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Optimization of Maintenance Strategies in an Oil and Gas
Production Facility to Minimize Maintenance Cost
Awara, Biokpomabo Festus
1,
, Ebieto Celestine
1,2
, Udeh Ngozi
1,3
,Omorogiuwa Eseosa
1,4
1
Institute of Engineering Technology and Innovation Management, University of Port Harcourt
2
Department of Mechanical Engineering, University of Port Harcourt, Nigeria
3
Department of Civil and Environmental Engineering, University of Port Harcourt
4
Department of Electrical Engineering, University of Port Harcourt, Nigeria
DOI : https://doi.org/10.51583/IJLTEMAS.2024.131005
Received: 18 October 2024; Accepted: 23 October 2024; Published: 05 November 2024
Abstract: In this study, maintenance strategies in an oil and gas production facility were optimized to minimize maintenance
costs. The centrifugal pump system in Port Harcourt Refinery was selected for this study because it significantly impacts on the
productivity of the refining plant. The pump system’s failure mode effects and criticality analysis (FMECA) were examined, and
an optimized maintenance task was developed for the system. The exponential reliability method was employed to analyse the
pump’s failure data to determine the pump systems' failure rate and reliability while reliability centred maintenance (RCM) and
linear programming (LP) model were employed to optimize the pump’s maintenance strategies and the pump’s maintenance
labour force respectively. Broad-based results of the optimized maintenance strategies consisted of on-condition-directed (CD)
maintenance, preventive maintenance (PreM), and Proactive maintenance (ProM) strategies to be carried out monthly. The
optimized maintenance labour force result revealed that approximately three engineers and two technicians should be employed
for the pump’s maintenance task with a minimum of N2, 585, 700 as cost of salaries for the labour force monthly as against the
current maintenance labour force requiring fourteen engineers and thirteen technicians monthly. The results showed that the
labour cost decreased from N180,000,000 per year to N31,028,400 per year (approximately 82.76% reduction) using the proposed
optimized maintenance task compared to the current maintenance plan. Conclusion and recommendation were made that the
maintenance optimization technique presented in this work should be adopted by the oil and gas processing and production
companies in order to minimize maintenance cost.
Keywords: Equipment, Pumps, Maintenance, Oil and Gas Industry, Optimization, Processing, Production, Reliability, Refinery,
RCM
I. Introduction
Maintenance is the act of holding, keeping, sustaining, or preserving assets (Murtjhy et al., 2002). Maintenance, a process of
production system management, is considered an activity or restoration process where a system or equipment has its failure
arrested, reduced, or eliminated (Ethevenin, 2010). Maintenance aims to extend the system or equipment lifetime or at least
extend the mean time to the next equipment failure, whose repair may be costly, thereby improving its availability and reliability
(Khathutshelo & Brian, 2018). Maintenance is the stamina of any manufacturing organization. Without having the proper
maintenance strategy and practice, an organization’s assets or equipment cannot sustain its performance. It may depreciate
quickly, impacting the organization's productivity and profitability. Oil and gas processing facilities rely on equipment and
machinery for efficient processing and robust production, such as pump systems, heat exchangers, valves, compressors, etc.
(Ilogamhc & Emmanuel, 2014).Inefficient and effective maintenance strategies for this equipment can result in a huge negative
impact on production quality, productivity, and profitability (Alsyouf, 2007).
Maintenance strategy depends on several factors: maintenance goals, the nature of the facility or equipment to be maintained,
workflow patterns, and the work environment (Al-Najjar, 2000). According to Misikir (2004), maintenance practices and systems
must be incorporated with a set of maintenance strategies and maintenance performance indicators for production improvement.
Due to equipment failures, poor equipment effectiveness in production industries poses economic problems and losses, especially
in cost (Zineb et al., 2017). Maintenance costs over 70% of the total production expenditures, and to reduce maintenance costs,
the various types of losses in the manufacturing industry must be identified, classified, and eliminated (Samuel et al., 2018).
