Oil and gas companies are expanding their operations in the remote Arctic offshore with harsh weather conditions such as the Barents Sea. One of the major challenges in reliability assessment of production plants operating in such areas is lack of life data accounting for the adverse effects of harsh operating conditions. The aim of this study is to develop an expert-based model to assess the reliability of oil and gas exploration and production plants operating in Arctic regions. Expert opinions are used to modify the life data available in normal-climate locations, which are considered as the base area, to account for the effects of operating conditions. The proposed model is illustrated by assessing the reliability of an oil processing train in the Western Barents Sea. Additionally, based on a criticality analysis, some design modifications are suggested to improve the reliability of the processing train.

References

References
1.
ISO
,
2010
, “
Petroleum and Natural Gas Industries—Arctic Offshore Structures
,” ISO, Geneva, Standard No. ISO 19906.
2.
Stachowiak
,
G. W.
, and
Batchelor
,
A. W.
,
2014
,
Engineering Tribology
,
Butterworth-Heinemann
,
Waltham, MA
, Chap. 2.
3.
Dutta
,
P. K.
,
1988
, “
Behaviour of Materials at Cold Regions Temperatures—Part 1: Program Rationale and Test Plan
,” U.S. Army Engineer Research and Development Centre, Hanover, NH,
Special Report 88-9
.
4.
Barabadi
,
A.
,
Barabady
,
J.
, and
Markeset
,
T.
,
2011
, “
A Methodology for Throughput Capacity Analysis of a Production Facility Considering Environment Condition
,”
Reliab. Eng. System Safety
,
96
(
12
), pp.
1637
1646
.
5.
Barabadi
,
A.
,
Barabady
,
J.
, and
Markeset
,
T.
,
2014
, “
Application of Reliability Models With Covariates in Spare Part Prediction and Optimization—A Case Study
,”
Reliab. Eng. System Safety
,
123
(
3
), pp.
1
7
.
6.
Barabadi
,
A.
,
Gudmestad
,
O. T.
, and
Barabady
,
J.
,
2015
, “
RAMS Data Collection Under Arctic Conditions
,”
Reliab. Eng. System Safety
,
135
(
3
), pp.
92
99
.
7.
Gudmestad
,
O. T.
, and
Karunakaran
,
D.
,
2012
, “
Challenges Faced by the Marine Contractors Working in Western and Southern Barents Sea
,”
Offshore Technology Conference
, Houston, TX, Dec. 3–5, Paper No. OTC-23842-MS.
8.
Naseri
,
M.
, and
Barabady
,
J.
,
2015
, “
Performance of Skimmers in the Arctic Offshore Oil Spills
,”
Safety and Reliability: Methodology and Applications
,
T.
Nowakowski
,
M.
Młyńczak
,
A.
Jodejko-Pietruczuk
:
S.
Werbińska-Wojciechowska
, eds.,
CRC Press
,
London
, pp.
607
614
.
9.
Artiba
,
A.
,
Riane
,
F.
,
Ghodrati
,
B.
, and
Kumar
,
U.
,
2005
, “
Reliability and Operating Environment-Based Spare Parts Estimation Approach: A Case Study in Kiruna Mine, Sweden
,”
J. Qual. Maint. Eng.
,
11
(
2
), pp.
169
184
.
10.
Ansell
,
J. I.
, and
Philipps
,
M. J.
,
1987
, “
Practical Aspects of Modelling of Repairable Systems Data Using Proportional Hazards Models
,”
Reliab. Eng. System Safety
,
58
(
11
), pp.
165
171
.
11.
Martorell
,
S.
,
Sanchez
,
A.
, and
Serradell
,
V.
,
1999
, “
Age-Dependent Reliability Model Considering Effects of Maintenance and Working Conditions
,”
Reliab. Eng. System Safety
,
64
(
4
), pp.
19
31
.
12.
Gao
,
X.
,
Barabady
,
J.
