Reliability analysis of subsea manifold system using FMECA and FFTA | Scientific Reports

Scientific Reports volume 14, Article number: 22873 (2024) Cite this article

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Subsea manifold system is a complex system that occupies a pivotal role in contemporary ocean engineering and has a significant impact on the safety of marine resource exploitation. Reliability technology plays a significant role in ensuring the safe operation of the subsea manifold system. To perform a comprehensive analysis of the reliability of complex systems, a combination method of FMECA-FFTA (Failure Modes, Effects and Criticality Analysis - Fuzzy Fault Tree Analysis) is introduced in this study. Firstly, FMECA is used to accomplish a qualitative analysis of system reliability considering multifactorial failure modes, which included analyzing potential failure modes, causes of system failure, and evaluating the degree of hazard to the system through a risk matrix diagram. Then, FFTA is applied to build a fault tree model to divide the system into “system-subsystem-component” and determine the minimal cut sets for quantitative analysis of system reliability. In addition, fuzzy set theory is incorporated to improve the accuracy of handling uncertainty in quantitative reliability analysis. Finally, a qualitative and quantitative reliability analysis is conducted by using FMECA-FFTA method for subsea manifold system. The failure modes of the subsea manifold system are clearly identified, including high-risk modes such as external leakage, medium-high-risk modes such as fail to close/lock on demand, and medium-risk modes such as leakage of critical location, plugged, and effective measures should be taken to focus on preventive protection and regular testing for the high risk, medium-high risk and medium risk modes.

With the increasing subsea oil and gas extraction and production activities around the world, the reliability and stability of subsea oil and gas production system has taken on critical significance1,2. As an important equipment for oil and gas development, subsea manifold system plays the role of hubs, responsible for centralizing the products from multiple wells to land3. Subsea manifold system works in extreme marine environments such as high pressure and low temperature, and may also be subject to risks such as falling objects at sea and impacts from fishing nets, leakage and corrosion4. Failures in offshore oil and gas production systems, such as leaks and explosions, have the potential to cause significant economic losses and environmental damage, with serious negative impacts on society. Many of these accidents have already resulted in serious economic losses and environmental degradation, which has increased concerns about the safety and reliability of equipment5,6. The subsea manifold system, as an important part of the subsea production system, plays a vital role in ensuring the normal exploitation of subsea oil and gas, and which is essential to the safety of the subsea production system. Therefore, it is necessary to analyze and study the reliability of subsea manifold system to provide effective measures and technical guidance on reliability for further preventing the occurrence of maritime accidents.

In the field of subsea oil and gas production, the application of reliability theory plays a crucial role in ensuring production continuity, reducing risk, improving safety and environmental protection, and enhancing economic efficiency7. The reliability theory and methodology help to fully understand system performance and potential risks, so that preventive measures can be taken to ensure that subsea oil and gas production systems operate stably and reliably in extreme marine environments. In the 21st century, reliability research tends to be integrated, systematic, synergistic and sophisticated8, and reliability assessment of complex systems has become a prominent issue9.

System reliability analysis is a multidisciplinary approach to assessing and ensuring the reliability of complex systems over their operational life, which includes a variety of methods and techniques for evaluating the likelihood of system failure, as well as strategies for mitigating the associated risks. Reliability analysis of subsea manifold system involves multiple methods, such as Failure Mode and Effects Analysis (FMEA)10,11, Fault Tree Analysis (FTA)12, Reliability Block Diagrams (RBD)13, and Monte Carlo Simulation14, etc. These methods differ in analysis levels, precision, and applicability, necessitating the selection of the most suitable method based on particular requirements. FMEA primarily identifies potential failure modes and assesses their impacts15, whereas FTA examines potential system failure paths by constructing a fault tree16. RBD establishes reliability models of systems, assisting in comprehending relationships among various system parts to evaluate the reliability of the overall system. Monte Carlo Simulation estimate reliability parameters by simulating system behavior under diverse scenarios, considering uncertainties, and offering probabilistic assessments of system performance17. Therefore, a comprehensive evaluation of subsea manifold system reliability necessitates employing a combination of diverse methods tailored to specific circumstances, providing enhanced guidance for system design and maintenance.