Effective utilization of man, machines, materials, and methods will result in higher productivity (Goodfellow, 2000; Itthipol et al.,
2017). Maintenance and asset-management functions can increase profits in two main ways: by decreasing running costs and
increasing capability. The total maintenance cost depends on the quality of the equipment, the way it is used, the maintenance
policy, and the business strategy (Brah & Chong, 2004). The Port Harcourt Refining Company (PHRC) Limited is in business to
optimally process hydrocarbon into petroleum products for the benefit of all stakeholders (PHRC, 2021). PHRC Limited is made
up of two refineries-the old and new refinery. The old refinery was constructed in 1965 at Alesa Eleme in Port Harcourt (PH1)
with a capacity of 35,000 bpsd. In the 1970s, the capacity was increased to 60,000 bpsd to accommodate the rapidly expanding
Nigerian economy (PHRC, 2021). The new refinery, known as PHII, since it is connected to the old refinery in Alesa Eleme, was
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completed and put into service in March 1989. It has an installed capacity of 150,000 bpsd. As a result, the Port Harcourt
Refinery can now process 210,000 bpsd of crude oil (PHRC, 2021). It has five (5) process sections, numbered from 1 to 5. Areas
1-4 of the new refinery comprise the structure, while Area 5 is the old refinery (PHRC, 2021). It is also has a power plant and
utilities section. The power plant consists of two (2) deaerators, five (5) centrifugal feed water pumps, four (4) boilers capable of
generating 120 tons of steam per hour each, four (4) steam turbine units (4 x 14) MW at 100% load and four (4) condensers
(PHRC, 2021),
Maintenance strategy may be inefficient; it may be too costly (in the long run) if it is done too often, and if it is done too little, it
may result in an excessive number of failures, reducing the system’s reliability (Goodfellow, 2000). The maintenance strategy
must be optimized for a cost-effective scheme, and reliability centred maintenance (RCM) approach provides that solution
(Afefy, 2010). Reliability-centred Maintenance is the optimum mix of reactive and run-to-failure, time- or interval-based,
condition-based, and proactive maintenance practices. Rather than being applied independently, these principal maintenance
strategies are integrated to take advantage of their strengths to maximize facility and equipment reliability while minimizing life-
cycle costs. RCM philosophy employs preventive maintenance, predictive maintenance (PdM), real-time monitoring (RTM), run-
to-failure (RTF), and proactive maintenance techniques in an integrated manner to increase the probability that a machine or
component will function in the required manner over its design life cycle with a minimum of maintenance (Afefy, 2010). RCM is
a systematic approach to determining plant and equipment maintenance requirements for its operation. It is used to optimize
preventive maintenance (PM) strategies. Sequel to the above, RCM is a process used to determine the maintenance requirements
of any physical asset in its operating context. This is based on equipment condition, criticality, and risk (Afefy, 2010). Studies
reveal that much attention is paid to the problem of optimizing maintenance strategies for production systems, such as
Goodfellow (2000), Bhangu et al. (2011), Ahasan (2015) and Samuel et al. (2018). Zhu et al. (2019) focused on the reliability
analysis of centrifugal pumps based on small-scale sample data, and Itthipol et al. (2017) conducted a reliability analysis for
refinery plants.
II. Methodology
This study optimized a comprehensive maintenance management strategy for production equipment in the Port-Harcourt refinery.
Failure data of the oil and gas processing equipment obtained from the refinery was employed for reliability analysis and to
improve its operations. The oil and gas production equipment were resolved into the smallest component of a system facilitating
the formulation of FMECA and Root Cause Failure Analysis (RCFA).To analyze the collected failure data, the exponential
reliability method was applied to analyse the oil and gas processing equipment's failure rate and other reliability parameters and
generate the RCM task. The generated RCM plan uses a predictive and preventive maintenance strategy (not just the reactive and
run-to-failure maintenance strategies currently used in the refinery).The method adopted for this research work is the RCM
methodology with a linear programming technique for analysis.
Reliability Model
The centrifugal pump system for the time between failures follows the Weibull distribution, where t is the continuous random
variable representing the failure time. The Probability Density Function (PDF) of the Weibull distribution is given by (Igor,
2004):
tt
tf exp..);;(
1
(1)
Where:
t = hours of operation or uptime
θ = scale parameter of the Weibull distribution
β = the shape parameter.
