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An expert-based approach to production performance analysis of oil and gas facilities considering time-independent Arctic operating conditions

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Abstract

The availability and throughput of offshore oil and gas plants operating in the Arctic are adversely influenced by the harsh environmental conditions. One of the major challenges in quantifying such effects is lack of adequate life data. The data collected in normal-climate regions cannot effectively reflect the negative effects of harsh Arctic operating conditions on the reliability, availability, and maintainability performance of the facilities. Expert opinions, however, can modify such data. In an analogy with proportional hazard models, this paper develops an expert-based availability model to analyse the performance of the plants operating in the Arctic, while accounting for the uncertainties associated with expert judgements. The presented model takes into account waiting downtimes and those related to extended active repair times, as well as the impacts of operating conditions on components’ reliability. The model is illustrated by analysing the availability and throughput of the power generation unit of an offshore platform operating in the western Barents Sea.

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Abbreviations

CDF:

Cumulative distribution function

DM:

Decision maker

FTTF:

First time to failure

GEN:

Generator

GT:

Gas turbine

MC:

Monte Carlo

MTTF:

Mean time to failure

MTTR:

Mean time to repair

O&G:

Oil and gas

ORDA:

Offshore reliability data

PDF:

Probability density function

PHM:

Proportional hazard model

RAM:

Reliability, availability, and maintainability

TR:

Train

TTF:

Time to failure

TTR:

Time to repair

E :

Degree of increase in MTTR of a component operating in an Arctic location. In other words, component MTTR increases by a factor of (1 + E). In E i , subscript i refers to component i

E′:

Time-independent factor by which component active repair rate is decreased due to the effects of Arctic operating conditions on maintenance crew performance

\(F_{i,E}^{DM} (\varepsilon )\) :

Decision maker’s CDFs of random variables E (i.e., the degree of increase in a component’s MTTR) corresponding to component i

\(F_{{i,\Delta }}^{DM} (\delta )\) :

Decision maker’s CDF of random variable Δ (i.e., the degree of reduction in a component’s MTTF) corresponding to component i

\(F_{TDT}^{(A)} (t)\) :

The CDF of total downtimes, including active repair times and waiting downtimes, corresponding to a component, whose repair is performed under Arctic operating environment

\(F_{TTF}^{(B)} (t)\) :

Failure probability function of a component operating in the base area. In \(F_{TTF}^{(A)} (t)\), superscript A refers to the Arctic

\(F_{TTR}^{(A)} (t)\) :

CDF of active TTRs of a component in the Arctic offshore

\(F_{WDT} (t)\) :

CDF of waiting downtimes

\(F_{\varPhi }^{DM} (\psi )\) :

Decision maker’s CDF of unknown random variable Φ

\(F_{\varPhi }^{j} (\psi )\) :

Expert j’s CDF of unknown random variable Φ

m :

Mean of the natural logarithm of WDTs

m :

Vector of the means of normal distributions fitted to experts’ data

m′:

Mean of the lognormal distribution of WDTs, \(F_{WDT} (t)\)

m j :

Mean of the normal distribution fitted to the data given by expert j

m DM :

Mean of DM’s distribution obtained by Bayesian aggregation of experts’ distributions

MTTF B :

Mean time to failure of a component operating in the base area. In MTTF A , subscript A refers to the Arctic

MTTR B :

MTTR of a component operating in the base area. In MTTR A , subscript A refers to the Arctic

N C :

Total number of system components

N e :

Total number of experts

N 1 s :

Number of required samples drawn from DM’s CDFs \(F_{{i,\Delta }}^{DM} (\delta )\) and \(F_{i,E}^{DM} (\varepsilon )\) to effectively represent uncertainties in system availability and throughput results

N 2 s :

Number of required samples drawn from waiting downtime and active repair distributions to form the distribution of total downtime

PGS s :

Power generation scenario s

TDT :

Total downtime corresponding to each corrective maintenance task, which includes both waiting downtime and active repair time

TTR :

Active time to repair

w j :

Expert j’s weighting factor

WDT :

Waiting downtime corresponding to each corrective maintenance task

y j :

Experience of expert j in years

β B :

Shape parameter of a Weibull failure probability function of a component operating in the base area. In β A , subscript A refers to the Arctic

Δ :

Degree of reduction in MTTF of a component in an Arctic location. In other words, component MTTF reduces by a factor of (1 − Δ). In Δ i , subscript i refers to component i

Δ′:

Time-independent factor by which component failure rate increases due to the effects of operating environment

ζ 1 :

A random number drawn from uniform distribution over (0, 1)

ζ 2 :

A random number drawn from uniform distribution over (0, 1)

η B :

Scale parameter of a Weibull failure probability function of a component operating in the base area. In η A , subscript A refers to the Arctic

λ B (t):

Weibull failure rate of a component operating in the base area. In λ A (t), subscript A refers to the Arctic

μ B :

Active repair rate of a component operating in the base area. μ B refers to the active TTRs and excludes other waiting downtimes. In μ A , subscript A refers to the Arctic

ρ jk :

Correlation coefficient of the data given by experts j and k

σ :

Standard deviation of the natural logarithm of WDTs

σ′:

Standard deviation of the lognormal distribution of WDTs, F WDT (t)

σ j :

Standard deviation of the normal distribution fitted to the data given by expert j

σ DM :

Standard deviation of DM’s distribution obtained by Bayesian aggregation of experts’ distributions

Σ :

Covariance matrix representing the correlation among experts

\(\{ \varDelta_{ji,5\% } ,\varDelta_{ji,50\% } ,\varDelta_{ji,95\% } \}\) :

The 5th, 50th, and 95th quantiles of the degree of reduction in MTTF of component i, given by expert j

\(\{ E_{ji,5\% } ,E_{ji,50\% } ,E_{ji,95\% } \}\) :

The 5th, 50th, and 95th quantiles of the degree of increase in MTTR of component i, given by expert j

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Acknowledgments

The authors would like to thank the anonymous experts for their participation in this study.

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Authors and Affiliations

  1. Department of Engineering and Safety, UiT – The Arctic University of Norway, PO Box 6050, Lagnes, 9037, Tromsø, Norway

    Masoud Naseri & Javad Barabady

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  1. Masoud Naseri
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  2. Javad Barabady
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Correspondence to Masoud Naseri.

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Naseri, M., Barabady, J. An expert-based approach to production performance analysis of oil and gas facilities considering time-independent Arctic operating conditions. Int J Syst Assur Eng Manag 7, 99–113 (2016). https://doi.org/10.1007/s13198-015-0380-4

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