The International Journal of Corrosion Processes and Corrosion Control, 2018
Corrosion is one of the major causes of failure in onshore and offshore oil and gas operations. M... more Corrosion is one of the major causes of failure in onshore and offshore oil and gas operations. Microbiologically influenced corrosion (MIC) is inherently more complex to predict, detect and measure because, for instance, the presence of biofilm and/or bacterial products is not sufficient to indicate active microbiological corrosion. The major challenge for current MIC models is to correlate factors that influence corrosion (i.e. chemical, physical, biological and molecular variables) with the potential of having MIC. Previous work has proposed the potential for MIC as a simple product of multiple factors, without fully considering the synergy or the interference among the factors. The present work proposes a network-based approach to analyse and predict MIC potential considering the complex interactions among a total of 60 influencing factors and 20 screening parameters. The proposed model has the ability to capture the complex interdependences and the synergic interactions of the factors used to assess MIC potential and uses an object-oriented approach based on a Bayesian Network. The model has been tested and verified using real data from a pipeline leakage incident that was a result of MIC. The proposed model constitutes a significant step in deepening the understanding of when MIC occurs and its predictability.
The hazards in complex process systems evolve at an accelerated rate. It is extremely difficult i... more The hazards in complex process systems evolve at an accelerated rate. It is extremely difficult if not impossible to identify and assess all potential hazards and develop strategies to manage them. This demands next generation of process system that is, intelligent to learn faults and prevent them from further propagating, adaptive to evolving conditions, and quick to recover in case failures take place in a component of part of the system. Resilience engineering is a comprehensive term that captures these three (absorptive, adaptive, and recovery) important characteristics of a system. There are limited tools to qualify or quantify the resilience of a system. There have been hardly any studies conducted on dynamic resilience assessment. This paper proposes a dynamic approach to quantify resilience under varying conditions. The approach uses Stochastic Petri-nets (SPN) coupled with Monte Carlo simulation to model and analyze resilience metrics. The proposed approach is tested on a crude oil pipeline system. The test results demonstrate a clear understanding of the resilience characteristics of the system and its evolving nature. This work puts forward a clear pathway for an integrated dynamic model for resilience engineering.
The domino effect accidents in process industries pose a severe threat to human life and the envi... more The domino effect accidents in process industries pose a severe threat to human life and the environment and have the potential to affect nearby facilities as well. Numerous techniques, such as the Bayesian network, have been used for modelling the domino effect. However, these techniques have inherent limitations. These include the inability to consider complex behaviour of process equipment in combined loading and the time dependency of equipment failure. In the current study, a Generalised Stochastic Petri-net model, called as DOMINO-GSPN, is developed to model domino effect accident likelihood and its propagation pattern. The proposed technique is capable of modelling time-dependent failure behaviour of the process component in combined loading. The results from the model are useful in monitoring process risk. A case study is used to demonstrate the application and effectiveness of the model. The results from the model are compared with the past study of a Bayesian network-based domino effect model. This comparative analysis highlights the unique feature of the model and its relevance as a domino effect risk assessment and management tool.
This work presents a new probabilistic methodology and model to estimate the microbiologically in... more This work presents a new probabilistic methodology and model to estimate the microbiologically influenced corrosion (MIC) rate. The proposed methodology considers the variability of the corrosion causing parameters, complex interdependencies of the parameters, and updating the corrosion rate in response to evolving conditions. The proposed method is used to develop a fully parameterized Bayesian network model for the MIC rate. The model is tested using MIC field data. The results show that the metabolism of iron-oxidizing bacteria and methanogens are the most probable contributors to the corrosion rate. The study also identifies the most sensitive parameters affecting the corrosion rate. The proposed model plays a vital role in safety assessment and corrosion risk management of oil and gas production and processing assets.
Competition among companies has intensified during the last few decades and hence monitoring the ... more Competition among companies has intensified during the last few decades and hence monitoring the organization’s environment has become a priority. Monitoring the internal and external environments involves collecting, retrieving, managing, and disseminating large volumes of data and information. Companies are able to handle these complex tasks very efficiently through knowledge management (KM). A valuable tool of KM is business intelligence (BI), that is, the set of coordinated actions of research, treatment, and distribution of information that can help support the company’s competitiveness. This study aims to evaluate BI and quantitatively demonstrate its impact on the competitiveness of an organization. It proposes a methodology and applies it to a multinational food processing company to determine the influencing elements in BI and measure their impacts on the organization’s competitiveness. This study identified four variables of BI that are likely to have an impact on the comp...
