{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-machine-learning-approach-for-virtual-flow","title":"A Machine Learning Approach for Virtual Flow Metering and Forecasting","arxiv_id":"1802.05698","date":"2018-02-15","proceeding":null,"authors":["Nikolai Andrianov"],"abstract":"We are concerned with robust and accurate forecasting of multiphase flow\nrates in wells and pipelines during oil and gas production. In practice, the\npossibility to physically measure the rates is often limited; besides, it is\ndesirable to estimate future values of multiphase rates based on the previous\nbehavior of the system. In this work, we demonstrate that a Long Short-Term\nMemory (LSTM) recurrent artificial network is able not only to accurately\nestimate the multiphase rates at current time (i.e., act as a virtual flow\nmeter), but also to forecast the rates for a sequence of future time instants.\nFor a synthetic severe slugging case, LSTM forecasts compare favorably with the\nresults of hydrodynamical modeling. LSTM results for a realistic noizy dataset\nof a variable rate well test show that the model can also successfully forecast\nmultiphase rates for a system with changing flow patterns.","url_abs":"http://arxiv.org/abs/1802.05698v1","url_pdf":"http://arxiv.org/pdf/1802.05698v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-machine-learning-approach-for-virtual-flow","repo_url":"https://github.com/nikolai-andrianov/VFM","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}