TY - JOUR
T1 - PREDICTION OF HORIZONTAL OIL-WATER FLOW PRESSURE GRADIENT USING ARTIFICIAL INTELLIGENCE TECHNIQUES
AU - Al-Wahaibi, Talal
AU - Mjalli, Farouq S.
PY - 2014/2
Y1 - 2014/2
N2 - 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.
AB - 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.
KW - ANN
KW - Homogeneous model
KW - Oil-water flow
KW - Pressure gradient
KW - Two-fluid model
UR - http://www.scopus.com/inward/record.url?scp=84887905881&partnerID=8YFLogxK
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U2 - 10.1080/00986445.2013.766603
DO - 10.1080/00986445.2013.766603
M3 - Article
AN - SCOPUS:84887905881
SN - 0098-6445
VL - 201
SP - 209
EP - 224
JO - Chemical Engineering Communications
JF - Chemical Engineering Communications
IS - 2
ER -