In this study, a composite reliability analysis method combining FMECA and Fuzzy Fault Tree Analysis (FFTA) is introduced to assess the reliability of the subsea manifold system, which also offers several advantages for analyzing the reliability of complex systems.

Complementary analysis: FMECA and FFTA provide complementary insights into different aspects of system reliability. FMECA focuses on failure modes and their causes, while FFTA emphasizes the aggregation of these failure modes to evaluate system-level reliability. By integrating these two approaches, a more comprehensive understanding of system reliability can be obtained.

Strengthen risk assessment: FMECA-FFTA provides a more accurate and comprehensive assessment of system risk; FMECA helps to identify critical failure modes and their impacts, while FFTA quantifies the probability of system failure, which provides a more reliable estimate of system risk and contributes to well-informed decisions on risk reduction measures.

Multi-level analysis: FMECA-FFTA allows the analysis to be broken down into multiple levels, from the component level to the system level, to better understand the sources and effects of the problem. Multi-level analyses help determine which failure modes or events have the greatest impact on the overall reliability of the system.

Comprehensive improvement strategies: FMECA-FFTA provides comprehensive improvement strategies, FMECA identifies the highest priority failure modes, while FFTA can provide recommendations on how to reduce the impact and probability of these modes. To assist in the development of targeted maintenance, repair and preventive measures to improve system reliability and performance.

In summary, the combination of FMECA and FFTA provides a solid framework for reliability analysis of complex systems. A comprehensive understanding of the failure modes, root causes, and their impact on overall system reliability allows for well-informed decision making, efficient resource allocation, and optimization of system design. Subsequently, FMECA-FFTA is conducted to analyze the failure modes, root causes of failures, and failed components. An in-depth qualitative and quantitative analysis of the severity of the failure modes of the subsea manifold system is conducted by using the most authoritative reliability database, Offshore Reliability Data (OREDA). The weak points of the system are clearly identified, providing a strong guide for the reliability analysis of subsea manifold systems.

The reliability analysis of subsea production systems is a critical aspect of ensuring safe and efficient operation in the oil and gas industry. Various studies have been conducted to assess the risk and reliability of different components within subsea production systems. Bhattacharyya et al.18 proposed a method for optimizing system cost and reliability using a fault tree representation, assisting system designers in selecting available basic events. Cheliyan et al.19 proposed a probabilistic failure analysis of oil and gas leakage in the subsea production system using fault tree analysis (FTA), thereby identifying the weakest links that may cause leakage. Mudrak et al.20 described subsea production systems, feasible maintenance strategies, and gives insight into reliability assessment. Deegan et al.21 introduced FMEA as applied to a remote subsea oil and gas production system located in the North Sea. Pang et al.22 proposed that a fault tree and a dynamic Bayesian model were used to assess reliability and safety and to consider the time slice-based characteristics of reliability, significantly reducing the risk of failure of oil extraction trees. Liu et al.23 proposed to model the risk occurrence time of each basic event assessed by an expert with uncertainty variables. Hu et al.24 conducted a risk analysis of oil and gas leakage in subsea production systems using a fuzzy fault tree approach. This study focused on identifying potential failure modes and assessing the associated risks in subsea production systems. Woo et al.25 introduced a simulation method for visualizing the system configuration of subsea production processes and simulating stable fluid flow. Choi et al.26 proposed the concept of subsea production systems with seabed storage tanks as an alternative to conventional floating facilities. Shafiee et al.27 developed an integrated FTA and FMEA model for risk analysis of safety-critical systems, including subsea blowout preventers. This model combined advanced techniques such as FTA and FMEA to assess the reliability of engineering systems. Wang et al.28 focused on the reliability and safety modeling of the electrical control system of subsea control modules using Markov and multiple beta factor models. In conclusion, the literature on reliability analysis of subsea production systems encompasses various methodologies to assess the risk of different components. Studies have focused on simulation methods, integrated models, and reliability modeling techniques to ensure the safe and efficient operation of subsea production systems.

