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<p>The proposed data-driven multiphase virtual flow meter (DD-MVFM) blends data-driven ensemble machine learning with historical portable test reports. It provides precise real-time estimates of oil, gas, and water flow rates, along with production predictions. A key novelty is its efficient use of existing hardware for wellhead measurements such as temperature and pressure. The DD-MVFM can serve in three distinct ways: as a verification tool for multiphase physical flow meters (MPFMs), ensuring their accuracy; as a redundant system when MPFMs are unavailable or undergoing maintenance; and as a standalone replacement for MPFMs, significantly reducing operating costs and eliminating the need for extensive infrastructure setup. This innovation contributes to the crucial objective of reducing CO<sub>2</sub> emissions. The development of the DD-MVFM involves the fusion of data wrangling and machine learning algorithms for optimal performance. Initial testing indicates an 85% success rate, with potential for further improvement as more field test data are incorporated, making it a pioneering solution in the field of oil and gas management.</p>

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Virtual multiphase flow meter for high gas/oil ratios and water-cut reservoirs via ensemble machine learning

  • 期刊论文

  • 发表日期：

    2025年3月6日

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  • Wael A. Farag

摘要

The proposed data-driven multiphase virtual flow meter (DD-MVFM) blends data-driven ensemble machine learning with historical portable test reports. It provides precise real-time estimates of oil, gas, and water flow rates, along with production predictions. A key novelty is its efficient use of existing hardware for wellhead measurements such as temperature and pressure. The DD-MVFM can serve in three distinct ways: as a verification tool for multiphase physical flow meters (MPFMs), ensuring their accuracy; as a redundant system when MPFMs are unavailable or undergoing maintenance; and as a standalone replacement for MPFMs, significantly reducing operating costs and eliminating the need for extensive infrastructure setup. This innovation contributes to the crucial objective of reducing CO2 emissions. The development of the DD-MVFM involves the fusion of data wrangling and machine learning algorithms for optimal performance. Initial testing indicates an 85% success rate, with potential for further improvement as more field test data are incorporated, making it a pioneering solution in the field of oil and gas management.