The estimation of the pressure losses inside annulus during pipe rotation is one of the main concerns in various engineering professions. Pipe rotation is a considerable parameter affecting pressure losses in annulus during drilling. In this study, pressure losses of Newtonian and non-Newtonian fluids flowing through concentric horizontal annulus are predicted using computational fluid dynamics (CFD) and support vector regression (SVR). SVR and CFD results are compared with experimental data obtained from literature. The comparisons show that CFD model could predict frictional pressure gradient with an average absolute percent error less than 3.48% for Newtonian fluids and 19.5% for non-Newtonian fluids. SVR could predict frictional pressure gradient with an average absolute percent error less than 5.09% for Newtonian fluids and 5.98% for non-Newtonian fluids.

References

References
1.
Ooms
,
G.
, and
Kamphan-Reinhartz
,
B. E.
,
1996
, “
Influence of Drill Pipe Rotation and Eccentricity on Pressure Drop Over Borehole During Drilling
,”
Eur. J. Mech. B
,
15
(
5
), pp.
695
711
.
2.
Escudier
,
M. P.
,
Gouldson
, I
. W.
,
Oliveira
,
P. J.
, and
Pinho
,
F. T.
,
2000
, “
Effects of İnner Cylinder Rotation on Laminar Flow of a Newtonian Fluid Through an Eccentric Annulus
,”
Int. J. Heat Fluid Flow
,
21
, pp.
92
103
.10.1016/S0142-727X(99)00059-4
3.
Escudier
,
M. P.
,
Gouldson
, I
. W.
,
Oliveira
,
P. J.
, and
Pinho
,
F. T.
,
2002
, “
Fully Developed Laminar Flow of Purely Viscous Non-Newtonian Liquids Through Annuli, İncluding the Effects of Eccentricity and İnner-Cylinder Rotation
,”
Int. J. Heat Fluid Flow
,
23
(1), pp.
52
73
.10.1016/S0142-727X(01)00135-7
4.
Hansen
,
S. A.
, and
Sterri
,
N.
,
1995
, “
Drill Pipe Rotation Effects on Frictional Pressure Losses in Slim Annuli
,”
Presented at the SPE Annular Technical Conference
,
Dallas
,
Oct. 22–25
, Paper SPE No. 30488.
5.
Haciislamoglu
,
M.
, and
Langlinais
,
J.
,
1990
, “
Non-Newtonian Fluid Flow in Concentric Annuli
,”
ASME J. Energy Res. Technol.
,
112
(
3
), pp.
163
169
.10.1115/1.2905753
6.
Erge
,
O.
,
Ozbayoglu
,
M. E.
,
Miska
,
S. Z.
,
Yu
,
M.
,
Takach
,
N.
,
Saasen
,
A.
, and
May
,
R.
,
2014
, “
Effect of Drillstring Deflection and Rotary Speed on Annular Frictional Pressure Losses
,”
ASME J. Energy Res. Technol.
, (in production).10.1115/1.4027565
7.
Ahmed
,
R.
,
Enfis
,
M.
,
Miftah-El-Kheir
,
H.
,
Laget
,
M.
, and
Saasen
,
A.
,
2010
, “
The Effect of Drillstring Rotation on Equivalent Circulation Density: Modeling and Analysis of Field Measurements
,”
Presented at the SPE Annual Technical Conference and Exhibition
, Florence, Italy, Sept. 19–22, Paper SPE No. 135587.
8.
Wei
,
X.
,
Bern
,
P.
,
Kenny
,
P.
,
Miska
,
S. Z.
, and
Takach
,
T. E.
,
1998
, “
The Effect of Drillpipe Rotation on Annular Frictional Pressure Loss
,”
ASME J. Energy Res. Technol.
,
120
(
1
), pp.
61
66
.10.1115/1.2795011
9.
Sorgun
,
M.
,
Ozbayoglu
,
M. E.
, and
Aydin
,
I.
,
2010
, “
Modeling and Experimental Study of Newtonian Fluid Flow in Annulus
,”
ASME J. Energy Res. Technol.
,
132
(
3
), p.
033102
.10.1115/1.4002243
10.
Saasen
,
A.
,
2014
, “
Annular Frictional Pressure Losses During Drilling—Predicting the Effect of Drillstring Rotation
,”
ASME J. Energy Res. Technol.
,
136
(
3
), p.
034501
.10.1115/1.4026205
11.
Kasnakoglu
,
C.
, and
Efe
,
M. O.
,
2008
, “
Prediction of Dynamical Properties of Flow Over a Three-Element Airfoil Via Computational Intelligent Architectures
,”
International Conference on Control, Automation and Systems
, Oct. 14–17, COEX, Seoul, Korea, pp.
381
386
. 10.1109/ICCAS.2008.4694673
12.
Naseri
,
M.
, and
Othman
,
F.
,
2012
, “
Determination of the Length of Hydraulic Jumps Using Artifical Neural Networks
,”
Adv. Eng. Software
,
48
(6), pp.
27
31
.10.1016/j.advengsoft.2012.01.003
13.
Vapnik
,
V.
, and
Chervonenkis
,
A.
,
1964
, “
A Note on One Class of Perceptrons
,”
Autom. Remote Control
,
25
(
1
), pp.
1
15
.
14.
Smola
,
A. J.
, and
Schölkopf
,
B.
,
1998
, “
A Tutorial on Support Vector Regression
,” Royal Holloway College, University of London, NeuroCOLT, UK, Technical Report No. NC-TR-98-030. Available at: http://eprints.pascal-network.org/archive/00000856/01/fulltext.pdf
15.