The reliability analysis of subsea manifold systems is crucial in ensuring the safety and efficiency of offshore operations. Scholars have conducted fewer studies on the reliability analysis of subsea manifold system. The American Petroleum Institute (API) gave a recommended practice for technical and risk management of subsea manifold system reliability-API RP 17 N, which is a summary of international experience and research results on reliability and technical risk management of subsea manifold system. Chandima et al.29 classified the consequences of functional failure of the subsea manifold, mostly involving valves which have an influence on production and safety functions based on ‘fail close’ or ‘fail open’. Umofia et al.30 analyzed the subsea production systems containing a subsea pipe sink based on the RBD methodology, evaluated the failure rate and mean time to failure (MTTR) of the system, and the study found that the subsea pipe sink is a secondary weak link in the subsea production systems. Duan et al.31 conducted a reliability analysis of a clumped-well pipeline manifold, established a fault tree containing pipelines, control systems and flow safeguards, and carried out an importance analysis to identify the main causes of failure in the fault tree and preventive measures. Guan et al.32 proposed a reliability analysis of subsea pipe confluence structure based on fuzzy fault tree model, and the reliability of the subsea manifold fault tree is obtained by combining the fuzzy comprehensive evaluation method.

Though some scholars have analyzed the reliability of subsea manifold system, most of the assessments focus only on specific failure modes, and the reliability analysis methods used are relatively single. In general, the reliability analysis of the subsea manifold system is not sufficiently developed. The traditional reliability analysis method can only perform some qualitative analysis, and the traditional FTA method applied generally takes the probability of event failure as the exact value, and its results have a large variation from the actual condition. Therefore, a compound reliability analysis method combining FMECA and FFTA is presented in this study to analyze the reliability of subsea manifold system.

FMECA encompasses Failure Mode and Effects Analysis (FMEA) and Criticality Analysis (CA)33, which is a design analysis methodology utilized to enhance system reliability, identifying, evaluating, and managing potential failure modes in products or processes and assessing their impact on system performance. FMEA is an inductive analysis method that identifies the causes of each failure and categorize based on its hazard level, ease of detection, and frequency of occurrence34,35. The core principle of FMEA is to proactively identify issues, allowing for measures to mitigate risk and enhance reliability and quality CA, an extension of FMEA36,37, provides a comprehensive assessment of the impact on the system, considering the severity and frequency of different failure modes. The implementation of FMECA methodology usually consists of the following basic steps.

Problem definition: Clearly define the scope and objectives of the problem being analyzed, including the scope of the system, product, or process.

Identify failure modes: To identify potential failure modes, including potential problems within the system.

Evaluate the impact of failure: To evaluate the potential impact of each failure mode, considering factors such as safety risks and production interruptions.

Determine the cause of failure: The cause of failure associated with each failure mode is determined.

Calculate risk priority: The risk priority of each failure mode is determined using methods such as the risk priority index (RPN), considering the severity and frequency of the failure mode.

Establish improvement measures: Development of improvement programs for high-risk failure modes to reduce or eliminate associated risks.

Based on the fundamental steps of implementing the FMECA method, a flowchart depicting its operational steps is constructed, as depicted in Fig. 1.

FMECA analysis flow chart.

Criticality analysis (CA) is a comprehensive evaluation of the impact of each failure mode on the system based on the probability of occurrence, severity, and other relevant factors of each failure mode38.CA includes both qualitative and quantitative analyses, with qualitative analyses such as Hazard Matrix Analysis and Risk Prioritization Numbers39, and quantitative analyses such as failure mode hazard and product hazard analysis. In this study, the hazard degree is used to synthesize the severity of a failure mode and the probability of that failure mode occurring, and then the impact caused by each failure mode in the system is rated comprehensively, with the help of graphical tools (such as risk matrix diagrams) to assist in the analysis.

Hazard degree is the severity of damage or risk that something poses to a system, people, the environment or others under specific conditions. It is commonly used in risk assessment, safety analysis, and engineering and management. Hazard degree is usually determined by a combination of factors.

Severity: A measure of the degree to which an event or occurrence may cause harm. The higher the severity, the greater the degree of harm.

Likelihood: An assessment on the probability of something or an event occurring. If the probability of something or an event occurring is high, the level of harm may be high, even if its severity is relatively low.