, and
Markeset
,
T.
,
2010
, “
An Approach for Prediction of Petroleum Production Facility Performance Considering Arctic Influence Factors
,”
Reliab. Eng. System Safety
,
95
(
8
), pp.
837
846
.
13.
Barabadi
,
A.
,
2014
, “
Reliability Analysis of Offshore Production Facilities Under Arctic Conditions Using Reliability Data From Other Areas
,”
ASME J. Offshore Mech. Arct. Eng.
,
136
(
2
), p.
021601
.
14.
Cooke
,
R. M.
,
1991
,
Experts in Uncertainty: Opinion and Subjective Probability in Science
,
Oxford University Press
,
New York
.
15.
Genest
,
C.
, and
Zidek
,
J. V.
,
1986
, “
Combining Probability Distributions: A Critique and an Annotated Bibliography
,”
Stat. Sci.
,
1
(
1
), pp.
114
135
.
16.
French
,
S.
,
1985
, “
Group Consensus Probability Distributions: A Critical Survey
,”
Bayesian Statistics 2—Proceedings of the Second Valencia International Meeting
,
J. M.
Bernardo
,
M. H. D.
Groot
,
D. V.
Lindley
, and
A. F. M.
Smith
, eds.,
Elsevier
,
Amsterdam
, pp.
183
201
.
17.
Clemen
,
R. T.
, and
Winkler
,
R. L.
,
1999
, “
Combining Probability Distributions From Experts in Risk Analysis
,”
Risk Anal.
,
19
(
2
), pp.
187
203
.
18.
Rufo
,
M. J.
,
Pérez
,
C. J.
, and
Martín
,
J.
,
2012
, “
A Bayesian Approach to Aggregate Experts' Initial Information
,”
Electron. J. Stat.
,
6
(
0
), pp.
2362
2382
.
19.
Wisse
,
B.
,
Bedford
,
T.
, and
Quigley
,
J.
,
2008
, “
Expert Judgement Combination Using Moment Methods
,”
Reliab. Eng. System Safety
,
93
(
5
), pp.
675
686
.
20.
Meyer
,
M. A.
, and
Booker
,
J. M.
,
1991
,
Eliciting and Analyzing Expert Judgement—A Practical Guide
,
Academic Press
,
London, UK
.
21.
Ortiz
,
N. R.
,
Wheeler
,
T. A.
,
Breeding
,
R. J.
,
Hora
,
S.
,
Meyer
,
M. A.
, and
Keeney
,
R. L.
,
1991
, “
Use of Expert Judgment in NUREG-1150
,”
Nucl. Eng. Des.
,
126
(
3
), pp.
313
331
.
22.
Mannan
,
S.
,
2012
,
Lees' Loss Prevention in the Process Industries
,
Butterworth-Heinemann
,
Oxford, UK
, Chap. 9.
23.
Mosleh
,
A.
,
Bier
,
V. M.
, and
Apostolakis
,
G.
,
1987
, “
Methods for the Elicitation and Use of Expert Opinion in Risk Assessment: Phase 1—A Critical Evaluation and Directions for Future Research
,” U.S. Nuclear Regulatory Commission, Washington, DC,
Report No. NUREG/CR-4962
.
24.
Bedford
,
T.
, and
Cooke
,
R. M.
,
2001
,
Probabilistic Risk Analysis: Foundations and Methods
,
Cambridge University Press
,
Cambridge, UK
.
25.
Kuselman
,
I.
,
Pennecchi
,
F.
,
Epstein
,
M.
,
Fajgelj
,
A.
, and
Ellison
,
S. L. R.
,
2014
, “
Monte Carlo Simulation of Expert Judgments on Human Errors in Chemical Analysis—A Case Study of ICP–MS
,”
Talanta
,
130
(
1
), pp.
462
469
.
26.
Benson
,
P. G.
, and
Nichols
,
M. L.
,
1982
, “
An Investigation of Motivational Bias in Subjective Predictive Probability Distributions
,”
Decis. Sci.
