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Risk and Reliability Analysis of a Subsea System for Oil Production
Keith Dillian Schneider
Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
The present project was accomplished with the support of CNPq, Conselho Nacional de Desenvolvimento Científico e Tecnológico – Brasil.
ABSTRACT: There are many layouts for offshore oil and gas production that can be implemented that differ on the topside and
subsea equipment. When applying new technologies in subsea production systems there is few information in conceptual design,
regarding sizes of equipment, capacities, failure statistics, among others. The objective of this work is to present a model to assess
the risk and reliability of subsea equipment used in offshore oil production fields. Two scenarios of offshore production layout are
compared in terms of risks and reliability: conventional and hybrid scenarios. The conventional scenario separates water and gas
from oil in a topside system of a floating production storage and offloading (FPSO), while the hybrid scenario uses a subsea separator
that allows the water to be reinjected into the well without being lifted together with the well fluid, letting more space available in
the FPSO for oil storage. Simplified reliability models are presented for the subsea separator, pumps, flowlines and risers. As there
is not enough failure data to characterize the reliability of the subsea separator, a methodology is applied to predict its reliability
using information from a similar topside separator. The reliability prediction approach is illustrated with a case study of an oil field
located in Brazil.
1 INTRODUCTION
With the increasing of energy demand and production, new
subsea technology is constantly being implemented in offshore
production. Innovation is only limited by costs and permissible
risks the project can be exposed to.
Risk analysis has a major importance in offshore industry. Not
only the costs of an accident can be expressively high, or
environment can be strongly affected, but lives are exposed,
which increases the social responsibility in offshore operations.
Christou and Konstantinidou (2012) also emphasize the indirect
economic consequences, using as example the accident of
Deepwater Horizon, in which the company had losses from the
fail in price of shares (shares have fallen around 50% in June
2010).
Risk analysis can be used as a decision supporting tool to
improve conceptual design. However, this analysis requires
failure data from equipment that are not available for new
equipment or equipment that will work in different
environmental conditions, such as at the subsea. In conceptual
design stage is normal to have many assumptions and
proportionally many uncertainties. The design team has an idea
of what kind of equipment can be implemented, but not enough
information regarding sizes, capacities and costs. Added to that,
when the project has innovation this task probably has more
uncertainties. According to Duan et al. (2018), subsea
production systems are important for exploitation of offshore oil
and gas, as an efficient and cost-effective plan for deepwater
fields. The challenge of new technology is to find information
to predict their reliability. As offshore equipment is designed to
have a long life, equipment does not fail in such frequency in
order to provide trustworthy conclusions from statistics.
Because of that, statistics of reliability data such as OREDA
(Offshore Reliability Data), SINTEF (2002); Handbook of
Reliability Prediction Procedures for Mechanical Equipment,
LTS (2010); and Guidelines for process equipment reliability
data, AIChE (1988), may not be sufficient to make a reasonable
prediction, and methodologies are being implemented with the
objective to predict failure rates of new subsea equipment based
on existing similar topside equipment.
The objective of this thesis is to present a model to assess the
risk and reliability of subsea equipment used in offshore fields
for oil production.
2 RISK ANALYSIS APPLIED IN
OFFSHORE INDUSTRY
Brandsæter (2002) analyzes the implementation of risk
assessment in the offshore industry, with focus on safety
aspects, and in quantitative risk assessment. Based on a research
with professionals in the field, it was indicated that the
perception of risk in offshore industry is not uniform. While the
extent of potential damage is high, the probability of occurrence
and uncertainties perception are ranged from low to medium.
Yasseri and Bahai (2018) calculated the availability of a subsea
distribution plant at the design stage using as reliability tool
DSM. The access of failure rate of equipment was directly made
using OREDA (2002), considering that the failure rates of
equipment is constant, which could be a source of error in
analysis, but in the architecture level of design is acceptable,
regarding the available data is scarce. Also, some assumptions
for the modelling were made, and must be updated as the design
progresses.
It is known that for risk and reliability analysis reliability data
of the components and systems are necessary to properly
evaluate the design or operation. In some cases, the studies and
projects must be developed not having total access to reliability
data, and for that there are some tools that can be used to predict
the failure characteristics of the equipment to overcome the lack
of data.
The method BORA-release, by Sklet et al. (2006) analyzes the
hydrocarbon release frequency, taking into account the effect of
the safety barriers used for release prevention. The method also
analyzes the influence of RIFs (Reliability Influence Factor) -
as technical, human, operational and organizational conditions-
in the barriers performance. One challenge of this method is the
lack of input data available, especially in human reliability in
the offshore field.
Vinnem et al. (2009) applied the BORA method in a study case
related to an offshore oil and gas production platform. The
2
situations in which the method would be applied were decided
in discussions between personnel from the oil company and
project team members, and it was decided to apply it in three
situations: release due to valve(s) in wrong position after
maintenance (A), release due to incorrect fitting of flanges or
bolts during maintenance (B), and release due to internal
corrosion (C). The results for situations A and B presented
higher release frequencies when compared with industry
average data, which is explained by the status of several of the
RIFs measured by the RNNS-data was worse than the industry
average standard. The quantitative results for scenarios A and B
were reasonable compared to release statistics. A question is
raised regarding how specific the assessment of the status of
RIFs needs to be. Results for scenario C did not presented the
same confidence from results of scenarios A and B, because the
phenomenon of corrosion is complex, and the assumptions
made for this work must be discussed. Sensitivity analyses
performed in the study supported the conclusion that the method
can a useful tool to analyses the effect on the release frequency
of safety barriers introduced to prevent hydrocarbon releases.
Rahimi and Rausand (2013) proposed an approach to determine
the failure rates of new subsea systems, making a detailed
comparison with the topside systems. Being subsea systems
adapted from topside systems, the reliability information cannot
be used directly for subsea design, due to design modifications,
different environmental stresses and maintenance. A reliability
data for topside systems are typically available, the approach
applied RIFs to analyze subsea systems.
Abdelmalek (2018), performed a semi-quantitative risk
assessment of a subsea production system in the conceptual
phase. It was used as tool the concept of RIFs to applicate in
subsea equipment, and ETA, linking the end terminals with
different consequence groups, denotating quantified
magnitudes of consequences. A practical demonstration is made
applying semi-quantitative risk assessment in an offshore field
located in Brazil.