The method of calculating the level of hazard varies according to the specific application. In the risk matrix, likelihood and severity are assigned different levels, which are multiplied together to calculate the level of hazard. The concept of degree of hazard helps organizations and professionals to understand and compare the relative importance of different risks and therefore to target measures to reduce them.

The risk matrix method is a simple and intuitive approach that helps organizations understand and manage various risks40. By visualizing and ranking risks, applications, organizations can allocate resources significantly better and take appropriate measures to manage and mitigate risks.

The risk matrix method is used to comprehensively identify the failure modes, and the failure severity evaluation criteria, incidence evaluation criteria, and risk matrix are firstly developed prior to the system analysis, as shown in Tables 1 and 2. On the basis of FMECA, the system is analyzed for multifactorial failure modes. The risk matrix method is applied to comprehensively identify the hazard level of the failure modes, and the failure modes are categorized into five categories: high risk (Class V risk), medium-high risk (Class IV risk), medium risk (Class III risk), medium-low risk (Class II risk), and low risk (Class I risk), as shown in Fig. 2.

Risk matrix table.

Fault tree analysis (FTA) is a reliability analysis method that uses fault trees as a graphical deductive logic reasoning method to decompose a system reliability analysis model from top to bottom41, aiming to identify the root causes leading to a specific top event. FTA is commonly used in risk assessment, system safety analysis and reliability analysis to help identify potential risks, improve system reliability and develop appropriate risk management strategies.

Structural function

Assuming that the n bottom events xi(i = 1, 2,…, n) leads to the top event are all independent of each other and are dimorphic, as shown in Eq. (1).

Definition: the structure function of the system is \(\phi {\text{(}}X{\text{)}}\), and the state {0, 1} of the top event is determined by some mapping of the bottom event state vector.

The structural functions of the system are described similarly to the bottom event, as shown in Eq. (2).

“Or” logic gate: any one of the bottom event occurs, then the top event of the logic gate occurs; “With” logic gate: when all the bottom event occurs, the top event of the logic gate occurs. According to the above definition, the structure function of the logic gate is shown in Eq. (3) and Eq. (4).

“Or” gate structure function:

“With” gate structure function:

Qualitative and quantitative analysis

The purpose of qualitative fault tree analysis is to analyze the occurrence pattern of faults and the impact of each bottom-level event on the top-level event based on the fault tree structure. Qualitative analysis generally involves solving for the minimal cut sets of the fault tree. The minimal cut sets mean that if all the bottom events are in the cut set, the top event will occur. Quantitative analysis is mainly included as fellow.

Determine the probability of occurrence of each bottom event through research;

The reliability of the top event is calculated by the logical relationship between the events of each layer;

Calculate the probabilistic importance of the occurrence of the bottom event.

The process of FTA is shown in Fig. 3. Firstly, the structural composition of the target system and the common failure modes of each component are analyzed, and reliability data are investigated; Then, deductive reasoning is carried out step by step downward, and the top event of the system failure is used as an intermediate event until the bottom event is identified and the fault tree model is established; Finally, the system is analyzed qualitatively and quantitatively, and the weak points of the system are analyzed.

FTA analysis flow chart.

The basic idea of fuzzy set theory is to blur the absolute affiliation of elements and sets contained in classical set theory42,43, the affiliation of the element x to the set A is fuzzy, which can be represented by any value in the interval of 0 ~ 1, and is represented by the Eq. (5).

in which, \({\mu _{\text{A}}}\) represents the strength of affiliation of x to set A.

\(L\left( x \right)\) is said to be a reference function of fuzzy numbers if \(L\left( x \right)\) satisfies the following conditions.

① \(L\left( x \right)=L\left( {-x} \right)\); ② \(L\left( 0 \right)=1\); ③ \(L\left( x \right)\) decreasing on \(\left[ {0,+\infty } \right]\) and continuous segment by segment.

Let the above \(L\left( x \right)\) and \(R\left( x \right)\) functions if satisfy the Eq. (6).