Offshore oil and gas processing equipment operating in harsh environments poses high risk.This ri... more Offshore oil and gas processing equipment operating in harsh environments poses high risk.This risk is further increased by the susceptibility of the equipment to natural disasters suchas hurricanes and snowstorms due to harsh environments. When equipment functionalityis compromised, it can become a hazard to personnel as well as to other equipment. Thekey safety practice on the offshore facility is to isolate the equipment and minimize conse-quences associated with processing equipment failures. When and how to isolate vulnerableequipment is a challenge due to limited understanding of the equipment’s susceptibilityand dependency to failure causes and consequences. This paper presents a methodology toanalyze potential failure scenarios considering causation dependency and also determinewhich parameter(s) have the most impact on the failure. The results of the analysis are usedto identify most sensitive equipment and their potential failure causes. This analysis willhelp to develop effective risk management strategies focusing on critical equipment.
Journal of Chemical Technology and Metallurgy, 2016
Flaring is a combustion process of waste gases from the oil and gas industry. The escape of these... more Flaring is a combustion process of waste gases from the oil and gas industry. The escape of these gases from the flare stack without been burned is known as Flameout. These released gases can present human and environment toxicity as well as they can lead to a vapor cloud explosion (V.C.E), if conditions are provided. The flameout events which can occur by environmental, equipment and human factors have not received significant attention compared to other types of flare incidents, most probably due to the fact that they may stay unnoticed if detected and successfully reignited in the early hours. In this work we define some performance indicators extrapolated from a prepared fault tree. They are subsequently assessed through probabilistic methods to evaluate our system safety. The investigation carried out is aimed at better understanding of flameout occurrence mechanisms.
The domino effect accidents in process industries pose a severe threat to human life and the envi... more The domino effect accidents in process industries pose a severe threat to human life and the environment and have the potential to affect nearby facilities as well. Numerous techniques, such as the Bayesian network, have been used for modelling the domino effect. However, these techniques have inherent limitations. These include the inability to consider complex behaviour of process equipment in combined loading and the time dependency of equipment failure. In the current study, a Generalised Stochastic Petri-net model, called as DOMINO-GSPN, is developed to model domino effect accident likelihood and its propagation pattern. The proposed technique is capable of modelling time-dependent failure behaviour of the process component in combined loading. The results from the model are useful in monitoring process risk. A case study is used to demonstrate the application and effectiveness of the model. The results from the model are compared with the past study of a Bayesian network-based domino effect model. This comparative analysis highlights the unique feature of the model and its relevance as a domino effect risk assessment and management tool.
Corrosion Engineering, Science and Technology, 2018
Corrosion is one of the major causes of failure in onshore and offshore oil and gas operations. M... more Corrosion is one of the major causes of failure in onshore and offshore oil and gas operations. Microbiologically influenced corrosion (MIC) is inherently more complex to predict, detect and measure because, for instance, the presence of biofilm and/or bacterial products is not sufficient to indicate active microbiological corrosion. The major challenge for current MIC models is to correlate factors that influence corrosion (i.e. chemical, physical, biological and molecular variables) with the potential of having MIC. Previous work has proposed the potential for MIC as a simple product of multiple factors, without fully considering the synergy or the interference among the factors. The present work proposes a network-based approach to analyse and predict MIC potential considering the complex interactions among a total of 60 influencing factors and 20 screening parameters. The proposed model has the ability to capture the complex interdependences and the synergic interactions of the factors used to assess MIC potential and uses an object-oriented approach based on a Bayesian Network. The model has been tested and verified using real data from a pipeline leakage incident that was a result of MIC. The proposed model constitutes a significant step in deepening the understanding of when MIC occurs and its predictability.
An efficient formalism for safety analysis should be: (i) able to consider the failure behaviour ... more An efficient formalism for safety analysis should be: (i) able to consider the failure behaviour of complex engineering systems, and (ii) dynamic in nature to capture changing conditions and have wider applicability. The current formalisms used for safety analysis are lacking in one of the above-listed criteria. Bayesian network (BN) allows the modelling of failure of systems where the inter-nodal dependencies are represented exclusively by conditional probabilities. Stochastic Petri nets (SPN) enable the study of the dynamic behaviour of complex systems; however, they lack the ability to adapt to changes in the data and operating conditions. This paper proposes a hybrid formalism that strengthens SPN with BN capabilities. The proposed formalism is graphical and uses advance feature such as predicates to perform the data updating functions. This ability enables the analysis of continuous input data without the necessity of time-slice discretization process. The proposed formalism is termed "Bayesian Stochastic Petri Nets" (BSPN). It provides a dynamic assessment of safety by capturing additional sets of data rends. In BSPN, the conditional probability is captured as a time-dependent function to allow consideration of the cumulative effect of the failure scenario. The BSPN implementation is demonstrated with an example illustrating the modelling capabilities.
