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Keywords: machine learning
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Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 3–7, 2021
Paper Number: PSIG-2101
... an actual operating NPS 16 multi-station blended crude pipeline system was available to assist in this parametric study. production control production logging transition reservoir surveillance midstream oil & gas machine learning artificial intelligence simulation interest group...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 3–7, 2021
Paper Number: PSIG-2102
... and rupture detection computation data science technique for pipeline operators. 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...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 3–7, 2021
Paper Number: PSIG-2109
... that are a part of such models. Artificial Intelligence (AI) and Machine learning (ML) offer a wealth of techniques to extract information from data that can be translated into knowledge about the underlying fluid mechanics. The purpose of this work is to investigate the use of several ML models to predict...
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 3–7, 2021
Paper Number: PSIG-2105
.... Just as important, the results keep the network within operating boundaries. To scale the solution for large networks, interpolation and machine learning algorithms are employed to keep the problem from exponentially increasing in computation time. Introduction and Background Onshore oil and gas...
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
..., 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 for sequential decision problems by optimizing a cumulative future reward signal. Notwithstanding of successes of reinforcement learning...
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
... member of ASME (American Society of Mechanical Engineers), CMES (Chinese Mechanical Engineering Society) and ORSC (Operations Research Society of China). product pipeline batch optimization problem algorithm pipe segment batch plan psig 1605 flow rate market demand machine learning...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 10–13, 2016
Paper Number: PSIG-1616
... midstream oil & gas maguda psig 1616 psig 1616 usage engineer model 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...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 10–13, 2016
Paper Number: PSIG-1628
... be a challenge. evm internal surface roughness subset pipe segment equation upstream oil & gas surface condition probability distribution markov chain monte carlo history matching artificial intelligence roughness machine learning reservoir simulation internal wall roughness effective...
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 12–15, 2015
Paper Number: PSIG-1502
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 12–15, 2015
Paper Number: PSIG-1504
Proceedings Papers

Paper presented at the PSIG Annual Meeting, May 12–15, 2015
Paper Number: PSIG-1506
.... ACM, San Diego, CA, USA. 17. Ross Quinlan, J. (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA. 18. Mitra,S. (2002) Fuzzy decision tree, linguistic rules and fuzzy knowledge-based network: generation and evaluation. IEEE Systems, Man, and Cybernetics...

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