Then it is called L-R type fuzzy number, denoted as \(\tilde {A}={\left( {m,\alpha ,\beta } \right)_{LR}}\). Where the degree of affiliation \({\mu _{\text{A}}}\left( x \right) \in \left[ {0,1} \right]\), m denotes the mean value of \(\tilde {A}\), \(\alpha ,\beta\) denotes the left and right edges of the fuzzy interval, then\(\left( {\left. {m - \alpha } \right), \left( {m{\text{+}}\beta } \right.} \right)\) denotes the upper and lower bounds of the fuzzy interval, which can also be denoted as\(\left( {m - \alpha ,m,m{\text{+}}\beta } \right)\) is a constant when .\(\alpha ,\beta\). is zero.

L-R type fuzzy numbers are commonly normal, triangular and pointed, as shown in Eq. (7) ~ Eq. (9).

Reference function for normal fuzzy numbers:

Reference function for triangular fuzzy numbers:

Reference functions for pointed fuzzy numbers:

FFTA is different from the traditional fault tree in that it expresses the bottom event failure probability as a fuzzy number and replaces the traditional logic gate operator with a fuzzy gate operator to solve the top event failure probability, and the fuzzy operator expression for the logic gates is shown in Eq. (10) ~ Eq. (13).

“With gate” fuzzy operator:

in which, \(m_{{s_{i} }} ,\alpha _{{s_{i} }} ,\beta _{{s_{i} }} \left( {i = 1,2,...,n} \right)\) is given in Eq. (11).

“Or gate” fuzzy operator:

Or write it in the following recursive form:

in which, \({m_{{s_i}}},{\alpha _{{s_i}}},{\beta _{{s_i}}}\) is respectively:

Triangular fuzzy function has the advantages of easy expression and simple arithmetic and has been most widely used, therefore, this paper also adopts the triangular fuzzy function, the corresponding function expression is shown in Eq. (14), and the graph of the affiliation function is shown in Fig. 4.

Schematic diagram of the triangular affiliation function.

Let \({\text{A}} \in F\left( U \right),\lambda \in \left[ {0,1} \right]\), noting \({A_\lambda }=\left\{ {u\left| {u \in U,A\left( u \right) \geqslant \lambda } \right.} \right\}\), then \({A_\lambda }\) is said to be an \(\lambda\)-intercept set of A. \(\lambda\) is called the confidence level.

If a triangular fuzzy function \(A=\left[ {\left( {m - \alpha ,m,m{\text{+}}\beta } \right)} \right]\), then its \(\lambda\)-intercept set in Eq. (15).

Let \(\tilde {A},\tilde {B}\) be triangular fuzzy numbers, according to the classical dilation principle, for any \(\lambda \in [0,1]\), as shown in Eq. (16) ~ Eq. (19).

Addition

Subtraction

Multiplication

Division

In the process of FFTA, the fuzzy number \({\tilde {F}_i}\) is used to describe the probability of occurrence of the bottom event, and the fuzzy number of the probability of occurrence of the top event is obtained through quantitative analysis. In this study, the triangular fuzzy number is used to represent the probability of bottom event occurrence, and the intercept set of the probability of bottom event occurrence \({\tilde {F}_i}\) is obtained shown in Eq. (20).

In FFTA, the logic-gate fuzzy operator for the bottom event containing the probability of failure of the \(\lambda\) truncated set is computed as shown in Eq. (21) ~ Eq. (22).

“With gate” structure:

“Or gate” structure:

The functional relationship between reliability \(R\left( t \right)\) and failure rate \(\lambda \left( t \right)\) is shown in Eq. (23).

When \(\lambda \left( t \right)\) is a fixed value, and the system operating time t\(\to\) 1, then \(R\left( t \right)\)can be approximated in Eq. (24).

in which, \(\lambda ={\tilde {F}_T}\) denotes the failure rate of the top event T.

The probabilistic importance is the trend of the bottom event failure rate relative to the top event failure rate, which best reflects the importance of the bottom event relative to the top event in the fault tree43, and can be expressed by the Eq. (25).

in which, is top event failure rate. is the probability that the i-th bottom event occurs.

In this study, the triangular fuzzy number is used to represent the failure rate of the bottom event, and the system fault tree logic gates are all “or gate”, then the probabilistic importance of the bottom event is jointly solved by the following Eq. (26) ~ Eq. (28).