Failure scenarios analysis constitutes one of the cornerstones of risk assessment and availabilit... more Failure scenarios analysis constitutes one of the cornerstones of risk assessment and availability analysis. After a detailed review of available methods, this paper identified two distinct formalisms to analyze failure scenarios and systems' availability: generalized stochastic Petri nets (GSPN) and Fault tree driven Markov processes (FTDMP). The FTDMP formalism is a combination of the Markov process and the fault tree. This aims to overcome fault tree limitations while maintaining the use of deductive logic. The GSPN is a Petri net with probabilistic analysis using Monte Carlo simulation. The effectiveness of both methods is studied through an emergency flare system including a knockout drum. It is observed that GSPN provides a robust and reliable mechanism for accident scenario analysis. It provides additional information such as events' frequencies at operating and failing modes and expected occurrence timing and durations resulting from different complex sequences. Even for multi-state variables which could be used to design a safety management system. Although FTDMP is a powerful formalism, it provides limited information.
The International Journal of Corrosion Processes and Corrosion Control, 2018
Corrosion is one of the major causes of failure in onshore and offshore oil and gas operations. M... more Corrosion is one of the major causes of failure in onshore and offshore oil and gas operations. Microbiologically influenced corrosion (MIC) is inherently more complex to predict, detect and measure because, for instance, the presence of biofilm and/or bacterial products is not sufficient to indicate active microbiological corrosion. The major challenge for current MIC models is to correlate factors that influence corrosion (i.e. chemical, physical, biological and molecular variables) with the potential of having MIC. Previous work has proposed the potential for MIC as a simple product of multiple factors, without fully considering the synergy or the interference among the factors. The present work proposes a network-based approach to analyse and predict MIC potential considering the complex interactions among a total of 60 influencing factors and 20 screening parameters. The proposed model has the ability to capture the complex interdependences and the synergic interactions of the factors used to assess MIC potential and uses an object-oriented approach based on a Bayesian Network. The model has been tested and verified using real data from a pipeline leakage incident that was a result of MIC. The proposed model constitutes a significant step in deepening the understanding of when MIC occurs and its predictability.
The hazards in complex process systems evolve at an accelerated rate. It is extremely difficult i... more The hazards in complex process systems evolve at an accelerated rate. It is extremely difficult if not impossible to identify and assess all potential hazards and develop strategies to manage them. This demands next generation of process system that is, intelligent to learn faults and prevent them from further propagating, adaptive to evolving conditions, and quick to recover in case failures take place in a component of part of the system. Resilience engineering is a comprehensive term that captures these three (absorptive, adaptive, and recovery) important characteristics of a system. There are limited tools to qualify or quantify the resilience of a system. There have been hardly any studies conducted on dynamic resilience assessment. This paper proposes a dynamic approach to quantify resilience under varying conditions. The approach uses Stochastic Petri-nets (SPN) coupled with Monte Carlo simulation to model and analyze resilience metrics. The proposed approach is tested on a crude oil pipeline system. The test results demonstrate a clear understanding of the resilience characteristics of the system and its evolving nature. This work puts forward a clear pathway for an integrated dynamic model for resilience engineering.
The domino effect accidents in process industries pose a severe threat to human life and the envi... more The domino effect accidents in process industries pose a severe threat to human life and the environment and have the potential to affect nearby facilities as well. Numerous techniques, such as the Bayesian network, have been used for modelling the domino effect. However, these techniques have inherent limitations. These include the inability to consider complex behaviour of process equipment in combined loading and the time dependency of equipment failure. In the current study, a Generalised Stochastic Petri-net model, called as DOMINO-GSPN, is developed to model domino effect accident likelihood and its propagation pattern. The proposed technique is capable of modelling time-dependent failure behaviour of the process component in combined loading. The results from the model are useful in monitoring process risk. A case study is used to demonstrate the application and effectiveness of the model. The results from the model are compared with the past study of a Bayesian network-based domino effect model. This comparative analysis highlights the unique feature of the model and its relevance as a domino effect risk assessment and management tool.
