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Fam, Mei Ling (Nanyang Technological University / Lloyd's Register Singapore Pte Ltd) | He, Xuhong (Lloyd's Register Consulting - Energy AB) | Dimitrios, Konovessis (Singapore Institute of Technology) | Ong, Lin Seng (Nanyang Technological University) | Tan, Hoon Kiang (Lloyd's Register Singapore Pte Ltd)
A Bayesian Belief Network (BBN) has been used to model operational risks of a decommissioning event in a bid to consider incorporating dependencies and human reliability analysis in offshore risk assessment. One method of validating BBN models includes the assessment of predictive power, sensitivity analysis and comparison with literature or historical data. The above validation method thus assists to support the development of the credibility and acceptance of a BBN model on decommissioning risks.
Decommissioning projects are sparse and spread out across different operating conditions, hence data availability pertaining to such events are difficult to obtain. There is also an increasing trend to incorporate human reliability analysis in offshore risk assessment. Current offshore Quantitative Risk Assessment tends to utilise a generic human error probability. In some cases only a few generic human error probabilities are used in the studies without detailed analysis. In some cases, HRA methods from nuclear industry are used. A Bayesian Belief Network (BBN) has been used to model operational risks of a decommissioning event. Expert judgement is used to quantify human failure probabilities, where expert judgement is interpreted as information on the magnitude of the likelihood of an error. The expert judgement process takes place over defined possible operating scenarios/conditions based on human reliability assessment guidelines. The BBN model also includes risk assessment based on system reliability analysis techniques by looking into components of defined equipment, and its corresponding safety functions.
There is no set standard for the validation of BBNs, however, a methodology by Kleeman et al (2018) has been applied to this BBN model. The BBN validation methods include the assessment of predictive power, sensitivity analysis and comparison with literature or historical data. The following will be applied to validate the model: (i) a sensitivity analysis of the model based on the sensitivity function, which expresses the probability of interest in terms of the parameters under study, or the extent a parameter can be varied without inducing a change in the most likely outcome, especially as different outcomes warrants different solutions required to reduce the risk picture and (ii) extreme- condition test of the BBN output, which expands the model’s ability to react to measures that move the system outside the normal behaviour. The third measure is an adoption of established system reliability analysis used in fault and event tree analysis, where certain aspects of the model can be compared with literature or historical data, this however refers to common equipment such as sensors or valves. The above thus assists to support the development of the credibility and acceptance of a BBN model on decommissioning risks.
Yin, Zhenyuan (National University of Singapore, Lloyds Register Global Technology Centre) | Moridis, George (Lawrence Berkeley National Laboratory, Texas A&M University) | Tan, Hoon Kiang (Lloyds Register Global Technology Centre) | Linga, Praveen (National University of Singapore)
Due to its increasing abundance, cleaner and lower emissions upon combustion, natural gas (NG) has been considered as the best transition fuel away from coal and oil to a carbon-constrained world. Methane gas is the major component in NG accounting for 70-90%, and is also the major constituent found in natural gas hydrates (NGHs). The amount of CH4 preserved in NGHs is vast and estimated to be 20,000 trillion cubic meter (TCM) worldwide. This outweighs the proved NG reserve on earth, which is 865.4 TCM, and doubles the combined reserve of all fossil fuels. Thus, NGHs have been considered as a potential future energy source. Extensive geological surveys and drilling programs have been carried out during the past two decades at various countries (Canada, USA, Japan, S. Korea, India, China, etc.) to identify the location of these NGH reservoirs, to quantify the amount of gas deposited and to recover hydrate cores to analyze their thermophysical and geomechanical properties. Extensive research have also been carried out in laboratories to synthesis gas hydrate mimicking marine and permafrost conditions, and to study their fundamental behavior of formation and dissociation. In this study, we numerically analyzed an experiment of methane hydrate bearing sediment (
Current visual assessment of coating damage is performed by experienced inspector. This leads to variation in procedures and decisions among inspectors. Disagreement caused during assessment introduces issue of low reliability and repeatability of assessment results. A software that automatically identifies and determines percentage of defective areas in a given image is presented. Tested on a dataset of structural defects, outcome of the software is validated against answers provided by engineers and inspectors. The software requires an image of coating surface and hints on regions of interest as inputs. Users provide hints by clicking few times on regions they are interested (known as foreground) and not interested in (known as background). With built-in intelligence, the software scans the image and identifies defective areas by comparing colour difference between each pixel and hints user provided. If colour value of a pixel is found to be closer to hints in foreground, it is labelled as defective area and vice versa. Next, percentage of defective area in the image is determined and reported. Dataset of structural defects (