Approximate dynamic fault tree calculations for modelling water supply risks

Traditional fault tree analysis is not always sufficient when analysing complex systems. To overcome the limitations dynamic fault tree (DFT) analysis is suggested in the literature as well as different approaches for how to solve DFTs. For added value in fault tree analysis, approximate DFT calculations based on a Markovian approach are presented and evaluated here. The approximate DFT calculations are performed using standard Monte Carlo simulations and do not require simulations of the full Markov models, which simplifies model building and in particular calculations. It is shown how to extend the calculations of the traditional OR- and AND-gates, so that information is available on the failure probability, the failure rate and the mean downtime at all levels in the fault tree. Two additional logic gates are presented that make it possible to model a system’s ability to compensate for failures. This work was initiated to enable correct analyses of water supply risks. Drinking water systems are typically complex with an inherent ability to compensate for failures that is not easily modelled using traditional logic gates. The approximate DFT calculations are compared to results from simulations of theorresponding Markov models for three water supply examples. For the traditional OR- and AND-gates, and one gate modelling compensation, the errors in the results are small. For the other gate modelling compensation, the error increases with the number of compensating components. The errors are, however, in most cases acceptable with respect to uncertainties in input data. The approximate DFT calculations improve the capabilities of fault tree analysis of drinking water systems since they provide additional and important information and are simple and practically applicable

FAULT TREE ANALYSIS FEATURES IN INDUSTRIAL SYSTEMS SAFETY ASSESSMENT: A CASE STUDY

Fault tree analysis is useful both in designing new products/services or in dealing with identified problems in existing ones. In the quality planning process, the analysis can be used to optimize process features and goals and to design for critical factors and human error. As part of safety process improvement, as it is emphasized in the present paper, it can be used to help identify root causes of undesired events such as occupational injuries and illnesses. A case study of application in industrial safety systems illustrates the aim, principle and structure of the technique, allowing better prevention measures selection and implementation

DYNAMIC PROBABILITY FAILURE USING BAYESIAN NETWORK FOR HYDROGEN INFRASTRUCTURE MODELING

To produce large scale hydrogen production, it requires adequate and efficient risk control. For decades, fault tree analysis was the most widely used tool for risk assessment for industrial sector generally and hydrogen infrastructure particularly in terms of risk and consequences associated to it. The limitation to this tool is it tends to be static and do not develop over time which can give unreliable estimation of risk. The purpose of this project is to study the suitability and efficiency of dynamic Bayesian Networks in terms of projecting the risk probability failure that develop over time for hydrogen infrastructure as the alternative of the fault tree analysis. In this study, only the risk probability failure is covered without further exploration on the consequences of the risk. The process involved by the conversion of fault tree to Bayesian Networks model by using appropriate framework. Then, the conditional probability table is assigned to each node where the numbers of CPT depend on the numbers of relationship between nodes. Finally the temporal reasoning is done to show the time-invariant between each node and the beliefs is updated to get the results. The ways of inference use for this study are filtering and smoothing. The results show that generally, the OR gates contribute to higher risk probability compare to AND gates. Besides that, the probability for hydrogen activities increase from year to year with the assumption the accident did not happen the previous year. In addition, the instantaneous release incident is relatively low and unlikely to happen compare to the continuous release

Deepwater Gulf of Mexico Oil Spill Scenarios Development and Their Associated Risk Assessment

World’s growing energy demand has pushed oil companies to explore and produce hydrocarbons in complex and technologically challenging deepwater environments. These difficult and complex operations involve the risk of major accidents as well, demonstrated by disasters such as the explosion and fire on the UK production platform Piper Alpha and capsizing of the Deepwater Horizon rig in the Gulf of Mexico (GoM). Accidents cause death, suffering, pollution of the environment, disruption of business and bad reputation to oil industry. A quantitative risk analysis technique has been used in this study to identify and categorize risk associated with different life phases of a deepwater well. Volume of oil released to the environment is used as a risk indicator. Five oil spill scenarios related to drilling and production life phases of a deepwater well are modeled. Risks associated with drilling an exploratory well in the deepwaters of GoM are analyzed in Scenario-1. A representative well location and corresponding reservoir properties were used to estimate the worst case discharge rates (WCD). Fault tree analysis (FTA) was performed to identify and categorize different hazards. Unexpected pore pressure and delayed response to an emergency situation were identified as two most important parameters contributing to overall risk of the system. In Scenario-2 an underground blowout was modeled by using representative geological settings from Popeye-Genesis field. A shallower low pressure zone is exposed to a deeper high pressure zone during drilling. The time to recharge the shallower zone to its fracture pressure is estimated. The shallower zone will transmit hydrocarbons to sea floor once its fracture pressure is reached. Risks associated with production life phase of a deepwater well are modeled in scenario-3. A representative well location and corresponding reservoir properties were used to estimate the WCD. FTA showed that sand screen and subsea tree control failures were main elements contributing to risk. In scenario-4 risk associated with floating production and offloading (FPSO) system for GoM are quantitatively and qualitatively presented. Scenario-5 deals with oil spill risk associated with severe weather conditions. An example mudslide calculation for SP-70 block of GoM is presented

Dynamic Water Balance Modelling for Risk Assessment and Decision Support on MAR Potential in Botswana

Botswana experiences a water stressed situation due to the climate and a continuously increasing water demand. Managed Aquifer Recharge (MAR) is considered, among other measures, to improve the situation. To evaluate the possibility for increased water supply security, a probabilistic and dynamic water supply security model was developed. Statistically generated time series of source water availability are used in combination with the dynamic storages in dams and aquifers, and the possible supply is compared with the demand to simulate the magnitude and probability of water supply shortages. The model simulates the system and possible mitigation measures from 2013 to 2035 (23 years), using one-month time steps. The original system is not able to meet the demand, and the estimated volumetric supply reliability in the year 2035 is 0.51. An additional surface water dam (now implemented) will increase the reliability to 0.88 but there will still be a significant water shortage problem. Implementing large-scale MAR can further improve the reliability to at least 0.95. System properties limiting the effect of MAR are identified using the model and show how to further improve the effect of MAR. The case study results illustrate the importance and benefit of using an integrated approach, including time-dependence and future scenarios, when evaluating the need and potential of MAR