Using stakeholders’ judgement and fuzzy logic theory to analyze the risk influencing factors in oil and gas pipeline projects: Case study in Iraq, Stage II
Introduction
Oil and Gas Pipelines (OGPs) are economic and safe to transport the petroleum products. As Hopkins et al. [1] stated that the OGPs are 1.19 times cheaper than ships, 5.29 times cheaper than rail and trucks, 40 times cheaper than airplanes, 40 times safer than railroad tank cars and 100 times safer than tank trucks. Nevertheless, several Risk Influencing Factors (RIFs) may threaten the safety of these projects during the planning, construction, and operational stages including Third-Party Disruption (TPD), corrosion [2], design and construction defects, natural hazards, operational errors, and many others [3], [4]. TPD refers to any external factors that can damage the pipelines [5], which is the major cause of OGP failure in European countries as well as in the USA [1]. Mitigating RIFs in OGP projects is valuable because it minimizes the massive losses that result from damage to the pipelines (e.g. life losses, disturbing the oil export activities, the cost of repairing the damaged pipes, the environmental consequences, and so forth). Moreover, it ensures the safety of the staff that work on OGP projects and the people who live in the surrounding areas. Therefore, the stakeholders in such projects must be aware of the RIFs that can damage OGPs. They must also have a robust risk mitigation system that can keep the RIFs at the lowest level, as far as possible.
As Fang and Marle [6] and Peng et al. [7] stated the management of the RIFs starts with identifying the RIFs, then analyzing them, responding to them, and, finally, control them. However, the existing Risk Analysis Frameworks (RAFs) have the following major and minor limitations regarding the identification and the analysis of the RIFs. The major limitation is that, it is worthwhile to analyze and rank the RIFs regarding their degree of influence on OGP projects considering their probability and severity levels [1] to identify the RIFs that require urgent attention. This is because dealing with each RIF as if it is the most critical one results in a large waste of resources [8]. However, the existing RAFs are not accurate enough to analyze the probability of all types of RIFs, especially TPD which is due to the absence of a historical database [7], [8], [9]. The minor limitation are: (1) the identification and registration of RIFs that may threaten the pipelines must be based on a real database and historical records of the pipelines’ failure causes [10]. Hopkins et al. [1] defined a real database as one that contains records of the pipelines’ design; maps of their routes; pipeline fault and accident data; previous inspections and surveillance records; operational pressure and pressure test records; pipeline maintenance records; and modification records. However, the current RAFs have some limitations due to the inaccurate and uncertain information about the RIFs regarding their levels of probability and severity [11]. This problem is exacerbated further in troubled and developing countries where documentation is not in the best condition. (2) Most of the RAFs are mainly considering one or two RIFs at a time, which means they are not applicable to manage the safety of the pipelines’ projects elsewhere [12]. (3) It is significant to evaluate the Risk Mitigation Methods (RMMs) in relation to their effectiveness in mitigating the RIFs. To make effective recommendations to mitigate the RIF in the projects. However, the effectives of the RMMs have not been evaluated in the previous studies about managing the RIFs in OGPs projects.
From the foregoing text, an authentic study about managing the RIFs in OGP projects is unachievable if the essential data about identifying the RIFs, analyzing them (e.g. their probability and severity of the RIFs), and evaluating the RMMs (e.g. a method's usability and effectiveness) are not accurate. These highlighted crucial problems hinder the efforts of risk mitigation in OGP projects in troubled and developing countries like Iraq. Hence, there is a vital need to help the stakeholders to focus on the most vulnerable segments of pipeline safety by employing a holistic risk analysis approach that can overcome the highlighted crucial problems.
Moreover, traditionally the RIFs are ranked based on their values of Risk Index (RI), which can be calculated using [11], [13], [14].
This method (i.e. Eq. (1)) gives an opportunity to rank the RIFs using the RI which reflect the both of the probability and severity levels of the factors. However, such a ranking method might not accurately reflect criticality of the RIFs in OGP projects in Iraq for the following two reasons. Firstly and majorly, this equations requires accurate values about the probability and severity levels of the RIFs, which are difficult to obtain in the case study area “Iraq” because of the limitations that have been explained earlier in the paper, see the second paragraph in Section 1. Therefore, there is a need to analyze the RIFs deepening on their range of probability and severity levels rather than accurate values about their probability and severity levels. Secondly, because an RIF with a high severity level could still be considered as a critical RIF that needs an urgent attention of management. However, the same RIF could not come at the top of the ranking if it had a low probability level. This is similar if the probability level of the RIF is high and the severity level is low, which is one of the RI method's limitations.
Additionally, Iraq after 2003 experienced a high demand for new OGP projects in addition to rehabilitating the existing pipelines to meet the requirements of the rapid increase in oil exports. However, the inadequacy of managing several RIFs that threatened these projects had a negative effect on the country's oil export activities. For example, there was no available or accessible data that could be used to accurately identify the RIFs that may affect the OGPs in Iraq.
