PREDICTION OF HORIZONTAL OIL-WATER FLOW PRESSURE GRADIENT USING ARTIFICIAL INTELLIGENCE TECHNIQUES

Talal Al-Wahaibi*, Farouq S. Mjalli

*المؤلف المقابل لهذا العمل

نتاج البحث: المساهمة في مجلةArticleمراجعة النظراء

30 اقتباسات (Scopus)

ملخص

In oil-water flow, the two-fluid and the homogeneous models are commonly used to predict the pressure gradient. However, these models fail in many cases to predict the pressure gradient, especially in dual continuous flow. In this work, an artificial neural network (ANN) model with five inputs-oil and water superficial velocities, pipe diameter, pipe roughness, and oil viscosity-was developed to predict the pressure gradient of horizontal oil-water flow based on a databank of around 765 measurements collected from the open literature. Statistical analysis showed that the ANN model has an average error of 0.30%, average absolute error of 2.9%, and standard deviation of 7.6%. A comparison with the two-fluid model, the homogeneous model, and the Al-Wahaibi (2012) correlation showed that the ANN model better predicts the pressure gradient data over a wide range of superficial oil (U so = 0.05-3.0 m/s) and water velocities (U sw = 0.05-2.7 m/s), oil viscosity values (1-35 cp), pipe diameters (14-82.8 mm), and different pipe materials.

اللغة الأصليةEnglish
الصفحات (من إلى)209-224
عدد الصفحات16
دوريةChemical Engineering Communications
مستوى الصوت201
رقم الإصدار2
المعرِّفات الرقمية للأشياء
حالة النشرPublished - فبراير 2014

ASJC Scopus subject areas

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