Muller
,
K. R.
,
Smola
,
A. J.
,
R[0201]tsch
,
G.
,
Schölkopf
,
B.
,
Kohlmorgen
,
J.
, and
Vapnik
,
V.
, “
Predicting Time Series With Support Vector Machines
,”
Artificial Neural Networks
ICANN’97 (Springer Lecture Notes in Computer Science), Vol.
1327
, pp.
999
1004
.10.1007/BFb0020283
16.
Drucker
,
H.
,
Burges
,
C. J. C.
,
Kaufman
,
L.
,
Smola
,
A.
, and
Vapnik
,
V.
,
1997
, “
Support Vector Regression Machines
,”
Advances in Neural Information Processing Systems
,
M. C.
Mozer
,
M. I.
Jordon
, and
T.
Petsche
, eds.,
MIT
,
Cambridge, MA
, Vol. 9, pp.
155
161
.
17.
Stitson
,
M.
,
Gammerman
,
A.
,
Vapnik
,
V.
,
Vovk
,
V.
,
Watkins
,
C.
, and
Weston
,
J.
,
1999
, “
Support Vector Regression With ANOVA Decomposition Kernels
,”
Advances in Kernel Methods—Support Vector Learning
,
B.
Schölkopf
,
C. J. C.
Burges
, and
A. J.
Smola
, eds.,
MIT
,
Cambridge, MA
, pp.
285
292
.
18.
Mattera
,
D.
, and
Haykin
,
S.
,
1999
, “
Support Vector Machines for Dynamic Reconstruction of a Chaotic System
,”
Advances in Kernel Methods—Support Vector Learning
,
B.
Schölkopf
,
C. J. C.
Burges
, and
A. J.
Smola
, eds.,
MIT
,
Cambridge, MA
, pp.
211
242
.
19.
Vapnik
,
V.
,
1995
,
The Nature of Statistical Learning Theory
,
Springer
,
New York
.
20.
Jade
,
A. M.
,
Jayaramaa
,V
. K.
,
Kulkarni
,
B. D.
,
Khopkar
,
A. R.
,
Ranade
,
V. V.
, and
Sharma
,
A.
,
2006
, “
A Novel Local Singularity Distribution Based Method for Flow Regime Identification: Gas–Liquid Stirred Vessel With Rushton Turbine
,”
Chem. Eng. Sci.
,
61
(2), pp.
688
697
.10.1016/j.ces.2005.08.002
21.
Hua
,
C.
,
Wang
,
C.
,
Geng
,
Y.
, and
Shi
,
T.
,
2010
, “
Noninvasive Flow Regime Identification for Wet Gas Flow Based on Flow-Induced Vibration
,”
Chin. J. Chem. Eng.
,
18
(
5
), pp.
795
803
.10.1016/S1004-9541(09)60131-2
22.
Zhang
,
L.
, and
Wang
,
H.
,
2010
, “
Identification of Oil–Gas Two-Phase Flow Pattern Based on SVM and Electrical Capacitance Tomography Technique
,”
Flow Measur. Instrum.
,
21
(
1
), pp.
20
24
.10.1016/j.flowmeasinst.2009.08.006
23.
Zhang
,
C.
,
Zhang
,
T.
, and
Yuan
,
C.
,
2010
, “
Oil Holdup Prediction of Oil–Water Two Phase Flow using Thermal Method Based on Multiwavelet Transform and Least Squares Support Vector Machine
,”
Expert Syst. Appl.
,
38
(
3
), pp.
1602
1610
.10.1016/j.eswa.2010.07.081
24.
El-Sebakhy
,
E. A.
,
2009
, “
Forecasting PVT Properties of Crude Oil Systems Based on Support Vector Machines Modeling Scheme
,”
J. Pet. Sci. Eng.
,
64
(
1–4
), pp.
25
34
.10.1016/j.petrol.2008.12.006
25.
Al-Marhoun
,
M. A.
,
Nizamuddin
,
S.
,
Abdul Raheem
,
A. A.
,
Ali
,
S. S.
, and
Muhammadain
,
A. A.
,
2012
, “
Prediction of Crude Oil Viscosity Curve Using Artificial Intelligence Techniques
,”
J. Pet. Sci. Eng.
,
86–87
(5), pp.
111
117
.10.1016/j.petrol.2012.03.029
26.
ANSYS Version 12.1, 2009, ANSYS Workbench CFX, ANSYS Inc.
27.
Sorgun
,
M.
,
Ozbayoglu
,
M. A.
, and
Ozbayoglu
,
M. E.
,
2012
, “
Estimation of Frictional Pressure Losses in Annulus With Pipe Rotation Using Support Vector Machines and Computational Fluid Dynamics
,”
10th International Congress on Advances in Civil Engineering
, Middle East Technical University, Ankara, Turkey, Oct. 17–19.
28.
Ahmed
,
R.
, and
Miska
,
S.
,
2008
, “
Experimental Study and Modeling of Yield Power-Law Fluid Flow in Annuli With Drillpipe Rotation
,”
IADC/SPE Drilling Conference in Orlando
, Mar. 4–6, SPE Paper No. 112604.
29.
ANSYS CFX-Solver Theory Guide, 2006, ANSYS Inc., Southpointe 275 Technology Drive Canonsburg, PA 15317.
30.
Cortes
,
C.
, and
Vapnik
,
V.
,
1995
, “
Support Vector Networks
,”
Mach. Learn.
,
20
(
3
), pp.
273
297
.
31.
Theodoridis
,
S.
, and
Koutroumbas
,
K.
,
2009
,
Pattern Recognition
,
4th ed.
,
Academic Press
, Burlington, MA.
You do not currently have access to this content.