The aim of this paper, therefore, is to develop an integrated RAF that follows qualitative data analysis approach to identify the RIFs and RMMs associated with OGPs; and statistical methodology-based and fuzzy logic theory approaches to analyze the RIFs and RMMs in a more accurate way to enhance the safety of OGP projects, particularly in trouble countries.
To overcome the problem of a scarcity of data and a lack of information about the causes of pipeline failure and risk mitigation methods, a worldwide, qualitative document analyses was carried out to provide a review about OGPs RFs and risk mitigation methods, specifically in insecure environments. Nnadi et al., [15] found that there are many of risk factors are effecting the safety of OGPs in Nigeria. Such as terrorism and sabotage attacks; official corruption; thieves; corrosion and lack of protection against it; improper inspection and maintenance; weak ability to identify and monitor the risks; stakeholders not paying proper attention; lack of proper training, shortage of modern IT services; limited warning signs; lack of risk registration; little research on this topic; public poverty and education level; operational errors; inadequate risk management; natural disasters and weather conditions. Moreover, Rowland [16] say the exposed pipelines and threats to staff are effacing the safety of OGPs in Nigeria. Srivastava & Gupta [8] draw a scenario about a terrorism attack that might happened in India and they expect risk factors like insecure areas, easy access to pipeline and hacker attacks on the operating or control systems might effect OGPs in their country. Other studies added more risk factors like lawlessness, low public legal and moral awareness, and vehicular accidents [7], improper safety regulations; design, construction and material defects and geological risks [17] conflicts over land ownership [18], leakage of sensitive information [19], and animal accidents [20].
Moreover, the existing RAFs are limited to analyzing only one type or two types of RIFs at once. For instance, European countries mainly focus on corrosion and stress-strain risks because their pipelines are underground and they are less subject to sabotage risk. USA focuses more on the terrorism risk, especially after 9/11, in addition to corrosion because the USA uses underground pipelines as well. African countries direct more attention toward thefts risks because the stolen products might be sold on the illegal market. In the meantime, due to current globally insecure environments, critical infrastructures like OGPs are potential targets for saboteurs. Correspondingly, intentional TPD like (terrorism, sabotage and theft) has been recognized as one of the most dominant mechanisms of OGP failure globally [2], [4].
Several studies were analyzed to develop the RAF. In Pakistan, Mubin and Mubin [20] developed a risk management model to provide recommendations to manage the RIFs in gas pipeline projects during the construction stage. In this model, a data bank was created to register the RIFs, which were simulated using Monte Carlo simulation. Schwarz et al. [16] developed a risk management procedure to evaluate the RIFs to support decision-making processes in construction projects. The model started by defining the scope of the projects, the criteria of the risk management, and identifying the RIFs. Then, experts’ judgments and artificial neural network were applied to analyze the RIFs. These two models identified the RIFs based on analyzing documents relating to local projects. El-Abbasy et al. [17] developed a framework to predict the conditions of offshore OGPs in Qatar. In this study, they employed historical data and artificial neural network to priorities the maintenance work for these pipelines. To assess the performance of water distribution pipeline network in Qatar and Canada, El-Abbasy et al. [18] carried out similar work using fuzzy analytical network. However, none of the reviewed models have identified and evaluated RMMs.
Moving forward in this paper, Section 2 provides a detailed review related to identifying and classifying the pipeline RIFs and RMMs. The methodology of designing the RAF and analyzing the RIFs and RMMs and Results are presented in Section 3. Section 4 provide discussions and limitations, and Section 5 highlights the conclusion of the study and the recommendations for future work.
Section snippets
Research methodology
The development of an integrated RAF is a part of the methodology of this paper to mitigate the RIFs. Accordingly, this paper adopts these models to develop a more holistic and effective RAF via bridging the highlighted gaps in these models as shown in Fig. 1.
Following the process of the developed RAF, step 1 was about identifying the associated RIFs and RMMs based on the available database(s) and the previous literature. Deterministic approach and simulation are the main two ways used to
Results
Table 5 presents the results of CBRAM that was developed using the FIS toolbox in MATLAB. The two types of membership functions gave different values of RI without affecting the ranking of these RIFs.
Discussion and limitations
OGPs are a safe mode by which to transport petroleum products as long as the stakeholders are following the design codes; inspecting and maintaining the pipes properly during service; and adopting an adequate risk management system to mitigate the RIFs. The existing RAFs cannot effectively manage the RIFs in OGP projects in troubles countries due to the fact that these methods cannot accurately calculate the probability of the RIFs, especially TPD risks because there is no real data available
Conclusion and future work
Risk management cannot protect pipelines from all RIFs. Meanwhile, it should recognize the best way to mitigate the RIFs. The developed RAF provides a comprehensive and systematic approach to OGP risk management, specifically for organizations that have just begun to mitigate OGP RIFs more effectively.
The results of the questionnaire survey were used to provide real input for a CBRAM to analyze the RIFs and RMMs by using the FIS toolbox in MATLAB. Using fuzzy logic in the process of the risk
Declaration of Competing Interest
None.
Acknowledgments
The authors are extremely grateful for the financial support from the Ministry of Higher Education and Scientific Research (MOHERS), Iraq and AL-Muthanna University.
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