A value of β > 1 signifies an increasing failure rate (or hazard rate) function, whereas a value of β < 1 signifies a decreasing
failure rate function. When β = 1, the failure rate function is constant. The scale parameter of the Weibull distribution, denoted
by θ, influences both the distribution's mean and spread. As θ increases, the reliability at a given point in time increases, whereas
the slope of the hazard rate decreases. When the failure rate (λ) of the system is determined, the reliability R (t) and the
unreliability F (t) of the centrifugal pump system at the end of (t) hours of operation/ up time from exponential reliability model
as given by(Igor, 2004):
t
etR
)(
(2)
t
etF
1)(
(3)
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Where:
= Failure rate of the centrifugal pump system.
t = time in operation of the centrifugal pump system.
Determination of Parameters for Reliability Model
The mean time between failures (MTBF), mean time to repair (MTTR), and failure rate parameters were computed to determine
the availability, maintainability, and ultimately the reliability of the centrifugal pump system using the exponential reliability
model as follows:
Mean Time Between Failures (MTBF)
The MTBF is a basic measure of reliability for reparable items. It is estimated by the total time in the operation of the centrifugal
pump system and its subsystems divided by the total number of failures (breakdowns) recorded within a specific investigation
period. Mathematically,
MTBF =
n
t
I
(4)
Where:
I
t
= the total running time in the operation of the centrifugal pump system during an investigation period for both failed and
non-failed items.
n = number of failures (breakdowns) of centrifugal pump system or its parts occurring during a certain investigation period.
Mean Time to Repair (MTTR)
Mean time to repair (MTTR) is the average time required to troubleshoot and repair failed equipment and return it to normal
operating conditions. It reflects how well the system can respond to and repair a problem. It is suitable for all kinds of systems,
and it is given by (Igor, 2004):
repairsofnumberTotal
timeenanceMaTotal
MTTR
int
MTTR =
n
t
i
(5)
Where:
t
1
= total accumulative time of the centrifugal pump system to repair or maintain in statistical time.
n = number of repair actions in the population of the centrifugal pump system during the specified investigation period.
Failure Rate (λ)
Failure rate is the probability of failure per time unit. It is the rate of occurrence of failures. It is the reciprocal of the MTBF
/MTTF function and is given by (Igor, 2004):
1
1
t
n
MTBF
(6)
Where:
I
t
= the total running time in the operation of the centrifugal pump system during an investigation period for both failed and
non-failed items.
n = number of failures (breakdowns) of centrifugal pump system or its parts occurring during a certain investigation period.
Repair Rate
The repair rate is the probability of repair per time unit. It is the rate of occurrence of repairs. A repair rate is used for systems
with repairable parts. It is the reciprocal of the MTTR function and is given by (Igor, 2004):
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MTTR
1
(7)
Where,
MTTR = Mean time to repair
Availability
The "availability" of a system is, mathematically, MTBF / (MTBF + MTTR) for scheduled working time. It is given by (Igor,
2004):
A =
)( MTTRMTBF
MTBF
(8
Or A =
1
TT
T
O
O
(9)
Where:
T
0
= uptime of the centrifugal pump system.
T
1
= downtime of the centrifugal pump system, including repair and maintenance time.
III. Results
Failure data of the pump system was used to analyse the reliability metrics of the pump equipment for maintenance.
Table 1: Failure Data of the Centrifugal Pump System in the Refinery
Centrifugal
Pump System
Number of Failure
Operating Time (hrs)
Downtime (hrs)
Total Available Time (hrs)
P01
13
7827.02
452.98
8280.00
P02
7
8101.60
178.40
8280.00
P03
9
8044.84
235.16
8280.00
P04
5
8167.65
112.35
8280.00
P05
11
7973.28
306.72
8280.00
Table 2: Current Equipment Labour Cost.
Item
Labour type
Number of labours Per day (Current Maintenance)
Engineers (N 700 000.00/month)
Mechanical
4
Electrical
6
Control
4
Technician (N 400, 000.00/month)
Mechanical
6
Electrical
Control
6
1
Total cost (Naira/year)
180,000, 000
Reliability Analysis of the Centrifugal Pump System in the Oil and Gas Production Facility
Table 3 presents the reliability parameters for the centrifugal pump system in the refinery.
