نشریه علمی فرآیند نو

نشریه علمی فرآیند نو

پیش بینی فشار نقطه شبنم میعانات گازی با استفاده از روش های یادگیری ماشین

نوع مقاله : کاربردی

نویسندگان
1 دانشجو، دانشگاه صنعتی امیر کبیر واحد ماشهر، ماهشهر، ایران
2 استاد، دانشگاه صنعتی امیر کبیر واحد ماشهر، ماهشهر، ایران
3 شرکت ملی مناطق نفتخیز جنوب، اهواز، ایران
10.22034/farayandno.2026.2070757.2015
چکیده
پیش‌بینی دقیق و به‌موقع فشار نقطه شبنم در مخازن گاز میعانی از اهمیت فنی و اقتصادی بالایی برخوردار است و به‌ویژه در تعیین ویژگی‌های سیال، محاسبات عملکرد مخزن، برنامه‌ریزی توسعه مخازن و طراحی بهینه سیستم‌های تولید نقش کلیدی ایفا می‌کند. از هشت مدل متفاوت، شامل رگرسیون خطی، رگرسیون لاسو، رگرسیون ریج، الاستیک نت، شبکه‌های عصبی مصنوعی، درخت تصمیم‌گیری، ترکیب نزدیک‌ترین همسایگی و جنگل تصادفی، برای پیش‌بینی فشار نقطه شبنم استفاده گردید. نتایج نشان می‌دهد که مدل جنگل تصادفی با میانگین درصد خطای نسبی %‌3/3 و ضریب همبستگی 0/84 بهترین عملکرد را در مقایسه با سایر مدل‌ها داشته و به‌طور موفقیت‌آمیزی از تمامی مدل‌های دیگر پیشی گرفته است. این مطالعه با استفاده از روش‌های هوش مصنوعی، نوآوری در پیش‌بینی فشار نقطه شبنم را نشان می‌دهد و تأکید می‌کند که تحلیل دقیق داده‌های میعانات گازی و مدل‌های پیشرفته، دقت و کارایی بالاتری در پیش‌بینی‌های صنعتی فراهم می‌آورد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Prediction of Dew Point Pressure of Gas Condensates Using Machine Learning Methods

نویسندگان English

Hamid Sangbor 1
Mohammad Molazadeh Ahmadi 2
Alireza Baghban 3
1 Student, Amirkabir University of Technology, Mahshahr campus, Mahshahr, Iran
2 Professor, Amirkabir University of Technology, Mahshahr campus, Mahshahr, Iran
3 National Iranian Soth Oilfeilds Company (NISOC), Ahvaz, Iran
چکیده English

Accurate prediction of the dewpoint pressure in gas condensate reservoirs holds significant technical and economic importance, particularly in determining fluid characteristics, reservoir performance calculations, planning the development of gas condensate reservoirs, and the optimal design of production systems. In this context, artificial intelligence-based methods have emerged as innovative and effective tools in various engineering fields. Eight different models, including linear regression, Lasso regression, Ridge regression, Elastic Net, artificial neural networks, decision trees, k-nearest neighbors, and random forest, were utilized for predicting the dew point pressure. The results indicate that the random forest model outperformed the other models, achieving an average relative error of 3.3% and a correlation coefficient of 0.84. This study represents an innovation by applying artificial intelligence methods to predict dew point pressure, demonstrating that with precise analysis of gas condensate data and the use of advanced models, higher accuracy and efficiency in industrial predictions can be achieved.

کلیدواژه‌ها English

Gas Condensates
Data Mining
Dew Point Pressure
Artificial Intelligence Models
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