This work presents a new probabilistic methodology and model to estimate the microbiologically in... more This work presents a new probabilistic methodology and model to estimate the microbiologically influenced corrosion (MIC) rate. The proposed methodology considers the variability of the corrosion causing parameters, complex interdependencies of the parameters, and updating the corrosion rate in response to evolving conditions. The proposed method is used to develop a fully parameterized Bayesian network model for the MIC rate. The model is tested using MIC field data. The results show that the metabolism of iron-oxidizing bacteria and methanogens are the most probable contributors to the corrosion rate. The study also identifies the most sensitive parameters affecting the corrosion rate. The proposed model plays a vital role in safety assessment and corrosion risk management of oil and gas production and processing assets.
Competition among companies has intensified during the last few decades and hence monitoring the ... more Competition among companies has intensified during the last few decades and hence monitoring the organization’s environment has become a priority. Monitoring the internal and external environments involves collecting, retrieving, managing, and disseminating large volumes of data and information. Companies are able to handle these complex tasks very efficiently through knowledge management (KM). A valuable tool of KM is business intelligence (BI), that is, the set of coordinated actions of research, treatment, and distribution of information that can help support the company’s competitiveness. This study aims to evaluate BI and quantitatively demonstrate its impact on the competitiveness of an organization. It proposes a methodology and applies it to a multinational food processing company to determine the influencing elements in BI and measure their impacts on the organization’s competitiveness. This study identified four variables of BI that are likely to have an impact on the comp...
Offshore oil and gas processing equipment operating in harsh environments poses high risk.This ri... more Offshore oil and gas processing equipment operating in harsh environments poses high risk.This risk is further increased by the susceptibility of the equipment to natural disasters suchas hurricanes and snowstorms due to harsh environments. When equipment functionalityis compromised, it can become a hazard to personnel as well as to other equipment. Thekey safety practice on the offshore facility is to isolate the equipment and minimize conse-quences associated with processing equipment failures. When and how to isolate vulnerableequipment is a challenge due to limited understanding of the equipment’s susceptibilityand dependency to failure causes and consequences. This paper presents a methodology toanalyze potential failure scenarios considering causation dependency and also determinewhich parameter(s) have the most impact on the failure. The results of the analysis are usedto identify most sensitive equipment and their potential failure causes. This analysis willhelp to develop effective risk management strategies focusing on critical equipment.
Journal of Chemical Technology and Metallurgy, 2016
Flaring is a combustion process of waste gases from the oil and gas industry. The escape of these... more Flaring is a combustion process of waste gases from the oil and gas industry. The escape of these gases from the flare stack without been burned is known as Flameout. These released gases can present human and environment toxicity as well as they can lead to a vapor cloud explosion (V.C.E), if conditions are provided. The flameout events which can occur by environmental, equipment and human factors have not received significant attention compared to other types of flare incidents, most probably due to the fact that they may stay unnoticed if detected and successfully reignited in the early hours. In this work we define some performance indicators extrapolated from a prepared fault tree. They are subsequently assessed through probabilistic methods to evaluate our system safety. The investigation carried out is aimed at better understanding of flameout occurrence mechanisms.
The domino effect accidents in process industries pose a severe threat to human life and the envi... more The domino effect accidents in process industries pose a severe threat to human life and the environment and have the potential to affect nearby facilities as well. Numerous techniques, such as the Bayesian network, have been used for modelling the domino effect. However, these techniques have inherent limitations. These include the inability to consider complex behaviour of process equipment in combined loading and the time dependency of equipment failure. In the current study, a Generalised Stochastic Petri-net model, called as DOMINO-GSPN, is developed to model domino effect accident likelihood and its propagation pattern. The proposed technique is capable of modelling time-dependent failure behaviour of the process component in combined loading. The results from the model are useful in monitoring process risk. A case study is used to demonstrate the application and effectiveness of the model. The results from the model are compared with the past study of a Bayesian network-based domino effect model. This comparative analysis highlights the unique feature of the model and its relevance as a domino effect risk assessment and management tool.
Corrosion Engineering, Science and Technology, 2018
Corrosion is one of the major causes of failure in onshore and offshore oil and gas operations. M... more Corrosion is one of the major causes of failure in onshore and offshore oil and gas operations. Microbiologically influenced corrosion (MIC) is inherently more complex to predict, detect and measure because, for instance, the presence of biofilm and/or bacterial products is not sufficient to indicate active microbiological corrosion. The major challenge for current MIC models is to correlate factors that influence corrosion (i.e. chemical, physical, biological and molecular variables) with the potential of having MIC. Previous work has proposed the potential for MIC as a simple product of multiple factors, without fully considering the synergy or the interference among the factors. The present work proposes a network-based approach to analyse and predict MIC potential considering the complex interactions among a total of 60 influencing factors and 20 screening parameters. The proposed model has the ability to capture the complex interdependences and the synergic interactions of the factors used to assess MIC potential and uses an object-oriented approach based on a Bayesian Network. The model has been tested and verified using real data from a pipeline leakage incident that was a result of MIC. The proposed model constitutes a significant step in deepening the understanding of when MIC occurs and its predictability.