FMECA (Failure Modes, Effects and Criticality Analysis) and FFTA (Fuzzy Fault Tree Analysis) are two different reliability and safety analysis methods that can be used in conjunction to more fully assess the reliability and risk of a system, process or product. This combined analysis approach provides insight into potential failure modes, causes and effects to develop more effective prevention and countermeasures. The steps of the combined FMECA-FFTA method:

FMECA analysis phase: The first step is to identify the potential failure modes and corresponding effects of the system, process. This includes determining the likelihood, severity, and detectability of each failure mode.

FFTA analysis phase: Based on the results of the FMEA, select critical potential failure modes that could cause serious safety issues or system failures. FFTA is created to show the root causes and causal relationships of these critical failure modes. Use logic gates to represent these causal relationships. For events in the FFTA, introduce probability values to represent their probability of occurrence.

Combined analysis phase: The results of the FMECA and FFTA are combined to assess the reliability and risk of the system.

Based on the basic steps of the implementation of the combined FMECA-FFTA method, a flowchart of its operational steps is constructed, as shown in Fig. 5.

FMECA-FFTA analysis flow chart.

FMECA-FFTA helps to provide insight into potential failure causes, risk propagation paths, and risk control measures, and helps to improve the reliability and safety of a system or product and reduce the level of risk.

As the mainstream mode of offshore oil and gas development, the subsea oil and gas production systems refers to a series of equipment installed on the seabed to carry out oil and gas extraction operations. Subsea oil and gas production systems has many advantages such as saving extraction cost, large oil and gas delivery, continuous and fast oil and gas delivery, and improving oil and gas extraction efficiency, therefore, it has attracted the attention of domestic and foreign major oil companies. Hierarchical definition and division of the subsea oil and gas production systems: the system mainly consists of five subsystems—subsea control system, riser system, subsea pipeline system, subsea manifold system, subsea oil wellhead and the Christmas tree system. Based on the functions and interactions between the different systems, a typical model of the subsea oil and gas production system is shown in Fig. 6.

A typical model of the subsea oil and gas production system.

In Fig. 6, the light blue line segments indicate that the oil is injected from the subsea control system to the topside umbilical cable terminals (TUTA), flowing through the dynamic umbilical cables, the subsea umbilical cable terminals (SUTA), and arriving through the static umbilical cables to the subsea distribution unit (SDM) for distribution to the various stages of the production oil well and manifold system. The black lines indicate the oil, gas, water pipelines, and water injection pipelines. The subsystems interact through terminal interfaces to transport extracted oil, gas, and water multiphase fluids from the seafloor to the offshore platform for processing.

The subsea manifold system is an integrated system consisting of a tubing header and multiple branch pipelines, it mainly comprises two main parts: the pipeline exchange module and the pipe clearing module. The main roles and functions of subsea manifold system can be included as fellow.

Optimizing the layout of subsea facilities to reduce pipeline usage.

Pooling and controlling the products of well output and injecting seawater into injection wells to maintain formation pressure.

Delivering gas to the wells and injecting chemicals into them.

Providing a support platform to maintain and protect valve and pipeline operations.

According to the basic structure and functional role of the subsea manifold subsystem, a functional structure block diagram is constructed, as shown in Fig. 7.

Schematic diagram of subsea manifold system.

Manifold Module

As the core part of the subsea manifold system, the manifold module mainly includes the following components:

Pipelines: The manifold module comprises inlet and outlet pipelines designed to connect the flow of oil and gas from different wellheads. These pipelines interconnect with the remainder of the module by valves and connectors.

Valves: Valves are employed to control and regulate the flow, direction, and distribution of oil and gas. They allow the operator to remotely control fluid flow within the system.

Connectors: Connectors are utilized to link the manifold module with other subsea equipment, such as the subsea distribution module, the subsea control system, and the pipe cleaning module, ensuring the coordinated operation of the entire system.

Pigging module

The pigging module mainly includes pigging launcher and pigging receiver.

Pigging launcher: Equipment used to remove deposits, dirt, or other impurities from the interior of a pipeline or manifold.

Pigging receiver: Equipment used to receive and process sediment, dirt, or other impurities flushed out by a pipe-cleaning emitter or other cleaning equipment.