A study conducted by Silva (2016) analyzed the relevance of
risk analysis in operational safety regarding subsea pipelines for
transport systems. A tool from DNV (2009) was used to
calculate failure rates for two case studies in North Sea,
considering not just the failure modes, but also a number of
factors that influence the likelihood of failure, such as age, size,
length of line and location. As a result, both cases revealed that
the failure frequency was directly proportional on the size
length.
3 METHODOLOGY
According to Rausand and Høyland (2004), in a qualitative risk
analysis, probabilities and consequences are determined purely
by qualitative characteristics,. As presented in the previous
chapter, there are several tools for qualitative analysis. In this
project the FMEA method is selected for a preliminary analysis
of the subsea production concept.
FMEA is a tool that can be used to acquire an overview of types
of failures that can happen in the system, their consequence, and
helps to determine ways to minimize their occurrence. It can be
implemented for systems functions, subsystems or components.
Quantitative risk analysis gives numerical estimates of
probabilities and consequences, and eventually these estimates
have some uncertainties, according to Rausand and Høyland
(2004). This approach is best suited for risk associated with low
probability of occurrence and high consequence events.
Reliability is defined as a characteristic of the ability of a
component or a system to perform a specific function (Aven,
1992). Important measures of the reliability of a nonrepairable
component are: reliability function 𝑅(𝑡), failure rate function
𝑧(𝑡) , and mean time to failure 𝑀𝑇𝑇𝐹 . To access this
information many distributions can be used, depending on data
available and failure modes, as exponential distribution,
Weibull distribution, gamma distribution, lognormal
distribution, and others.
Time to failure is understood as the time that an item takes to
failure, since it started the operation. It is interpreted as a
random variable, 𝑇 . The time to failure is often a discrete
variable, but can, however, be considered as a continuous
variable. In this case it is assumed that the time to failure 𝑇 is
continuously distributed with probability density function 𝑓(𝑡) and distribution function 𝐹(𝑡).
The reliability function is presented in equation [1]. Being the
complement of failure probability function, reliability of an item
is the probability that this item will survive for a time 𝑇 greater
than 𝑡.
𝑅(𝑡) = 1 − 𝐹(𝑡) = Pr(𝑇 > 𝑡) 𝑓𝑜𝑟 𝑡 > 0 [1]
The lack of data in reliability and risk analysis may lead to
analysis with increasing uncertainties. According to Aven
(1992), reliability statistics can be used monitor the reliability
level; identify critical components, equipment and systems;
analyze causes of failure; or evaluate the effect of measures and
prioritize between different measures. Statistics of failures and
test reports on component, equipment and systems may
originate reliability data.
Rausand (2011) presented that reliability data is related to
frequency how and components and subsystems in a system
may fall. There are generic databases that provide average data
for a wide application area, and company-specific databases,
that are based on reports of failures and other events in the
application of equipment. Examples of databases are MIL-
HDBK-217F and IEC TR 62380 for electronic equipment,
MechRel for mechanical equipment, and OREDA, that is a
database for the offshore and oil industry.
In this study reliability data is based on OREDA (SINTEF 2002)
information. OREDA provides data for topside and subsea
equipment, but the range of subsea equipment is not yet
embracing. For that reason, a reliability prediction method is
used in this project, for subsea equipment that has not
probability/frequency information available in OREDA, topside
equipment will be used, and corrected by the application of
RIFs.
As expected, a subsea equipment has different failure rate from
a similar topside equipment. As the environment are not the
same, the stresses that affect the components and systems vary.
To adapt the reliability information from topside to subsea
equipment, reliability prediction methods are required.
Rahimi and Rausand (2013) developed an approach for failure
rate prediction of new subsea equipment, that can be used in
design and development phases. The methodology is based on
the application of RIFs to determinate the failure rate of subsea
equipment. The methodology developed by Rahimi and
Rausand (2013) is performed in eight steps, explained below:
3
1st step: New system familiarization
The new subsea system in study must be clearly defined, taking
in account physical boundaries, operational and environmental
conditions. The recommendation is that the system is
represented hierarchically with subsystems and maintainable
items. DNV (2011) has a set of recommendations in which rises
the importance of list the critical components, reporting the
main characteristics, in the form of drawings, text, data, etc.
2nd step: Identification of failure modes and failure causes
Failure modes and failure causes must be identified, and for that
a FMEA could be appropriated. The potential failure modes
should be studied, with the respective failure causes and
mechanisms, considering all operational modes. This phase of
analysis can be carried out by a FMEA sheet. An influence
diagram, as presented in Figure 1, is recommended to better
understand the relation between failure cause and failure mode,
and the possible RIFs that may influence. The authors
emphasizes that is important that the failure causes that lead to
the failure modes are defined as a single failure cause, not as a
combination of failure causes, and they give as example as a
failure mode that is induced by two separate causes “high flow
and high content of sand in the fluid”, the failure cause should
be specified as “high flow and high content of sand in the fluid”.
Step 3: Reliability information acquisition for the similar known
system; comparison of thee new and the known system
In this stage, a similar topside system is found, with similar
functions and structure. For this system, the reliability
information is obtained from OREDA. The following
mathematical assumptions are made:
𝜆𝑇 = 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 𝑡𝑜𝑡𝑎𝑙 𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑟𝑎𝑡𝑒 𝑓𝑜𝑟 𝑡𝑜𝑝𝑠𝑖𝑑𝑒 𝑠𝑦𝑠𝑡𝑒𝑚
𝜆𝑖(𝑇)= 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑟𝑎𝑡𝑒 𝑜𝑓 𝐹𝑀𝑖 , 𝑓𝑜𝑟 𝑖 = 1,2, … , 𝑛
𝛼 = (𝛼1, 𝛼2, … , 𝛼𝑛) = 𝑑𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 𝑜𝑓 𝑡ℎ𝑒 𝑛 𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑚𝑜𝑑𝑒𝑠
𝛼𝑖 = 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑡ℎ𝑎𝑡 𝑡ℎ𝑒 𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑚𝑜𝑑𝑒 𝑖𝑠 𝐹𝑀𝑖
Figure 1 - Factors that contributes to the total failure of the
system. Rahimi and Rausand (2013)
When the failure modes (FM) are disjoint, the constant total
failure rate is the sum of each failure rate of the correspondent
FM, as presented in equation [2].
𝜆(𝑇) = ∑ 𝜆𝑖(𝑇)𝑛
𝑖=1 [2]
𝜆𝑖(𝑇)= 𝛼𝑖 × 𝜆
(𝑇) [3]
Equation [2] indicates that if a failure mode has occurred, the
failure rate of this failure mode is a percentage of the total
failure rate.
