For gas condensate reservoirs, as the reservoir pressure drops below the dew point pressure (DPP), a large amount of valuable condensate drops out and remains in the reservoir. Thus, prediction of accurate values for DPP is important and leads to successful development of gas condensate reservoirs. There are some experimental methods such as constant composition expansion (CCE) and constant volume depletion (CVD) for DPP measurement but difficulties in experimental measurement especially for lean retrograde gas condensate causes to develop of different empirical correlations and equations of state for DPP calculation. Equations of state and empirical correlations are developed for special and limited data sets and for unseen data sets they are not generalizable. To mitigate this problem, in this paper we developed new artificial neural network optimized by particle swarm optimization (ANN-PSO) for DPP prediction. Reservoir fluid composition, temperature and characteristics of the C7+ considered as input parameters to neural network and DPP as target parameter. Comparing results of the developed model in this research with Gaussian processes regression by particle swarm optimization (GPR-PSO), previous models and correlations shows that the predictive model is accurate and is generalizable to new unseen data sets.

References

1.
Osman
,
E. A.
,
Abdel-Wahhab
,
O. A.
, and
Al-Marhoun
,
M. A.
,
2001
, “
Prediction of Oil PVT Properties Using Neural Networks
,”
SPE
Paper No. 68233.
2.
Elsharkawy
,
A. M.
,
2001
, “
Predicting the Dew Point Pressure for Gas Condensate Reservoirs: Empirical Models and Equations of State
,”
Fluid Phase Equilib.
,
193
(
1–2
), pp.
147
165
.
3.
Danesh
,
A.
,
1998
,
PVT and Phase Behavior of Petroleum Reservoir Fluids
,
Elsevier
,
Amsterdam, The Netherlands
.
4.
Pedersen
,
K. S.
,
Thomassen
,
Q.
, and
Fredenslund
,
A.
,
1988
, “
Characterization of Gas Condensate Mixtures
,”
AIChE Spring National Meeting
,
New Orleans, LA
, Mar. 6–10.
5.
Eilerts
,
K.
, and
Smith
,
R. V.
,
1942
, “
Specific Volumes and Phase-Boundary Properties of Separator-Gas and Liquid-Hydrocarbon Mixtures
,” Report of Investigations, Vol. 3642, U.S. Bureau of Mines, Washington, DC.
6.
Olds
,
R. H.
,
Sage
,
B. H.
, and
Lacey
,
W. N.
,
1945
, “
Volumetric and Phase Behavior of Oil and Gas from Paloma Field
,”
Trans. AIME
,
160
(
1
), pp.
77
99
.
7.
Organick
,
E. I.
, and
Golding
,
B. H.
,
1952
, “
Prediction of Saturation Pressures for Condensate-Gas and Volatile-Oil Mixtures
,”
Trans. AIME
,
4
(
5
), pp.
135
148
.
8.
Nemeth
,
L.
, and
Kennedy
,
H.
,
1967
, “
A Correlation of Dew Point Pressure With Fluid Composition and Temperature
,”
SPE J.
,
7
(
2
), p.
11477
.
9.
Potsch
,
K.
, and
Braeuer
,
L.
,
1996
, “
A Novel Graphical Method for Determining Dew Point Pressure of Gas Condensates
,”
Society of Petroleum Engineers, European Petroleum Conference
(
SPE
),
Milan, Italy
, Oct. 22–24, Paper No. SPE-36919-MS.
10.
Yisheng
,
F.
,
Baozhu
,
L.
, and
Yongle
,
H.
,
1998
, “
Condensate Gas Phase Behavior and Development
,”
SPE
Paper No. 50925.
11.
Rostami
,
H.
, and
Manshad
,
A. K.
,
2014
, “
A New Support Vector Machine and Artificial Neural Networks for Prediction of Stuck Pipe in Drilling of Oil Fields
,”
ASME J. Energy Resour. Technol.
,
136
(
2
), p.
024502
.
12.
Derakhshan
,
S.
, and
Kasaeian
,
N.
,
2014
, “
Optimization, Numerical, and Experimental Study of a Propeller Pump as Turbine
,”
ASME J. Energy Resour. Technol.
,
136
(
1
), p.
012005
.
13.
Palchak
,
D.
,
Suryanarayanan
,
S.
, and
Zimmerle
,
D.
,
2013
, “
An Artificial Neural Network in Short-Term Electrical Load Forecasting of a University Campus: A Case Study
,”
ASME J. Energy Resour. Technol.
,
135
(
3
), p.
032001
.
14.
Ilamathi
,
P.
,
Selladurai
,
V.
, and
Balamurugan
,
K.
,
2013
, “
Modeling and Optimization of Unburned Carbon in Coal-Fired Boiler Using Artificial Neural Network and Genetic Algorithm
,”
ASME J. Energy Resour. Technol.
