rate of penetration for samples with different...
Proceedings Papers

Paper presented at the SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry, October 18–19, 2021
Paper Number: SPE-208642-MS
.... Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q...
Proceedings Papers

Paper presented at the SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry, October 18–19, 2021
Paper Number: SPE-208663-MS
... Artificial Intelligence Techniques were tested to predict CGR logs; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). A Sugeno-type FIS structure using subtractive clustering demonstrated the best prediction with correlation coefficient of 0.96 and mean absolute percentage...
Proceedings Papers

Paper presented at the SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry, October 18–19, 2021
Paper Number: SPE-208648-MS
... virtual flow metering systems has become a focal point for many companies. This paper discusses the importance of flow modelling for virtual flow metering. In addition, main data-driven algorithms are introduced for the analysis of several dynamic production data sets. Artificial Neural Networks (ANN...
Proceedings Papers

Paper presented at the SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry, October 18–19, 2021
Paper Number: SPE-208665-MS
... mandate greater exposure times. With increasing exposure time, however, more wear is put on the X-Ray tube and longer cooldown periods are required, inevitably limiting the temporal resolution of the particular phenomena under investigation. In this work, we propose a deep convolutional neural network...
Proceedings Papers

Paper presented at the SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry, October 18–19, 2021
Paper Number: SPE-208653-MS
... gradient boost, Gradient boost regressor and Linear regressor) and a deep learning algorithm (Artificial Neural Network). The output results from the set of supervised learning algorithms and Deep Learning algorithms were compared in terms of their accuracies in predicting the hydrate formation...
Proceedings Papers

Paper presented at the SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry, October 18–19, 2021
Paper Number: SPE-208646-MS
... on surface measurements which have the major influence on the bit torque (downhole torque) values while drilling. Artificial intelligence technology and its related applications such as; artificial neural network (ANN), support vector machine (SVM) and adaptive neuro fuzzy interference system (ANFIS...
Proceedings Papers

Paper presented at the SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry, October 18–19, 2021
Paper Number: SPE-208649-MS
... the inventory issue. Therefore, it is desirable to reduce workover time and optimize production by providing early warning of ESP failures ( Fig. 1 ). artificial lift system neural network machine learning deep learning natural language upstream oil & gas operating condition electrical...
Proceedings Papers

Paper presented at the SPE Intelligent Oil and Gas Symposium, May 9–10, 2017
Paper Number: SPE-187936-MS
... unhealthy states. unhealthy condition information false alarm machine learning compressor deviation maintenance activity Artificial Intelligence poor health sensor value availability neural network society of petroleum engineers health confidence interval mathematical representation...
Proceedings Papers

Paper presented at the SPE Middle East Intelligent Energy Conference and Exhibition, October 28–30, 2013
Paper Number: SPE-167403-MS
... failures. data mining Upstream Oil & Gas key performance indicator maintenance compressors engines and turbines KPI information neural network performance indicator dry gas seal Seal System process gas predictive analytics application seal performance Indicator asset management...

Product(s) added to cart

Close Modal