The failure mode may not be independent in all situations, as
illustrated in Figure 2.
Figure 2 - Subsea and topside system comparison - Rahimi and
Rausand (2013)
As can be seen in Figure 2, failure modes can be different for
subsea and topside systems. All the differences between the new
and the known systems must be recorded. The dashed lines
indicate that the information belongs only to topside failure rate,
the thick lines indicates that it belongs for both systems, and the
thin lines only for subsea system.
Step 4: Selection of relevant RIFs
The objective is to find out how a RIF can influence the failure
rate changes. Definition of RIFs is made based on analysis made
in Step 3 and in expert judgment. In Table 1 are presented
generic RIFs, categorized by design and manufacturing,
operation and maintenance. This table can be used as a checklist
when deciding which RIFs must be applied in the subsea
system, that must be selected by experts. It is important to select
the RIFs in a manner that the failure is influenced by individual
RIFs, and not by a set of RIFs.
After being specified, the RIFs must be ranked according to
their relevance for each failure cause. This ranking can be based
in a weight, 𝜀𝑘𝑗, related to the 𝑅𝐼𝐹𝑘 and the 𝐹𝐶𝑗. The weights
should be scaled in a manner that satisfy equation [4].
∑ 𝜀𝑘𝑗𝑝𝑘=1 = 1 [4]
For 𝑘 = 1,2, … , 𝑝, for each 𝐹𝐶𝑗.
Step 5: Scoring the effects of the RIFs
Some RIFs may have more influence for topside or subsea
4
Table 1 - Generic RIFs, adapted from Rahimi and Rausand
(2013)
Category RIFs
Design and
manufacturing
System structure
Materials
Dimensions
Loads and capacities
Quality (manufacturing process, installation, logistics, assembly, …)
Operational and
maintenance
Functional requirements
Time in operation
Mechanical constraints
Frequency of maintenance
Maintenance policy
Accessibility for maintenance
Type and quality maintenance
Environmental External Temperature
Location of operation
Pressure
Corrosive environment
Pollution
Environmental Internal Pressure
Sand particles in the fluid
Chemical content
systems, and it must be clearly identified when comparing RIFs
on failure causes for both systems. The indication of which RIFs
are relevant for the failure can be made using the indication 𝑣𝑘𝑗(𝑇)
and 𝑣𝑘𝑗(𝑆)
for topside and subsea systems, respectively. The
indications are presented in equations [5] and [6].
𝑣𝑘𝑗(𝑇)= {
1 𝑖𝑓 𝑡ℎ𝑒 𝑅𝐼𝐹𝑘 ℎ𝑎𝑠 𝑒𝑓𝑓𝑒𝑐 𝑜𝑛 𝑡𝑜𝑝𝑠𝑖𝑑𝑒 𝐹𝐶𝑗0 𝑖𝑓 𝑡ℎ𝑒 𝑅𝐼𝐹𝑘 ℎ𝑎𝑠 𝑛𝑜 𝑒𝑓𝑓𝑒𝑐 𝑜𝑛 𝑡𝑜𝑝𝑠𝑖𝑑𝑒 𝐹𝐶𝑗
[5]
𝑣𝑘𝑗(𝑆)= {
1 𝑖𝑓 𝑡ℎ𝑒 𝑅𝐼𝐹𝑘 ℎ𝑎𝑠 𝑒𝑓𝑓𝑒𝑐 𝑜𝑛 𝑠𝑢𝑏𝑠𝑒𝑎 𝐹𝐶𝑗0 𝑖𝑓 𝑡ℎ𝑒 𝑅𝐼𝐹𝑘 ℎ𝑎𝑠 𝑛𝑜 𝑒𝑓𝑓𝑒𝑐 𝑜𝑛 𝑠𝑢𝑏𝑠𝑒𝑎 𝐹𝐶𝑗
[6]
As the effects of each RIF are different from topside to subsea
systems, the influence score 𝜂𝑘𝑗 indicates how much higher or
lower this 𝑅𝐼𝐹𝑘 influences 𝐹𝐶𝑗 in the subsea system,
comparing with topside system. It is suggested by the authors to
use the scale of Table 2 to assign the scores.
Table 2 - Scale for scoring of RIFs, adapted from Rahimi and
Rausand (2013)
-3 -2 -1 0 1 2 3
Much
lower
effect
Significantly
lower effect
Slightly
lower
effect
No
differe
nce
Slightly
higher
effect
Significantly
higher effect
Much
higher
effect
If the indicator from [6] is equal to 1, the seven points from
Table 3 are applicable, otherwise, if the 𝑅𝐼𝐹𝑘 has no effect on
topside, only the positive scoring must be used. Table 3 shows
a summary of information for scoring the RIFs.
Step 6: Weighing the contribution of the failures causes to
failure modes
The failure causes that lead to a failure mode can have a
different contribution for topside and subsea systems. The
weight 𝜔𝑗𝑖(𝑇)
represents how much 𝐹𝐶𝑗 contributes for 𝐹𝑀𝑖 .
These weights for topside system can be deduced from data base
from OREDA, where is assumed that FC are disjoint and the
sum of weights of each FM is 1. For the subsea system, in other
hand, these weights must be determined and obtained from
expert judgments, technical reports, etc. The contributing
weight of 𝐹𝐶𝑗 to 𝐹𝑀𝑖 is 𝜔𝑗𝑖(𝑆)
, according to equation [7].
∑ 𝜔𝑗𝑖(𝑆)𝑟
𝑗=1 = 1 [7]
Table 3 - Scoring of RIFs based on comparison with topside
system, adapted from Rahimi and Rausand (2013)
Reliability influencing factor Failure cause
𝐹𝐶1 𝐹𝐶2 … 𝐹𝐶3
𝑅𝐼𝐹1
Relevance topside 𝜈11(𝑇)
𝜈12(𝑇)
… 𝜈1𝑟(𝑇)
Relevance subsea 𝜈11(𝑆)
𝜈12(𝑆)
… 𝜈1𝑟(𝑆)
Scoring topside/subsea 𝜂11 𝜂12 … 𝜂1𝑟
𝑅𝐼𝐹2
Relevance topside 𝜈21(𝑇)
𝜈22(𝑇)
… 𝜈2𝑟(𝑇)
Relevance subsea 𝜈21(𝑆)
𝜈22(𝑆)
… 𝜈2𝑟(𝑆)
Scoring topside/subsea 𝜂21 𝜂22 … 𝜂2𝑟
…
…
…
…
…
…
𝑅𝐼𝐹𝑃
Relevance topside 𝜈𝑝1(𝑇)
𝜈𝑝2(𝑇)
… 𝜈𝑝𝑟(𝑇)
Relevance subsea 𝜈𝑝1(𝑆)
𝜈𝑝2(𝑆)
… 𝜈𝑝𝑟(𝑆)
Scoring topside/subsea 𝜂𝑝1 𝜂𝑝2 … 𝜂𝑝𝑟
where 𝑖 = 1,2, … , 𝑞, being 𝑞 the number of FM that is similar
for both topside and subsea systems.