The data mainly comes from the latest release of foreign OREDA (2015) published by DNV in this study44, these data have been collected through the practice of a large number of subsea oil and gas production systems in the North Sea, the Gulf of Mexico and other regions of the reliability of the system45, which has an important reference value. According to the relevant statistics, the potential failure modes of subsea manifold system are shown in Table 3; Fig. 8, and the FMECA of subsea manifold system is shown in Table 4.

Failure statistics of subsea manifold system45.

According to the steps of FMECA analysis on the subsea manifold system for multi-factor failure mode analysis, the use of risk matrix method to comprehensively analyze and identify the hazard degree of the failure mode, the failure mode is divided into five levels of high-risk, medium-high wind, medium-wind, medium-low risk and low-risk, to complete the system’s FMECA analysis, refer to Table 4, it can be concluded that: there are a total of 17 failure modes, 72 failure causes in subsea manifold system. External leakage (production medium) belongs to high-risk failure mode, and inability to close/lock belongs to medium-high risk level, effective measures should be taken to strengthen prevention. External leakage (working medium), failure to open/unlock, leakage at critical locations, and blockage are of medium risk.

Based on the analysis results of the FMECA table for subsea manifold system, the severity of the failure modes on the final impact of the system is determined by the “Hazard level” item in the table, and the failure modes with a higher severity are selected as the objects of key attention. Failure modes that need to be emphasized in the subsea manifold system include external leakage (production media), failure to close/lock, external leakage (working media), failure to open/unlock, leakage at critical locations, blockage, etc.

In this study, the top event of the fault tree is selected as “subsea manifold system failure”. Then, the subsea manifold system is stratified according to the model of “system level-subsystem level-device level-component level”, as shown in Fig. 9.

Subsea manifold system stratification.

According to the above division structure block diagram, system-level faults are identified as top-level event T, subsystem and equipment-level faults are identified as intermediate events (denoted by E and Y), and component-level faults are identified as bottom-level event X. The system fault tree is constructed step by step from top to bottom. The event code list is shown in Table 5 and the fault tree model is shown in Figs. 10 and 11.

The fault tree branch of subsea manifold system.

The fault tree branch of subsea manifold system.

The qualitative analysis of the subsea manifold system fault tree is mainly focused on finding the minimal cut sets and identifying all the basic events that lead to the occurrence of the top event. According to the fault tree rules, the minimal cut sets of the subsea manifold system fault tree is shown in Eq. (29).

Failure data mainly from the latest version of OREDA reliability data manual, combined with the actual field research situation to carry out failure data statistics can be obtained from the bottom event failure mean value m. The introduction of the triangular fuzzy set theory will be the failure probability of each bottom event fuzzy, with a fuzzy subset of the bottom event failure rate to describe the hypothetical fuzzy triangular function \(\tilde{F}_{i}\) is symmetric with each other, and the failure mean value of -50% of the points x is 0.3, and the failure mean value of + 50% of the points x affiliation degree is 0.2, then there is a subordination degree of the point x which is -50% different from the failure mean is 0.3, and the subordination degree of the point x which is + 50% different from the failure mean \({m_i}\) is 0.2, as shown in Eq. (30).

Solving the above equations:, according to this relationship equation can be solved for the upper and lower values of the probability of failure of the basic event, which are summarized in Table 6.

In this study, the traditional logic gate operator is replaced by fuzzy gate operator in performing FTA, and the fuzzy operator for each bottom event can be found according to Eq. (31).

According to the above structure functions, and using the programming solution of Eq. (32).

When λ = 1, the bottom event failure rate is constant and the failure probability and reliability of T.

When λ = 0, the bottom event failure rate is a fuzzy interval value, the failure probability and reliability of T.

The algorithm is programmed using MATLAB for Eq. (25) ~ Eq. (28) to find the average of the bottom event probability significance and the results are imported into Origin for plotting as shown in Fig. 12.

Bottom event probability importance.