,
13
(
2
), pp.
225
239
.
27.
Cooke
,
R. M.
, and
Goossens
,
L. H. J.
,
2000
Procedures Guide for Structural Expert Judgement in Accident Consequence Modelling
,”
Radiat. Prot. Dosim.
,
90
(
3
), pp.
303
309
.
28.
Cooke
,
R. M.
,
Mendel
,
M.
, and
Thijs
,
W.
,
1988
, “
Calibration and Information in Expert Resolution: A Classical Approach
,”
Automatica
,
24
(
1
), pp.
87
93
.
29.
Murthy
,
D. N. P.
,
Xie
,
M.
, and
Jiang
,
R.
,
2004
,
Weibull Models
,
Wiley
,
Hoboken, NJ
.
30.
Rausand
,
M.
, and
Høyland
,
A.
,
2004
,
System Reliability Theory: Models, Statistical Methods, and Applications
,
Wiley
,
Hoboken, NJ
.
31.
Sarhan
,
A. M.
, and
Apaloo
,
J.
,
2013
, “
Exponentiated Modified Weibull Extension Distribution
,”
Reliab. Eng. System Safety
,
112
(
4
), pp.
137
144
.
32.
Dale
,
C. J.
,
1985
, “
Application of the Proportional Hazards Model in the Reliability Field
,”
Reliab. Eng.
,
10
(
1
), pp.
1
14
.
33.
Jardine
,
A.
,
Anderson
,
P.
, and
Mann
,
D.
,
1987
, “
Application of the Weibull Proportional Hazards Model to Aircraft and Marine Engine Failure Data
,”
Qual. Reliab. Eng. Int.
,
3
(
2
), pp.
77
82
.
34.
Kumar
,
D.
, and
Klefsjö
,
B.
,
1994
, “
Proportional Hazards Model: A Review
,”
Reliab. Eng. System Safety
,
44
(
2
), pp.
177
188
.
35.
OREDA Participants
,
2009
,
Offshore Reliability Data Handbook
,
OREDA Participants
,
Trondheim, Norway
.
36.
Rohatgi
,
V. K.
, and
Saleh
,
A. K. M. E.
,
2011
,
An Introduction to Probability and Statistics
,
Wiley
,
New York
.
37.
Everitt
,
B. S.
, and
Hand
,
D. J.
,
1981
,
Finite Mixture Distributions
,
Chapman and Hall
,
London, UK
.
38.
McLachlan
,
G.
, and
Peel
,
D.
,
2000
,
Finite Mixture Models
,
Wiley
,
New York
.
39.
Zio
,
E.
,
2013
,
The Monte Carlo Simulation Method for System Reliability and Risk Analysis
,
Springer
,
London, UK
.
40.
Modarres
,
M.
,
Kaminskiy
,
M. P.
, and
Krivtsov
,
V.
,
2009
,
Reliability Engineering and Risk Analysis: A Practical Guide
,
CRC Press
,
Boca Raton, FL
.
41.
Baraldi
,
P.
,
Zio
,
E.
, and
Compare
,
M.
,
2009
, “
A Method for Ranking Components Importance in Presence of Epistemic Uncertainties
,”
J. Loss Prev. Process Ind.
,
22
(
9
), pp.
582
592
.
42.
Modarres
,
M.
,
2006
,
Risk Analysis in Engineering: Techniques, Tools, and Trends
,
CRC Press
,
Boca Raton, FL
.
43.
Natvig
,
B.
,
2011
,
Multistate Systems Reliability Theory With Applications
,
Wiley
,
West Sussex, UK
.
44.
Cooke
,
R. M.
, and
Goossens
,
L. H.
,
2008
, “
TU Delft Expert Judgment Data Base
,”
Reliab. Eng. System Safety
,
93
(
5
), pp.
657
674
.
You do not currently have access to this content.