Step 7: Determination of the failure rate for similar failure
modes
The failure rates for FM of subsea systems are calculated based
on the corresponding failure rates of topside systems, being
adjusted accorded to previous steps. The expression that relates
failure rate from topside and subsea systems is presented in
equation [8].
𝜆𝑖(𝑆)= 𝜆𝑖
(𝑇)× (1 + 𝜅𝑖) [8]
For 𝑖 = 1,2, … , 𝑞 , where 𝜅𝑖 > −1 is the scaling factor, that
depends on the weights 𝜔𝑗𝑖(𝑆)
. This parameter, 𝜔𝑗𝑖(𝑆)
can be
interpreted as conditional probability: if 𝐹𝑀𝑖 has occurred, 𝐹𝐶𝑗
has also occurred, because is one of its causes, which means:
𝜔𝑗𝑖(𝑆)= Pr (𝑡ℎ𝑒 𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑖𝑠 𝑐𝑎𝑢𝑠𝑒𝑑 𝑏𝑦 𝐹𝐶𝑗𝑖|𝐹𝑀𝑖ℎ𝑎𝑠 𝑜𝑐𝑐𝑢𝑟𝑒𝑑)
[9]
The scaling factor is also dependent in how much different FC
affect FM of the subsea systems, compared to topside systems.
This can be calculated as weighted average of scores of RIFs
that influence 𝐹𝐶𝑗. The RIFs are also weighed according to its
importance, as shown in equation [10]:
𝜂�̅� = ∑ 𝜀𝑘𝑗𝜈𝑘𝑗(𝑆) 𝜂𝑘𝑗
3
𝑝𝑘=1 [10]
For 𝑗 = 1,2, … , 𝑟 . The division by three is because 3 is the
highest score from Table 2, and is used for normalization. The
scale factor 𝜅𝑖 is then calculated by 11]:
𝜅𝑖 = 𝑐𝑖 × ∑ 𝜔𝑗𝑖(𝑆)𝑟
𝑗=1 × 𝜂�̅� [11]
For 𝑖 = 1,2, … , 𝑞. 𝑐𝑖 is a constant scaling factor, that is specified
later.
It is specified that the failure rate is delimited such as: 𝜆𝑖(𝑆)∈
[ 𝜆𝐿𝑜𝑤,𝑖(𝑆)
, 𝜆𝐻𝑖𝑔ℎ,𝑖(𝑆)
]. These boundary values are found based on
5
𝜆𝑖(𝑇)
, and two factors: 𝜃𝑚𝑖𝑛,𝑖 and 𝜃𝑚𝑎𝑧,𝑖, that are determined by
expert judgment, such as:
𝜃𝑚𝑖𝑛,𝑖 × 𝜆𝑖(𝑇)≤ 𝜆𝑖
(𝑆)≤ 𝜃𝑚𝑎𝑥,𝑖 × 𝜆𝑖
(𝑇) [12]
Combining equations [8], 11] and [12], it is obtained:
𝜃𝑚𝑖𝑛,𝑖 ≤ 1 + 𝑐𝑖 ∑ 𝜔𝐽𝐼(𝑆)𝑟
𝑗=1 × 𝜂�̅� ≤ 𝜃𝑚𝑎𝑥,𝑖 [13]
The values of 𝜔𝐽𝐼(𝑆)
and 𝜂�̅� were determined previously, so 𝑐𝑖 is
found as a function of 𝜃𝑚𝑖𝑛,𝑖 and 𝜃𝑚𝑎𝑥,𝑖.
Considering extreme cases of equation [13], where all the
scores of RIFs are given as +3 and -3, the values of 𝜂�̅� would be
1 and -1, respectively. It is figured out that in the maximum and
minimum cases 𝑐𝑖 = 1 − 𝜃𝑚𝑖𝑛,𝑖 and in the maximum case 𝑐𝑖 =
𝜃𝑚𝑎𝑥,𝑖 − 1 . It is then obtained:
𝑐𝑖 =
{
1 − 𝜃𝑚𝑖𝑛,𝑖 𝑤ℎ𝑒𝑛 ∑ 𝜔𝑗𝑖
(𝑆)× 𝜂�̅� < 0
𝑟𝑗=1
0 𝑤ℎ𝑒𝑛 ∑ 𝜔𝑗𝑖(𝑆)× 𝜂�̅� < 0𝑟
𝑗=1
𝜃𝑚𝑎𝑥,𝑖 − 1 𝑤ℎ𝑒𝑛 ∑ 𝜔𝑗𝑖(𝑆)× 𝜂�̅� < 0𝑟
𝑗=1
[14]
The equation [9] becomes:
𝜆𝑖(𝑆)= 𝜆𝑖
(𝑇)× (1 + 𝑐𝑖 × ∑ 𝜔𝑗𝑖
(𝑆)× 𝜂�̅�
𝑟𝑗=1 ) [15]
For the subsea failure modes that has no relation with topside
systems, the failure rate cannot be obtained from previous
equations, which implies that the failure rates must be
determined according to expert judgments, technical reports,
and operational data from similar systems.
Step 8: Determination of failure rates of new failure modes,
calculation of new total failure rate
The total failure rate for the subsea system is calculated by
equation [16]. The authors states that even when the failure
modes are not completely independent, this equation has
enough accuracy.
𝜆𝑇𝑜𝑡𝑎𝑙(𝑆)
= ∑ 𝜆𝑖(𝑆)𝑛
𝑖=1 [16]
4 CASE STUDY
To exemplify the presented methodology, a field in Brazil is
analyzed in terms of reliability and risk of its main equipment.
