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Keywords: machine learning
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Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 16–19, 2023
Paper Number: PSIG-2306
.... This could be used as the primary input to a machine learning model that PSIG 2206 A Stochastic Approach to Slack Line Flow in Online Models 7 could also include upstream and downstream pressure measurements, flow rate measurements, batch locations, etc. Over time, it is expected that a sufficient number...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 16–19, 2023
Paper Number: PSIG-2303
... to a particular flowing scenario. midstream oil & gas artificial intelligence diffusion coefficient upstream oil & gas machine learning equation laplace transform crude batch united states government canada government psig 2303 brett christie psig 2303 crude linefill condensate...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 16–19, 2023
Paper Number: PSIG-2313
... Abstract Statistical and machine learning approaches to pipeline leak detection can benefit from augmentation with simple physical models that can predict line pack, particularly in cases where the fluids involved change density strongly with temperature and pressure. Determining the system...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 10–13, 2022
Paper Number: PSIG-2210
... real time system production control artificial intelligence upstream oil & gas ed nichola psig 2210 clay soil completion monitoring systems/intelligent wells production monitoring pipeline leak detection temperature change ground temperature pore space machine learning...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 10–13, 2022
Paper Number: PSIG-2213
... are key elements of the NG network infrastructure. This work aims to develop a method for offline monitoring of the odorization process within a CGS located in central Italy, based on the exploiting of the odorization station dataset through several machine learning models development, to evaluate...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 10–13, 2022
Paper Number: PSIG-2201
... flow assurance wax inhibition paraffin remediation scale remediation oilfield chemistry machine learning hydrate remediation asphaltene remediation remediation of hydrates correlation neural network asphaltene inhibition accuracy hydrate inhibition upstream oil & gas...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 10–13, 2022
Paper Number: PSIG-2204
... and operated at a sufficient fluid velocity to avoid solid deposition. Recent studies at the Tulsa University Sand Management Projects (TUSMP) have shown that Artificial Intelligence – Machine Learning (ML) methods can be effectively and accurately used in predicting minimum particle transport velocities...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 3–7, 2021
Paper Number: PSIG-2118
..., then a method for 14: E E + e(iPi| Error from each parameter choosing the nal optimal friction factor becomes necessary. added to total error Such a choice could be made by taking a simple average all the way to implementing some type of machine learning algorithm 15: end for that produce continuum...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 3–7, 2021
Paper Number: PSIG-2109
...PSIG 2109 Prediction of Sand Transport in Horizontal and Inclined Flow Based on Machine Learning Algorithms Ronald E. Vieira, Bohan Xu, Siamack A. Shirazi University of Tulsa, USA © Copyright 2021, PSIG, Inc. models are cross-compared and were further validated by comparing its performance...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 3–7, 2021
Paper Number: PSIG-2101
... - Inlet Pressure (normalized) Observed versus Inlet Pressure Predicted, a) Non-linear correlation b) Linear correlation Case 1 Case 2 Case 3 Case 4 production control production logging transition reservoir surveillance midstream oil & gas machine learning artificial intelligence simulation...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 3–7, 2021
Paper Number: PSIG-2102
... data mining machine learning small leak 1 neural network rupture detection data mining algorithm psig 2102 yavuz yilmaz psig 2102 algorithm midstream oil & gas leak 3 small leak 3 artificial intelligence mars pipeline dataset interaction rd segment scenario PSIG 2102 New...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 3–7, 2021
Paper Number: PSIG-2113
... theoretical data produced by a theoratical INTRODUCTION AND BACKGROUND physics model. The results demonstrate that the randomly simulated leakage can be efficiently detected using the trained Data driven Machine Learning technologies such as ANN ANN (Artificial Neural Network) model based on theoretical...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 3–7, 2021
Paper Number: PSIG-2105
... upstream oil & gas artificial intelligence optimization problem machine learning midstream oil & gas direct optimization mathematica andrew yule pipeline simulation interest group decision variable psig 2105 maop pump timing optimization eric smith optimization routine tank...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 3–7, 2021
Paper Number: PSIG-2103
... the importance of the phase change modelling ability of the CPM to avoid false positives and detect the leaks of different types, which would have otherwise been masked under operating conditions that involve phase change. machine learning climate change production control production monitoring...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 14–17, 2019
Paper Number: PSIG-1915
... a possibility of using other simulators in a similar way. constraint steady state psig 1915 evolution strategy objective function evolutionary calculation server experiment downstream oil & gas computer machine learning gas transport artificial intelligence midstream oil & gas...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 14–17, 2019
Paper Number: PSIG-1929
... to improve its operations as a result of the incident and the subsequent analysis. valve pipeline leak detection experience rttm chemical spill reservoir surveillance psig 1929 operation downstream oil & gas application machine learning artificial intelligence midstream oil & gas...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 14–17, 2019
Paper Number: PSIG-1930
... the paper was presented. Write Librarian, Pipeline Simulation Interest Group, 945 McKinney, Suite #106, Houston, TX 77002, USA info@psig.org. ABSTRACT Pipeline data analysis utilizing machine learning method is present in this paper. Three machine learning models using Artificial Neuro Network methods...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 14–17, 2019
Paper Number: PSIG-1933
... diameter, the type of fluid transferred by the pipeline and pressure noise level. flow rate leak location uncertainty midstream oil & gas window size npw system pressure transmitter leak detection system leak location machine learning transient period pipeline leak detection pressure...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 15–18, 2018
Paper Number: PSIG-1806
... ABSTRACT The article deals with reinforcement learning methods adapted to the gas transport management. Reinforcement learning, a part of machine learning, is nowadays one of the most active research areas in artificial intelligence. The goal of reinforcement learning is to learn good policies...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 15–18, 2018
Paper Number: PSIG-1809
... reservoir simulation pipeline flow rate data engineering mario arredondo arce psig 1809 machine learning artificial intelligence david cheng user 3 flow rate history matching performance testing data interpretation midstream oil & gas natural gas transmission pipeline markov...

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