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Keywords: machine learning
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Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 10–13, 2022
Paper Number: PSIG-2201
... create ideal thermodynamic conditions 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...
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 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
... data mining upstream oil & gas operating condition artificial intelligence mg sm 3 florence engineering sm 3 offline monitoring method carlo carcasci psig 2213 outlier algorithm machine learning midstream oil & gas flow rate operation italy point value university psig...
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-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-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-2113
... on the efficiency and reliability of the training and results of the ANN model. The paper also discusses the influence of the range of input data on the predictability of the ANN model in leak detection. Introduction and Background Data driven Machine Learning technologies such as ANN (Artificial Neural...
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 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
... 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...
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
... between the measurement data and hydraulics model prediction because of the consideration of more physical aspects. reservoir simulation pipeline flow rate data engineering mario arredondo arce psig 1809 machine learning artificial intelligence david cheng user 3 flow rate history...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 9–12, 2017
Paper Number: PSIG-1711
... assumptions are yet to be confirmed. In addition, many projects may never progress beyond the prospecting stage despite significant design and analysis. leak detection model node size coefficient machine learning chemical spill reference model significance professional engineer decay...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 10–13, 2016
Paper Number: PSIG-1605
... continued, Diego C. Cafaro and Jaime Cerdá (2010) proposed an improved model, which allowed simultaneous batch injection at different sources. product pipeline batch optimization problem algorithm pipe segment batch plan psig 1605 flow rate market demand machine learning university...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 10–13, 2016
Paper Number: PSIG-1616
... profile usage report machine learning artificial intelligence weather station customer hourly usage multi-parameter residential hourly profile model hourly model daily temperature gas usage service territory profile model tmin smartmeter ` 1 SmartMeter is a trademark of SmartSynch, Inc...

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