The field is located in approximately 300 km from the coast of
Rio de Janeiro, with an area of 533 km². The water depth varies
from 2.200 to 2.300 m. According to Teixeira et al. (2018), the
productivity index of each well is 8.000 bbl/day, with a time
production of 27 years. This study describes the differences
between separation systems in the conventional and hybrid
production systems scenarios.
In the conventional scenario there are 66 production wells, 22
water injection wells, and 4 gas injection wells. The wells are
divided in 12 clusters, and for each 3 clusters there is one FPSO,
plus the subsea systems, composed by manifolds, flowlines,
rises, Christmas tree, etc. In Figure 3 the conventional scenario
is represented. In the FPSOs there are separation systems, and
the oil is separated and stored in the tanks, while water and gas
are treated and reinjected in the reservoir. Table 4 shows the
division of wells for each FPSO.
The hybrid scenario, shown in Figure 4, has two FPSOs and six
subsea systems, and differently from the conventional, the
subsea systems are composed also of subsea separators. There
Figure 3 - Conventional production system layout, adapted from
Teixeira et al. (2018)
Table 4 - Division of production and injection wells for each
FPSO in conventional, adapted from Souza et al. (2017)
FPSO - A
Cluster A - 1 4 Prod. Well
FPSO - C
Cluster A - 1 6 Prod. Well
Cluster A - 2 4 Prod. Well Cluster A - 2 6 Prod. Well
Cluster A - 3 4 Prod. Well Cluster A - 3 6 Prod. Well
4 Water Inj. Well 6 Water Inj. Well
1 Gas Inj. Well 1 Gas Inj. Well
FPSO - B
Cluster A - 1 6 Prod. Well
FPSO - D
Cluster A - 1 6 Prod. Well
Cluster A - 2 6 Prod. Well Cluster A - 2 6 Prod. Well
Cluster A - 3 6 Prod. Well Cluster A - 3 6 Prod. Well
6 Water Inj. Well 6 Water Inj. Well
1 Gas Inj. Well 1 Gas Inj. Well
are also 66 production wells and 22 water injection wells, but
only three gas injection wells.
Each two clusters are connected by a tri-phase subsea separator,
that separates most of the water produced from the wells to
posteriorly reinject in the reservoir. The hydrocarbon fluid (with
some left-over water) is sent to FPSO to be separated in water,
gas and oil. The water and gas are reinjected, and the oil is stored
in tanks. There is an additional subsea system to reinject the
water, composed by three risers, one manifold and three water
reinjection wells associated with each FPSO. This system also
has one riser and one gas injection well associated with FPSO
1, and two risers and two gas injection wells associated with
FPSO 2. The division of wells is presented in Table 5.
The wells are not put in operation simultaneously, they are
placed in operation as the percentage of cut water increases
along the years. Table 5 also presents the year that each well
starts its operation.
The main difference between the scenarios is the use of subsea
separators to separate the water from the fluid and promptly
reinject the water into the water injection wells. According to
6
Figure 4 – Hybrid production system layout, adapted from Souza
et al. (2017)
Table 5 - Division of production and injection wells for each
FPSO in hybrid scenario, adapted from Souza et al. (2017)
FPS
O 1
SS-
1
Clust
er
A - 1
4 Prod.
Well
Year
1
FPS
O 2
SS-
4
Clust
er
B - 1
6 Prod.
Well
Year
1
Clust
er
A - 2
4 Prod.
Well
Year
1
Clust
er
B - 2
6 Prod.
Well
Year
1
SS-
2
Clust
er
A - 3
4 Prod.
Well
Year
1 SS-
5
Clust
er
B - 3
6 Prod.
Well
Year
1
Clust
er
A - 4
6 Prod.
Well
Year
2
Clust
er
B - 4
6 Prod.
Well
Year
5
SS-
3
Clust
er
A - 5
6 Prod.
Well
Year
6 SS-
6
Clust
er
B - 5
6 Prod.
Well
Year
9
Clust
er
A - 6
6 Prod.
Well
Year
10
Clust
er
B - 6
6 Prod.
Well
Year
13
3 Water rein. Well 3 Water reinj. Well
1 Gas Inj. Well 2 Gas Inj. Well
Souza et al. (2017), in the conventional scenario due to the
processing capacity of each FPSO, each one would endure 18
production wells, and as the production declines along the years,
there will be a moment that the FPSO would produce only
water, which characterizes the end of production life. As in the
hybrid concept most of the water is separated at subsea, there is
more capacity in the FPSOs for oil treatment and storage, and
therefore only two FPSOs are necessary.
The benefits of subsea separation of water are not only related
to FPSO capacity. Bai and Bai (2010) mention that the removal
of water helps to avoid hydrate formation in the export
pipelines, increases the recovery of reserves, and accelerates the
lifting of fluid stream making it lighter and easier.
4.1 Risk Analysis Conventional Scenario
System Definition
The conventional scenario, as mentioned before, is composed
by manifolds, lifting pumps, pipelines and risers. The oil
mixture from each well is gathered for the correspondent
manifold, and then lifted by pumps trough flowlines and risers
to the respective FPSO. Manifolds, as being a complex set of
valves are not considered for calculations of risk and reliability
analysis. The equipment considered are lifting pump, flowlines
and risers.
Information from pump is taken from Rahimi and Rausand
(2013), that applied the reliability prediction procedure based
on RIFs methodology for a subsea pump. The multi-stage pump
is composed by impellers placed in series, and the pump system
is composed by the pump and an electric motor. Considerations
are that the system must have high reliability (each component
of pump); maintenance plan is not standard; and the properties
of pump fluid may change over time.
The diameter and extension of flowlines and riser are presented
in Table 6, being considered from manifold to the FPSO.
Flowlines are considered the extension of pipeline positioned in
the seabed until TDP, while riser is the extension from TDP
until the FPSO. All pipelines and risers are flexible.
Table 6 - Extension of flowlines and risers of conventional
scenario, adapted from Teixeira et al. (2018)
FPSO Cluster Diameter [in] Length [km] Manifold -
TDP
Length [km]
Riser
A A1 8 9.3 2.3
A2 8 1.1 2.3
A3 8 9.3 2.3
B1 B1 8 2.8 2.3
B3 8 2.8 2.3
B3 8 9.5 2.3
B2 B4 8 2.9 2.3
B5 8 2.9 2.3
B6 8 5.6 2.3
C C1 8 4.7 2.3
C2 8 1.1 2.3
C3 8 4.7 2.3
Hazard Identification
Normally the process of identification of hazards and FMEA is
performed by a specialized team, bringing together
professionals with expertise to conduct a brainstorming and
identify particularities of the system being analyzed. In the
present study the FMEA is carried out by the author, based on
information taken from specialized literature.
