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دسته بندی:
یادگیری ماشین - machine learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
An improved model for gas-liquid flow pattern prediction based on machine learning
ترجمه فارسی عنوان مقاله:
یک مدل بهبود یافته برای پیش بینی الگوی جریان گاز مایع بر اساس یادگیری ماشین
منبع:
Sciencedirect - Elsevier - Journal of Petroleum Science and Engineering, 183 (2019) 106370: doi:10:1016/j:petrol:2019:106370
نویسنده:
Gene Maska, Xingru Wua,⁎, Kegang Lingb
چکیده انگلیسی:
The determination of flow patterns is a fundamental problem in two-phase flow analysis, and an accurate model
for gas-liquid flow pattern prediction is critical for any multiphase flow characterization as the model is used in
many applications in petroleum engineering. We developed a new model based on machine learning techniques
via dimensionally analyzing more than 8000 laboratory multi-phase flow tests. As shown in the test results, the
flow pattern is affected by fluid properties, in-situ flow rates of liquid and gas, flow conduit geometry and
mechanical properties. Applying hydraulic fundamentals and dimensional analysis, three upscaling numbers are
developed to reduce the number of freedom dimensions. These dimensionless variables are easy to use for
upscaling and have physical meanings. Machine learning techniques on the dimensionless variables significantly
improved their predictive accuracy. Until now the best matching on these laboratory data was approximately
80% using the most recently developed semi-analytical models. The quality of the matching is improved to 90%
or greater on the experimental data using machine learning techniques.
Keywords: Machine learning | Data analytics | Two-phase flow model | Predictive analytics | Flow pattern | Gas-liquid modeling
قیمت: رایگان
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