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Keywords: machine learning
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Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 7–10, 2024
Paper Number: PSIG-2426
... natural language machine learning psig 2426 application optimizing pipeline system real time system jennifer worthen psig 2426 greater precision paul dickerson safety algorithm efficiency PSIG 2426 Optimizing Pipeline Systems for Greater Precision, Efficiency & Safety Using Emerging...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 7–10, 2024
Paper Number: PSIG-2413
.... prediction pipeline batch information artificial intelligence representation viscosity psig 2413 calculation pipeline simulation interest group machine learning source correspond determination scenario similarity variation pipeline throughput prediction api gravity springer PSIG 2413...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 7–10, 2024
Paper Number: PSIG-2409
... also be cost-prohibitive. In response to PHMSA's increasingly stringent regulations, this paper delves into an examination of the effectiveness of a leak detection system that exclusively relies on pressure measurements to ensure compliance. This system utilizes an unsupervised machine learning...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 16–19, 2023
Paper Number: PSIG-2306
... & gas artificial intelligence machine learning rttm psig 2206 equation 1 midstream oil & gas pipeline production control slack indicator case 1 trent brown psig 2206 slack line flow norense okungbowa stochastic approach case 2 pipeline simulation interest group PSIG 2306...
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
.... Machine Learning algorithms can optimize the DTS-based PLDS performance during thermal transients. Machine Learning can learn pipeline operations induced thermal behavior and other spatial/temporal temperature correlations. Significant improvements in detection time, minimum detectable T, and false alarm...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 10–13, 2022
Paper Number: PSIG-2213
... on the exploiting of the odorization station dataset through several machine learning models development, to evaluate the odorization process performance. An unsupervised machine learning method based on two different algorithms, the LOF (Local Outlier Factor) algorithm and the K-Means clustering, is developed...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 10–13, 2022
Paper Number: PSIG-2201
... for the formation of gas hydrates in deep-water drilling. During the confinement of the kick, this might generate major well safety and control issues. flow assurance wax inhibition paraffin remediation scale remediation oilfield chemistry machine learning hydrate remediation asphaltene remediation...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 10–13, 2022
Paper Number: PSIG-2204
.... The processes in these applications must be designed 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...
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
... improvement in the sense of leak detection will help pipeliners to mitigate the risk Leaks and ruptures are the most important possible risks for of hazardous effects. The inclusion of new machine learning operational oil and gas pipelines. Due to their hazardous effects algorithms may enable some previously...
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
... a good method for smooth continuous number of decision variables. For a large number of points, it s easier to train a Machine Learning model to understand how the system responds to changes in the decision variables. For a large number of decision variables, the machine learning model processes points...
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 3–7, 2021
Paper Number: PSIG-2118
... supply pressure and flow into the pipe are subject to change as are the pressure and flow demand on the delivery side of the pipe. These conditions, as well as their timing are only predictable to a degree. artificial intelligence compressors engines and turbines machine learning piping...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 14–17, 2019
Paper Number: PSIG-1929
... 1929 operation downstream oil & gas application machine learning artificial intelligence midstream oil & gas neil stockton pressure instrument production control flow metering refining nz peter smit psig 1929 climate change production monitoring flow balance upstream oil...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 14–17, 2019
Paper Number: PSIG-1930
... ABSTRACT Pipeline data analysis utilizing machine learning method is present in this paper. Three machine learning models using Artificial Neuro Network methods are constructed to use nominal flow rate and head loss as input and pipeline roughness change, internal diameter change, a potential...

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