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Table 3: Reliability Indices of the Centrifugal Pump System in the Refinery
Centrifug
al Pump
System
Operating
Time (hrs)
Downtime
(hrs)
MTBF
(hrs/failu
re)
MTTR
(hrs/repai
r)
Failure rate
(failure/hr)
Repair rate
(repair/hr)
Availability
Reliability
(%)
P01
7927.02
352.98
602.07
34.85
0.001660
0.0287
0.945
47.2
P02
8101.60
178.40
1157.37
25.49
0.000864
0.0392
0.979
85.7
P03
8088.84
191.16
893.87
26.12
0.001120
0.0383
0.972
76.9
P04
8177.65
102.35
1633.53
22.47
0.000612
0.0445
0.985
93.4
P05
8033.28
246.72
724.84
27.88
0.001380
0.0359
0.963
65.5
Figure 1 represents the mean time between failures (MTBF) of the centrifugal pump system in the refinery. The results show that,
out of the five (5) centrifugal pumps that make up the centrifugal pump system in the power plant, pump P04 has the highest
MTBF with 1633.53 hours, and pump P01 has the lowest MTBF with 602.07 hours within the study period.
Figure 1: Centrifugal Pump System’s Mean Time Between Failures (MTBF)
Figure 2 represents the mean time to repair (MTTR) of the centrifugal pump system in the refinery. The results show that, out of
the five (5) centrifugal pumps that make up the centrifugal pump system in the refinery, pump P01 has the highest MTTR with
34.85 hours, and pump P04 has the lowest MTTR with 22.47 hours within the study period.
Figure 2: Centrifugal Pump System’s Mean Time to Repair (MTTR)
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The failure rate of the centrifugal pump system in the refinery is represented in Figure 3. The results show that out of the five (5)
pumps that make the centrifugal pump system in the refinery, pump P01 has the highest failure rate at 0.001660failure/hr, and
pump P04 has the lowest failure rate with 0.000612failure/hr within the study period.
Figure 3: Centrifugal Pump System’s Boiler’s Failure Rate
Figure 4 represents the repair rate of the centrifugal pump system in the refinery. The results show that out of the five (5) pumps
that make the centrifugal pump system in the refinery, pump P04 has the highest repair rate at 0.0445repairs/hr, and pump P01
has the lowest repair rate at 0.0287repairs/hr within the study period.
Figure 4: Centrifugal Pump System’s Repair Rate.
Figure 5 represents the availability of the centrifugal pump system in the refinery. The results show that, out of the five (5) pumps
that make up the centrifugal pump system in the refinery, pump P04 has the highest availability with 0.985, and pump P01 has the
lowest availability with 0.945 within the study period.
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Figure 5: Centrifugal Pump System’s Availability (A)
The reliability of the centrifugal pump system in the refinery is represented in Figure 6. The results show that, out of the five (5)
pumps that make the centrifugal pump system in the refinery, pump P04 has the highest reliability with 93.4%, and pump P01 has
the lowest reliability with 47.2% within the study period.
Figure 6: Centrifugal Pump System’s Reliability (R)
The maintenance strategy is directed towards the item, a major contributor to system failures (Afefy, 2010). In the present case,
pump P01as has the least reliability at 47.2% and the highest failure rate at 0.00166 failures/hr. Table 4 revealed that the
centrifugal pump failure mode effect analysis and the criticality analysis for the centrifugal pump were used to generate the
maintenance plan for the pump, as presented in Table 5.
Table 4: Criticality Analysis for the Centrifugal Pump
Equipment
Failure
Mode
Failure Cause
Criticality Analysis
Criticality
Index
Group/Level
Safety
Production
Cost
Low
discharge
pressure
Water
excessively hot
2
3
1
2.2
B (Medium-
High)
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Pump
High bearing
temperature
Bent shaft
3
3
3
3.0
A (High)
Worn bearing
3
3
2
2.8
A (High)
Lack of
lubrication
3
3
2
2.8
A (High)
Improper
installation of
bearing
3
3
2
2.8
A (High)
Pump casing
overheats
Misalignment of
pump drive
motor
3
3
3
3.0
A (High)
Shaft sleeve
worn
3
3
3
3.0
A (High)
Low flow
Impeller
damaged on
loose shaft
3
3
3
3.0
A (High)
Table 5: Centrifugal Pump Maintenance Task.