In Fig. 12, the values with higher probability of importance of the underlying events can be extracted and sorted in order of magnitude: X12 > X4 > X14 > X11 > X8 > X17 > X16 > X5 > X13 > X18 > X7 > X6. To facilitate the identification of system weaknesses, the probabilistic importance of the components is sorted out according to the sub-systems, and it can be concluded that the failure of components such as manifold module control valve, manifold module insulation device, subsea pipe cleaner, manifold module blocking valve, manifold module production medium isolation valve, manifold clearing module support structure, pipe module protection structure and other components is relatively the weak link of the subsea manifold system. To address weak links, measures should be taken to focus on defense and protection and regular testing to prevent and reduce production safety accidents and promote the orderly work of the subsea oil and gas production system.

In this study, a system reliability analysis method FMECA-FFTA is introduced. Firstly, the basic components and functions of the subsea manifold system are described, and a functional structure block diagram of the system is established. Then, the FMECA-FFTA is used to qualitatively analyze the reliability to identify the potential failure modes and causes of the subsea manifold system, and the risk matrix is applied to classify the failure modes into five levels: high risk, medium-high risk, medium risk, medium-low risk and low risk. A total of 17 main failure modes are identified, including high-risk modes such as external leakage (process medium), medium-high-risk modes such as fail to close/lock on demand, and medium-risk modes such as external leakage (utility medium), leakage of critical location, plugged. Subsequently, an agreed hierarchy of the subsea manifold system is divided, and the system fault tree model is built to find the minimal cut sets of the fault tree. The fuzzy set theory is introduced to quantitatively analyze the subsea manifold system, calculate the system failure probability, and find out the relative weakness of the system. Finally, by solving the bottom event probability importance to find out the weak links in each subsystem, the analysis results show that the manifold module control valves, manifold module insulators, pipe cleaners, manifold module blocking valves, manifold module production media isolation valves, manifold cleaner module support structures, and pipe module protection structures are relatively weak points of the subsea manifold system. Measures should be taken to focus on defense protection and regular detection.

Future work will focus on further addressing its limitations, contributing to the broader applicability and effectiveness of its future development in ensuring system integrity and performance. For example, integrating artificial intelligence techniques reduces the amount of manual effort required for analysis and increases computational efficiency. The development of reliable predictive models based on historical failure data improves the accuracy of FMECA-FFTA analyses. Establishing standardized methods and guidelines for performing FMECA-FFTA ensures consistency and promotes adoption across industries.

The datasets used and during the current study available from the corresponding author on reasonable request.

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This research was funded by the National Key R&D Program of China (2021YFB3401400), the Fundamental Research Funds for the Central Universities—the Opening Fund of National Engineering Laboratory of Offshore Geophysical and Exploration Equipment, China University of Petroleum, Qingdao 266580, China (20CX02303A), and the Project of Ministry of Industry and Information Technology of the People’s Republic of China (CBZ02N23-10).

College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao, 266061, China

Chao Liu & Hongyan Wang

National Engineering Research Center of Marine Geophysical Prospecting and Exploration and Development Equipment, China University of Petroleum (East China), Qingdao, 266580, China

Chao Liu, Chuankun Zhou, Liping Tan, Junguo Cui, Wensheng Xiao, Jian Liu, Hongyan Wang & Teng Wang

College of Mechanical and Electrical Engineering, China University of Petroleum (East China), Qingdao, 266580, China

Chuankun Zhou, Liping Tan, Junguo Cui, Wensheng Xiao, Jian Liu & Teng Wang

Ulsan Ship and Ocean College Ludong University, Yantan, China

Liping Tan

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Chao Liu: Methodology, Design, Experiment, Writing – original draftChuankun Zhou: Writing – Review & editingLiping Tan: Writing – Review & editingJunguo Cui: Review & editingWensheng Xiao: Analysis, Writing – review & editing, Funding acquisition.Jian Liu: Review & editingHongyan Wang：Review & editing, Funding acquisitionTeng Wang: Investigation, Data collection.

Correspondence to Liping Tan or Hongyan Wang.

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Liu, C., Zhou, C., Tan, L. et al. Reliability analysis of subsea manifold system using FMECA and FFTA. Sci Rep 14, 22873 (2024). https://doi.org/10.1038/s41598-024-73410-y

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Received: 30 December 2023

Accepted: 17 September 2024

Published: 02 October 2024

DOI: https://doi.org/10.1038/s41598-024-73410-y

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