The failures modes used by Rahimi and Rausand (2013)
adopted for application of methodology are presented in Table
7. Only the most important failure modes are considered in the
calculations.
Table 7 - Failure modes of a pump, from Rahimi and Rausand
(2013)
FTS Fail to start on demand
LOO Low output
UST Spurious stop
Regarding flowlines and risers, information is based on DNV
(2011). Table 8 presents failure modes considered for flowlines
and risers.
Table 8 - Failure modes of a pipelines and risers, adapted from
DNV (2011)
BUR Bursting
FAT Fatigue
COL Collapse
PRB Propagating Buckling
7
Evaluation of failure rates and failure probabilities
Pump
Failure rate data for subsea pumps were taken from Rahimi and
Rausand (2013), and values are presented in Table 9.
Table 9 - Failure rate for pump failure modes, from Rahimi and
Rausand (2013)
FM FTS LOO UST
𝜆𝑖 [106 ℎ−1] 42.07 83.5 103.86
Failure rate presented in Table 20 are for an operational time of
106 hours. The total failure rate for each subsea pump is
presented below, for operational time and for a year,
respectively 𝜆(ℎ−1) = 2.29 × 10−4. The probability of failure
of a pump in a year is: 𝐹(1 𝑦𝑒𝑎𝑟) = 8.66 × 10−1
Flowline and Risers
The failure frequencies of flowline and risers are calculated
based on DNV (2009), and results are presented in Table 10. As
failure frequency of risers does not have influence of length, the
value is the same for all.
Table 10 – Probability of failure of flowlines and risers for
each cluster in conventional scenario
FPSO Cluster Diameter [in] Length [km]
Manifold - TDP
Probability of
Failure Flowline
Probability of
Failure Riser
A A1 8 9.3 2.14E-03 4.50E-03
A2 8 1.1 2.53E-04 4.50E-03
A3 8 9.3 2.14E-03 4.50E-03
B1 B1 8 2.8 6.44E-04 4.50E-03
B3 8 2.8 6.44E-04 4.50E-03
B3 8 9.5 2.19E-03 4.50E-03
B2 B4 8 2.9 6.67E-04 4.50E-03
B5 8 2.9 6.67E-04 4.50E-03
B6 8 5.6 1.29E-03 4.50E-03
C C1 8 4.7 1.08E-03 4.50E-03
C2 8 1.1 2.53E-04 4.50E-03
C3 8 4.7 1.08E-03 4.50E-03
Total System
Considering the complete system as a series system, the total
probability of failure of each cluster can be obtained and Table
11 presents the results of each cluster.
Table 11 - Failure frequency of cluster for conventional scenario
FPSO Cluster Probability of Failure Cluster
A A1 8.67E-01
A2 8.67E-01
A3 8.67E-01
B B1 8.67E-01
B2 8.67E-01
B3 8.67E-01
C B4 8.67E-01
B5 8.67E-01
B6 8.67E-01
D C1 8.67E-01
C2 8.67E-01
C3 8.67E-01
Risk Matrix
Using the scales of frequency and consequence, the risk for each
cluster from FPSO A is presented in Table 12.
Table 12 - Risk for clusters and FPSO A, of conventional
scenario
Cluster Probability of
failure Flowline
Probability of
failure Riser
Probability of
failure Pump
Probability of
failure Cluster
A1 2.14E-03 4.50E-03 8.66E-01 8.67E-01
Frequency 3 4 5 5
Severity 4 4 1 3
Risk 12 12 5 15
A2 2.53E-04 4.50E-03 8.66E-01 8.67E-01
Frequency 2 3 5 5
Severity 4 4 1 3
Risk 8 12 5 15
A3 2.14E-03 4.50E-03 8.66E-01 8.67E-01
Frequency 3 3 5 5
Severity 4 5 1 3
Risk 12 12 5 15
The analysis has considered that the pumps normally have
safety valves, which protect the system from leakage when a fail
occurs. For this reason, it was stipulated a severity rating of 1
(slightly effects), in all cases. Regarding flowlines and risers, a
fail means an oil spill, which induces to pollution. The
consideration is that even if the leakage is small, at least the
severity would be significant, with major effects, 4.
chemical injection.
4.2 Risk Analysis of Hybrid Scenario
In the hybrid scenario, the number of components increases due
to addition of subsea separation equipment and water injection
system, such as valves, pumps and the separator itself.
System Definition
According to Bai and Bai (2010), the concept of having water
separation and injection on the seafloor requires as basic
components a separator, a pump to reinject the water and a water
injector well. Other more specific components are
instrumentations, equipment for control the pump, separator and
valves, power transmission/distribution equipment, and
A separator is a pressure vessel that has the function to separate
fluid. In the shipping sector, for example, its application is
mandatory in all vessels, to separate impurities from fuel before
the fuel is injected in machinery, and sludge from water before
the water is dumped into the sea. In ships, the separators can be
gravitational or centrifugal, depending on application. In the
offshore field, there are many types of separators used to
separate the well spread varying according to the type of fluid
coming from the well. A separator from offshore industry
separates well fluids coming from a well or a group of wells into
gaseous and liquid components. A three-phase separator is
capable of separating the gas from liquid, and water from oil,
(Laik 2018).
For this project a simplified system is assumed. Equipment to
be considered in the risk analysis are: pumps, separator,
flowline and risers. Even the lifting and injection pumps being
conceptually different, they are considered to be identical and
their failure characteristics are taken from Rahimi and Rausand
(2013).
The diameter and length of flowlines and risers are presented in
Table 13, being considered from manifold to FPSO. Flowlines
are considered the from the manifold to the separator, and from
8
the separator to TDP, while risers are from the TDP to the
FPSO. The pipelines from the production well to the manifold,
and from separator to the reinjection well are excluded from the
analysis. Also, in this scenario, all pipelines are considered to
be flexible.