Equipment
Failure Mode
Failure cause
Group
Task
Description
Frequency
Pump
Low
discharge
pressure
Water
excessively hot
B (Medium-
High)
CD
Check the
temperature of
water
Monthly
High bearing
temperature
Bent shaft
A (High)
CD
Check and
replace the bent
shaft
Monthly
Worn bearing
A (High)
ProM
Check and
replace worn
bearing
Monthly
Lack of
lubrication
A (High)
PreM
Lubricate
adequately
Monthly
Improper
installation of
bearing
A (High)
CD
Check bearing
for improper
installation
Monthly
Pump casing
overheats
Misalignment of
pump drive
motor
A (High)
CD
Check pump
drive motor for
misalignment
Monthly
Shaft sleeve
worn
A (High)
CD
Check and
replace worn
shaft sleeve
Monthly
Low flow
Impeller
damaged on
loose shaft
A (High)
CD
Check for loose
shaft and
replace
damaged
impeller
Monthly
Optimization of Maintenance Labour Cost
Table 2 shows the facility's current maintenance force: four mechanical engineers, four electrical engineers, four control
engineers, six mechanical technicians, six electrical technicians, and one control technician. The facility's current maintenance
task costs the industry N180, 000, 000 annually. The maintenance cost was optimized using a linear programming method.
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Table 6: Model Formulation
Labour type
Cost of Salaries
M
El
C
(x 10
3
Naira
Labour Rank
E
4
6
4
699
T
6
6
1
399
Quantity available
20
14
13
Let the number of engineers needed for maintenance (E) = x
1
Let the number of technicians needed for maintenance (T) = x
2
Let F denote the cost to be minimized
The linear programming model for the above production data is given by:
21
399000699000 xxFMin
..tS
2064
521
xx
4166
521
xx
134
521
xx
0,
21
xx
Converting the model into its corresponding standard forms;
2121
0039000699000 ssxxFMin
..tS
2064
1
21
sxx
4166
221
sxx
134
321
sxx
0,,,
2121
ssxx
The formulated linear programming model was solved, and the fourth iteration gave an optimal solution ofx
1
=2.9, x
2
= 1.4, and
F =2585700 naira.
IV. Conclusion
The results of the optimized maintenance strategies using reliability-centred maintenance and linear programming model applied
for the centrifugal pump system in the oil and gas production facility generated the reliability centred maintenance tasks and plan.
Results of the optimized maintenance strategies consisted of on-condition-directed (CD) maintenance, preventive maintenance
(PreM), and Proactive maintenance (ProM) strategies to be carried out monthly. The optimized maintenance labour force results
showed that approximately three engineers and two technicians should be employed for the maintenance task to spend a
minimum of N2, 585, 700 naira as the cost of salaries for the labour force monthly and N31, 028, 400 annually. The results
showed that the labour cost decreased from N180,000,000/year to N31,028,400/year (approximately 82.76% reduction) using the
proposed optimized maintenance plan compared to the current maintenance plan. The study recommends that the maintenance
optimization technique presented in this work should be adopted by the oil and gas processing and production companies in order
to minimize maintenance cost.
V. Acknowledgements
I am grateful to all whose gift of wisdom and understanding directed me through this research work. I wish to express my
profound gratitude to my supervisor, Engr. Prof. Omorogiuwa, Eseosa who has been the ideal project supervisor. His sage advice,
insightful criticisms, and patient encouragement aided the writing of this project in innumerable ways. I cannot forget the
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contributions of other academics in the Institute of Engineering Technology and Innovation Management (METI), Centre for
Engineering Technology Management (CETM), Faculty of Engineering, University of Port Harcourt especially Engr. Dr. Ebieto
Celestine and Udeh Ngozi for their ardent tutelage.
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