,
135
(
3
), p.
032201
.
15.
González
,
A.
,
Barrufet
,
M. A.
, and
Startzman
,
R.
,
2003
, “
Improved Neural-Network Model Predicts Dewpoint Pressure of Retrograde Gases
,”
J. Pet. Sci. Eng.
,
37
(
3
), pp.
183
194
.
16.
Nowroozi
,
S.
,
Ranjbar
,
M.
,
Hashemipour
,
H.
, and
Schaffie
,
M.
,
2009
, “
Development of a Neural Fuzzy System for Advanced Prediction of Dew Point Pressure in Gas Condensate Reservoirs
,”
Fuel Process. Technol.
,
90
(
3
), pp.
452
457
.
17.
Manshad
,
A. K.
, and
Rostami
,
H.
,
2014
, “
Application of Evolutionary Gaussian Processes Regression by Particle Swarm Optimization for Prediction of Dew Point Pressure in Gas Condensate Reservoirs
,”
Neural Comput. Appl.
,
24
(
3–4
), pp.
705
713
.
18.
Qu
,
X.
,
Feng
,
J.
, and
Sun
,
W.
,
2008
, “
Parallel Genetic Algorithm Model Based on AHP and Neural Networks for Enterprise Comprehensive Business
,”
International Conference on Intelligent Information Hiding and Multimedia Signal Processing
(
IIHMSP'08
), Harbin, China, Aug. 15–17, pp.
897
900
.
19.
Tang
,
P.
, and
Xi
,
Z.
,
2008
, “
The Research on BP Neural Network Model Based on Guaranteed Convergence Particle Swarm Optimization
,”
Second International Symposium on Intelligent Information Technology Application
(
IITA'08
), Shanghai, Dec. 20–22, pp.
13
16
.
20.
Rajasekaran
,
S.
, and
Pai
,
G. A. V.
,
2004
,
Neural Networks, Fuzzy Logic, and Genetic Algorithms Synthesis and Applications
,
Prentice-Hall
,
New Delhi, India
.
21.
Gharbi
,
R.
,
1997
, “
Estimating the Isothermal Compressibility Coefficient of Under Saturated Middle East Crudes Using Neural Networks
,”
Energy Fuels
,
11
(
2
), pp.
372
378
.
22.
Hornik
,
K.
,
Stinchcombe
,
M.
, and
White
,
H.
,
1989
, “
Multilayer Feedforward Networks are Universal Approximators
,”
Neural Networks
,
2
(
5
), pp.
359
366
.
23.
Brown
,
M.
, and
Harris
,
C. J.
,
1994
,
Neurofuzzy Adaptive Modelling and Control
,
Prentice Hall
,
Hemel Hepstead, UK
.
24.
Sayyad
,
H.
,
Manshad
,
A. K.
, and
Rostami
,
H.
,
2014
, “
Application of Hybrid Neural Particle Swarm Optimization Algorithm for Prediction of MMP
,”
Fuel
,
116
, pp.
625
633
.
25.
Eberhart
,
R.
, and
Kennedy
,
J.
,
1995
, “
A New Optimizer Using Particle Swarm Theory
,”
Sixth International Symposium on Micro Machine and Human Science
(
MHS'95
), Nagoya, Japan, Oct. 4–6, pp.
39
43
.
26.
Bertsimas
,
D.
, and
Nohadani
,
O.
,
2010
, “
Robust Optimization With Simulated Annealing
,”
J. Global Optim.
,
48
(
2
), pp.
323
334
.
27.
Goldberg
,
D. E.
,
1989
,
Genetic Algorithms in Search, Optimization, and Machine Learning
,
Addison-Wesley Longman
,
Boston, MA
.
28.
Kennedy
,
J.
, and
Eberhart
,
R.
,
2002
, “
Particle Swarm Optimization
,”
IEEE International Conference on Neural Networks
, Vol.
4
, pp.
1942
1948
.
29.
Liu
,
D.
, and
Hou
,
Z.-G.
,
2007
,
Advances in Neural Networks—ISNN 2007
,
4th International Symposium on Neural Networks
, Nanjing, China, June 3–7,
Springer-Verlag
,
New York
, pp.
1177
1186
.
30.
Eberhart
,
R.
,
Simpson
,
P.
, and
Dobbins
,
R.
,
1996
,
Computational Intelligence PC Tools
,
Academic Press
,
San Diego, CA
.
31.
Guo
,
T.-M.
, and
Du
,
L.
,
1989
, “
A Three-Parameter Cubic Equation of State for Reservoir Fluids
,”
Fluid Phase Equilib.
,
52
, pp.
47
57
.
32.
Wang
,
P.
,
1989
, “
Prediction of Phase Behavior for Gas Condensate
,”
SPE
Paper No. 19503.
You do not currently have access to this content.