Table 13 - Extension of flowlines and risers of hybrid scenario,
adapted from Teixeira et al. (2018)
FPSO Cluster Separator Diameter
[in]
Length [km]
Manifold -
Separator
Length [km]
Separator -
TDP
Length
[km]
Riser
1
A1 1
8 5.01 14.33 2.3
A2 8 5.01
A3 2
8 3.74 4.17 2.3
B2 8 3.74
B1 3
8 3.29 10.58 2.3
B6 8 3.29
2
B3 4
8 3.06 3.3 2.3
C1 8 3.06
B4 5
8 3.67 8.47 2.3
B5 8 3.67
C2 6
8 2.62 9.04 2.3
C3 8 2.62
Hazard Identification
The identification of hazards of a separator is performed
according to information available in SINTEF (2002) for similar
topside separator vessels. The boundary considered in the
analysis is shown in Figure 5. The figure indicates that inlet,
pressure relief, outlet and drain valves are not considered
in the analysis of the separator. Table 14 presents the critical
failure modes of the separator.
Figure 5 - Vessel boundary definition, adapted from SINTEF
(2002)
Table 14 – Critical failures modes of a vessel separator, adapted
from SINTEF (2002)
AIR Abnormal instrument reading
ELP External leakage - process medium
OTH Other
PDE Parameter Deviation
PLU Plugged/Chocked
Regarding pumps, flowlines and risers, hazards are considered
the same as conventional scenario.
Evaluation of failure rates and failure probabilities
Subsea separator
The probability of failure of the subsea separator is calculated
by applying the failure rate prediction method proposed by
Rahimi and Rausand (2013) described in previous section, that
consists in the following steps:
New system familiarization
A system familiarization was already made in section, 0. The
main function of the subsea separator is to separate water from
the well spread. It must have high reliability, due to the
difficulty in performing corrective maintenance in water depth
of 2200 m.
Identification of failure modes and failure causes
Failure modes and failure causes are selected from SINTEF
(2002). Failure modes are considered only for critical failures,
and failure causes are selected according to higher frequency of
occurrence. For simplification purposes, it is selected the failure
causes with higher frequency: instrument failure – general; out
of adjustment; blockage / plugged; faulty signal / indication /
alarm; mechanical failure – general; no signal / indication /
alarm; electrical failure – general; breakage; control failure; and
leakage.
Reliability information acquisition for the similar
known system
Failure rates for each failure mode are taken from SINTEF
(2002), and are presented in Table 15. Failure rates are
considered for an operational time of 106 hours.
Table 15 - Failure rates for the failure modes (topside separator)
𝑖 𝐹𝑀𝑖 𝑁𝑓𝑎𝑖𝑙𝑢𝑟𝑒𝑠 𝛼𝑖 𝜆𝑖 [ℎ−1] 𝛼𝑖 × 𝜆𝑖 [ℎ
−1]
1 AIR 23 0.39 17.62 6.87
2 ELP 21 0.36 16.09 5.73
3 OTH 7 0.12 5.36 0.64
4 PDE 3 0.05 2.3 0.12
5 PLU 5 0.08 3.83 0.32
The total failure rate of the topside separator is: 𝜆(𝑇)[ℎ−1] =13.67 × 10−6.
The same assumptions of Rahimi and Rausand (2013) are made:
failure modes and failure cause for subsea system are
considered similar to the topside system, with different effects.
This is an assumption with simplifications purposes, but ideally
both systems should be deeply detailed to obtain more reliable
results.
a) Selection of relevant RIFs
According to Table 1, the RIFs 𝑘 are selected according to
consideration of their importance to each failure cause 𝑗 .
Besides, weights 𝜀𝑘𝑗 of RIF 𝑘 are distributed equally for each
failure cause 𝑗 . The decision of how much each RIF 𝑘
influences in the failure cause must also be performed by
experts. In this study they were equally divided among all.
b) Scoring the effects of the RIFs
RIFs are categorized according to relevance to topside and
subsea system and scored according to their importance. The
scale for scoring the RIFs used is the same suggested by Rahimi
and Rausand (2013), presented in Table 2.
9
c) Weighing the contribution of the failure causes to
failure modes
The weight contribution of the failure causes to failure modes
are taken from data available in SINTEF (2002), in the section
Failure descriptor versus failure mode. The information is
normalized in order to be transformed in weights. The weights
are presented in Table 16.
It is important to notice that this data is a percentage of the total
failure rate for the failure description/failure mode combination,
and it takes in consideration all types of failures: critical,
degraded, incipient and unknown. This implies that the data do
not represent faithfully the weight contribution for critical
failures.
Table 16 - Weight contribution of failure modes
Failure Cause i=1 i=2 i=3 i=4 i=5
AIR ELP OTH PDE PLU
j=1 Instrument failure - general 0.36 0.00 0.15 0.33 0.07
j=2 Out of adjustment 0.36 0.00 0.21 0.42 0.02
j=3 Blockage/Plugged 0.10 0.03 0.15 0.09 0.80
j=4 Faulty signal / indication/alarm 0.09 0.00 0.03 0.00 0.00
j=5 Mechanical failure - general 0.03 0.28 0.15 0.06 0.07
j=6 No signal / indication / alarm 0.02 0.00 0.00 0.00 0.00
j=7 Electrical failure - general 0.01 0.00 0.03 0.00 0.00
j=8 Breakage 0.01 0.03 0.12 0.06 0.02
j=9 Control failure 0.01 0.00 0.06 0.03 0.02
j=10 Leakage 0.01 0.65 0.12 0.00 0.00
SUM 1.00 1.00 1.00 1.00 1.00
d) Determination of failure rate for similar failure modes
The failure rate of the failure modes is calculated according to
the factor intervals presented in Table 17. These factors must
also be result from a brainstorm of experts in the field.
Table 17 - Factor intervals for each failure mode
FM AIR ELP OTH PDE PDU
θ min, i 0.4 0.4 0.4 0.4 0.4
θ max, i 1.2 1.2 1.2 1.2 1.2
Results of failure rates of operational time (106 hours) for each
failure mode are shown in Table 18.
Table 18 - Failure rates of subsea separator failure modes
FM AIR ELP OTH PDE PDU
𝜆𝑚𝑖𝑛,𝑖 [ℎ−1] 7.05 6.44 2.14 0.92 1.53
𝜆𝑚𝑎𝑥,𝑖 [ℎ−1] 21.14 19.31 6.43 2.76 4.60
𝜆𝑎𝑣𝑒,𝑖 [ℎ−1] 14.10 12.87 4.29 1.84 3.06
e) Determination of failure rates of new failure modes,
calculation of new total failure rate
The final failure rate of the subsea separator calculated by
equation [ 31 ], is 𝜆[ ℎ−1](𝑆𝑆) = 3.62 × 10−5.
