Abstract:Well plugging and abandonment are necessitated to ensure safe closure of a non-producing offshore asset. Little or no condition monitoring is done after the abandonment operation, and data are often unavailable to analyze the risks of potential leakage. It is therefore essential to capture all inherent and evolving hazards associated with this activity before its implementation. The current probabilistic risk analysis approaches such as fault tree, event tree and bowtie though able to model potential leak scen… Show more
“…The results (leak probability) obtained is in between that derived from an OR and AND gate. NOISYOR gates have also been used to model static well PA where there is uncertainty of data [5] and in O'Connor and Mosleh [6] ’s common cause failure models. Fig.…”
There is increasing interest to consider dependent failures and human errors in the offshore industry. Permanently abandoned wells dot most of the subsea environment. The nature of a well plugging and abandonment (Well P&A) run - usually the lowest-cost contractor engaged to plug several wells tapping the same reservoir makes it an ideal case study for incorporating failures based on common causes. The heavy use of operators during a cementing job also provides the case for analysis of human error in such tasks. One proposed method to analyse the above-mentioned is the use of Bayesian Belief Networks to achieve the following objectives (1) to capture better estimates of a well PA event by incorporating dependencies, and meet regulatory requirements by authorities; and (2) to use the same model to provide long term monitoring of a group of wells linked by common dependencies. This model has not only captured the dependencies of multiple variables, but also projected it in a dynamic manner to provide a risk profile for the next decade where well integrity failure is likely to happen.
• Proposed adapted method capture better estimates of a well PA event by incorporating dependencies
• Method allows for extension of model to long term monitoring of a group of wells linked by common dependencies
“…The results (leak probability) obtained is in between that derived from an OR and AND gate. NOISYOR gates have also been used to model static well PA where there is uncertainty of data [5] and in O'Connor and Mosleh [6] ’s common cause failure models. Fig.…”
There is increasing interest to consider dependent failures and human errors in the offshore industry. Permanently abandoned wells dot most of the subsea environment. The nature of a well plugging and abandonment (Well P&A) run - usually the lowest-cost contractor engaged to plug several wells tapping the same reservoir makes it an ideal case study for incorporating failures based on common causes. The heavy use of operators during a cementing job also provides the case for analysis of human error in such tasks. One proposed method to analyse the above-mentioned is the use of Bayesian Belief Networks to achieve the following objectives (1) to capture better estimates of a well PA event by incorporating dependencies, and meet regulatory requirements by authorities; and (2) to use the same model to provide long term monitoring of a group of wells linked by common dependencies. This model has not only captured the dependencies of multiple variables, but also projected it in a dynamic manner to provide a risk profile for the next decade where well integrity failure is likely to happen.
• Proposed adapted method capture better estimates of a well PA event by incorporating dependencies
• Method allows for extension of model to long term monitoring of a group of wells linked by common dependencies
“…Then, individual Bayesian networks and an object-oriented Bayesian network were developed, considering common cause failures and conditional dependencies along with performing probability updating and sequential learning using accident precursors. Similarly, Babaleye et al (2019) and Chang et al (2019) applied the Bayesian network for a dynamic safety analysis of the plugging and abandonment of oil and gas wells and the deep-water drilling riser, respectively. Furthermore, Bayesian networks have also been used for evaluation of the domino effect, where Khakzad et al (2018) and Zeng et al (2020) implemented a dynamic model of wildfire spread by using the Bayesian network.…”
After the Seveso disaster occurred more than 40 years ago, there has been an increasing awareness of the potential impacts that similar accident events can occur in a wide range of process establishments, where the handling and production of hazardous substances pose a real threat to society and the environment. In these industrial sites denominated "Seveso sites," the urgent need for an effective strategy emerged markedly to handle hazardous activities and to ensure safe conditions. Since then, the main challenging research issues have focused on how to prevent such accident events and how to mitigate their consequences leading to the development of many risk assessment methodologies. In recent years, researchers and practitioners have tried to provide useful overviews of the existing risk assessment methodologies proposing several reviews. However, these reviews are not exhaustive because they are either dated or focus only on one specific topic (e.g., liquefied natural gas, domino effect, etc.). This work aims to overcome the limitations of the current reviews by providing an up-to-date and comprehensive overview of the risk assessment methodologies for handling hazardous substances within the European industry. In particular, we have focused on the current techniques for hazards and accident scenarios identification, as well as probability and consequence analyses for both onshore and offshore installations. Thus, we have identified the research streams that have characterized the activities of researchers and practitioners over the years, and we have then presented and discussed the different risk assessment methodologies available concerning the research stream that they belong to.
“…The P&A operation of the Elgin oil well in 2013 and the G-4 oil well of Troll Field in 2016 failed. The leaked crude oil not only polluted a large area of the nearby sea but also spent a lot of money on environmental management and relaunch of rescue P&A work (Babaleye et al, 2019). The current research on P&A operations is mainly aimed at estimating the operation time and cost (Moeinikia et al, 2015), and determining the cause and probability of leakage after the operation is completed (Babaleye et al, 2019).…”
The Riserless Well Intervention (RLWI) system that performs complex offshore oil well Plugging and Abandonment (P&A) operations is a typical Multi-Mission Phased-Mission System (MM-PMS), which requires multiple missions to be completed within a phase. P&A processes involve complex operations and equipment that can contaminate local marine ecosystems if they fail. Therefore, it is necessary to evaluate the reliability of the RLWI system. This paper proposes a dynamic reliability evaluation model for analyzing the RLWI MM-PMS. The GO model of the phase operation process and the Fault Tree (FT) model used to analyze the failure of each mission were established, and a Dynamic Bayesian Network (DBN) model based on the GO model and the FT model was developed for reliability evaluation. The established model can analyze the changes in the reliability of the RLWI MM-PMS more comprehensively, and can also clarify the importance of different missions and different system components. In addition, considering the impact of the marine environment on operators, the Standardized Plant Analysis Risk-Human (SPAR-H) reliability analysis is used for quantification. These findings can guide the improvement of the reliability of the RLWI system and the success rate of P&A operations.
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