An efficient formalism for safety analysis should be: (i) able to consider the failure behaviour ... more An efficient formalism for safety analysis should be: (i) able to consider the failure behaviour of complex engineering systems, and (ii) dynamic in nature to capture changing conditions and have wider applicability. The current formalisms used for safety analysis are lacking in one of the above-listed criteria. Bayesian network (BN) allows the modelling of failure of systems where the inter-nodal dependencies are represented exclusively by conditional probabilities. Stochastic Petri nets (SPN) enable the study of the dynamic behaviour of complex systems; however, they lack the ability to adapt to changes in the data and operating conditions. This paper proposes a hybrid formalism that strengthens SPN with BN capabilities. The proposed formalism is graphical and uses advance feature such as predicates to perform the data updating functions. This ability enables the analysis of continuous input data without the necessity of time-slice discretization process. The proposed formalism is termed "Bayesian Stochastic Petri Nets" (BSPN). It provides a dynamic assessment of safety by capturing additional sets of data rends. In BSPN, the conditional probability is captured as a time-dependent function to allow consideration of the cumulative effect of the failure scenario. The BSPN implementation is demonstrated with an example illustrating the modelling capabilities.
Failure scenarios analysis constitutes one of the cornerstones of risk assessment and availabilit... more Failure scenarios analysis constitutes one of the cornerstones of risk assessment and availability analysis. After a detailed review of available methods, this paper identified two distinct formalisms to analyze failure scenarios and systems' availability: generalized stochastic Petri nets (GSPN) and Fault tree driven Markov processes (FTDMP). The FTDMP formalism is a combination of the Markov process and the fault tree. This aims to overcome fault tree limitations while maintaining the use of deductive logic. The GSPN is a Petri net with probabilistic analysis using Monte Carlo simulation. The effectiveness of both methods is studied through an emergency flare system including a knockout drum. It is observed that GSPN provides a robust and reliable mechanism for accident scenario analysis. It provides additional information such as events' frequencies at operating and failing modes and expected occurrence timing and durations resulting from different complex sequences. Even for multi-state variables which could be used to design a safety management system. Although FTDMP is a powerful formalism, it provides limited information.
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with the potential of having MIC. Previous work has proposed the potential for MIC as a simple product of multiple factors, without fully considering the synergy or the interference among the factors. The present work proposes a network-based approach to analyse and predict MIC potential considering the complex interactions among a total of 60 influencing factors and 20 screening parameters. The proposed model has the ability to capture the complex interdependences and the synergic interactions of the factors used to assess MIC potential and uses an object-oriented approach based on a Bayesian Network. The model has been tested and verified using real data from a pipeline leakage incident that was a result of MIC. The proposed model constitutes a significant step in deepening the understanding of when MIC occurs and its predictability.
factors. The present work proposes a network-based approach to analyse and predict MIC potential considering the complex interactions among a total of 60 influencing factors and 20 screening parameters. The proposed model has the ability to capture the complex interdependences and the synergic interactions of the factors used to assess MIC potential and uses an object-oriented approach based on a Bayesian Network. The model has been tested and verified using real data
from a pipeline leakage incident that was a result of MIC. The proposed model constitutes a significant step in deepening the understanding of when MIC occurs and its predictability.
with the potential of having MIC. Previous work has proposed the potential for MIC as a simple product of multiple factors, without fully considering the synergy or the interference among the factors. The present work proposes a network-based approach to analyse and predict MIC potential considering the complex interactions among a total of 60 influencing factors and 20 screening parameters. The proposed model has the ability to capture the complex interdependences and the synergic interactions of the factors used to assess MIC potential and uses an object-oriented approach based on a Bayesian Network. The model has been tested and verified using real data from a pipeline leakage incident that was a result of MIC. The proposed model constitutes a significant step in deepening the understanding of when MIC occurs and its predictability.
factors. The present work proposes a network-based approach to analyse and predict MIC potential considering the complex interactions among a total of 60 influencing factors and 20 screening parameters. The proposed model has the ability to capture the complex interdependences and the synergic interactions of the factors used to assess MIC potential and uses an object-oriented approach based on a Bayesian Network. The model has been tested and verified using real data
from a pipeline leakage incident that was a result of MIC. The proposed model constitutes a significant step in deepening the understanding of when MIC occurs and its predictability.