Pump
Failure rates for subsea pumps, for both lifting and injection
pump are the same calculated by Rahimi and Rausand (2013),
𝜆[ ℎ−1] = 2.29 × 10−4
Flowline and Risers
The probability of failure of flowline and risers are calculated,
and results are presented in Table 19.
Total hybrid system
Considering the complete system as a series system, the total
probability of failure can be obtained. Table 20 presents the
results of each group. Each group is composed by two clusters
and one separator.
Table 19 – Probability of failure of flowlines and risers for each
cluster in hybrid scenario
FPSO Cluster Diameter
[in]
Probability of
Failure Flowline
Manifold -
Separator
Probability of
Failure Flowline
Separator - TDP
Probability of
Failure Riser
1 A1 8 1.15E-03 3.30E-03 4.50E-03
A2 8 1.15E-03
A3 8 8.60E-04 9.59E-04 4.50E-03
B2 8 8.60E-04
B1 8 7.57E-04 2.43E-03 4.50E-03
B6 8 7.57E-04
2 B3 8 7.04E-04 7.59E-04 4.50E-03
C1 8 7.04E-04
B4 8 8.44E-04 1.95E-03 4.50E-03
B5 8 8.44E-04
C2 8 6.03E-04 2.08E-03 4.50E-03
C3 8 6.03E-04
Table 20 - Failure frequency of group and for hybrid scenario
FPSO Cluster Probability of Failure Group
1
A1 9.87E-01
A2
A3 9.87E-01
B2
B1 9.87E-01
B6
2
B3 9.87E-01
C1
B4 9.87E-01
B5
C2 9.87E-01
C3
Risk Matrix
Using the scales of frequency and consequence, the risk for each
group is presented in Table 21.
The hybrid scenario considered that separator and pumps have
safety valves that prevent the system from leakage when a
failure occurs, and the severity rating is 1. Flowlines from
manifold to separator have a severity of 3, because of the
possibility of leakage and consequently pollution. The flowlines
from separator to TDP and risers have higher severity, due to
higher flow capacity.
4.3 Comparison of scenarios
Both scenarios are influenced by the failure frequency of the
subsea pumps. The failure frequency of pumps in both scenarios
is also similar, even though the hybrid scenario having the
double of pumps than conventional scenario.
10
Despite the pumps, the conventional scenario presented higher
influence from risers when compared with the hybrid scenario.
Table 21 - Risk for clusters of FPSO 1, of hybrid scenario
Cluster
Probabi
lity of
failure
Flowlin
e
Manifol
d -
Separat
or
Probabi
lity of
failure
Separat
or
Probabi
lity of
failure
Lift
Pump
Probabi
lity of
failure
Injectio
n Pump
Probabi
lity of
failure
Flowlin
e
Separat
or -
TDP
Probabi
lity of
failure
Riser
Probabi
lity of
failure
Group
A1 1.15E-
03 2.71E-
01
8.66E-
01
8.66E-
01
3.30E-
03
4.50E-
03
9.87E-
01 A2
1.15E-
03
Freque
ncy 3 5 5 5 3 3 5
Severit
y 3 1 1 1 4 4 4
Risk 9 5 5 5 12 12 20
A3 8.60E-
04 2.71E-
01
8.66E-
01
8.66E-
01
9.59E-
04
4.50E-
03
9.87E-
01 B2
8.60E-
04
Freque
ncy 2 5 5 5 2 3 5
Severit
y 3 2 2 2 4 4 3
Risk 6 10 10 10 8 12 15
B1 7.57E-
04 2.71E-
01
8.66E-
01
8.66E-
01
2.43E-
03
4.50E-
03
9.87E-
01 B6
7.57E-
04
Freque
ncy 2 5 5 5 3 3 5
Severit
y 3 1 1 1 4 4 4
Risk 6 5 5 5 12 12 20
If all equipment in conventional scenario were considered, such
as separator system on the FPSO, pumps, pipelines for water
and gas injection, Christmas tree and wellheads, the total
probability of failure would be greater than that of the hybrid
scenario. This is expected because the number of FPSOs is
higher, so the number of separators would increase. Also, as this
scenario has more injection wells because of its
characterization, more risers, flowlines, injection pumps,
wellheads, Christmas tree would be necessary.
The hybrid scenario would have less equipment for separation
on the FPSO (considering less water in the fluid spread), and
also the number of injection wells is lower, so it would not be
necessary to implement the same number of equipment than
conventional scenario.
The probability of failure estimates for a subsea separator is
greater than for a topside separator. As for pumps, redundancy
for subsea separation was not considered.
5 CONCLUSION
The work presents a practical application of an approach to
predict the failure rate of a subsea separator using failure data
from a topside separator. The reliability prediction methodology
based on RIFs is a useful tool to calculate the reliability of
subsea equipment, when most of information available is from
topside equipment. The methodology must be applied by a team
with expertise in the field, to gather all the details that can
influence in subsea equipment reliability. Factors as
maintenance, material and environment can modify the
reliability when comparing topside with subsea equipment.
This tool can be used in the conceptual design phase, when not
enough information from layout and equipment is available and
for reliability and risk prediction. Despite not having final
information, all uncertainties and considerations must be
recorded.
In the case study developed, many simplifications and
considerations were assumed, such as exclusion of
redundancies in the system, exclusion of complex equipment
(Christmas tree, wellheads and manifolds), etc. The separator
and pumps were simplified to units, while in operations there is
a set of valves and sensors that must be coupled and should be
considered in reliability assessment. Also, functionalities of a
separator such as a test to verify if the content of water in the
separated oil is under the stipulated limit should be considered.
Simplifications made in the analysis could have a great
influence in the results of reliability.
Despite these simplifications and uncertainties, the reliability
prediction methodology has shown to be a useful tool for oil and
gas offshore industry, as it provides information to support risk
analysis with risk matrix and can help deciding the layout of
field development.
In the development of this project some suggestions for future
research works were identified, such as:
• Sensitivity analysis for failure modes and failure
causes used in the reliability prediction methodology,
and how much a RIF can influence in the subsea
equipment reliability;
• Validation of results, to assess whether the results from
the analysis are reliable or not;
• Use of more complex systems, considering details
from each component of the system;
• Financial analysis in the comparison between
conventional and hybrid scenarios, considering the
cost of equipment and what is the production level of
each option;
• Risk analysis considering financial losses and
environmental impacts.
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