Quantitative risk assessment of a marine riser: an integrated approach
Bibliographic record
Abstract
This work presents an integrated risk assessment methodology for structural failure of a marine riser and the consequent release of oil causing ecological risks to marine life. -- A simple, but efficient methodology for fatigue reliability assessment of a vertical top-tensioned rigid riser is proposed. The fatigue damage response is considered as a narrow-band Gaussian stationary random process with a zero mean for the short-term behavior. However, non-linearity in a response associated with Morison-type wave loading is accounted for by using a factor, which is the ratio of expected damage according to a non-linear probability distribution to the expected damage according to a linear method of analysis. Long-term non-stationary response is obtained by summing up a large number of short-term stationary responses. Uncertainties associated with both strength and stress functions of the limit state are quantified by a lognormal distribution. A closed form reliability analysis is carried out, which is based on the limit state function formulated in terms of Miner's cumulative damage rule. The results thus obtained are compared with the well-documented lognormal format of reliability analysis based on time to fatigue failure. The validity of selecting a lognormal hazard rate function for fatigue life is discussed. A Monte Carlo simulation technique is also used as a reliability assessment method. A simple algorithm is used to reduce the large uncertainty associated with direct sampling. Uncertainty arises in the direct sampling technique because of using a small number of simulations in calculating small failure probabilities. A worked example is included to show the practical riser design problem based on reliability analysis. -- As a part of the ecological risk assessment, a fugacity-based methodology is presented to predict the multimedia fate of spilled oil in a marine environment. A level IV (dynamic) fugacity-based methodology coupled with weathering processes is presented. A two-compartment system, comprised of water and sediment, is used to explore the fate of oil. During a spill, oil is entrained into the water column due to natural dispersion, which is considered as the primary input source to the water compartment. Direct input to the sediment compartment is assumed negligible. However, the water column acts as a source to the sediment compartment. Unlike the conventional multimedia modelling approach, the impact area is not predefined; rather the oil slick spreading process determines the contaminated area growth. Naphthalene is used as an indicator for oil. To demonstrate the application of the proposed methodology, simulations for a batch spill scenario of Statfjord oil are also presented. The current study suggests that the water compartment response to the chemical input is faster than the sediment compartment. The major fate processes identified are advection and volume growth in water and sediment, respectively. -- The current study has used the U.S. EPA ecological risk assessment (ERA) framework to estimate the effects on marine life due to underwater release of oil and gas from a broken riser. This approach combines the hydrodynamics of underwater blowout, weathering algorithms, and multimedia fate and transport to measure the exposure concentration. Uncertainties related to multimedia input parameters are incorporated in the analysis. The 95th percentile of the exposure concentration (EC95% ) is taken as the representative exposure concentration (as a conservative value). A bootstrapping method is utilized to characterize EC 95% and associated uncertainty. Toxicity data available in the literature are used to calculate the 5th percentile of the 'predicted no observed effect concentration' (PNEC5% ) using bootstrapping. The risk is characterized based on the cumulative distribution of risk quotient (RQ), which is defined as the ratio of EC 95% , to PNEC5%. -- This thesis describes a probabilistic basis for the ERA, which is essential from risk management and decision making viewpoints. Two case studies of underwater oil and gas mixture release, and oil release with no gaseous mixture, are used to show the systematic implementation of the methodology, elements of ERA, and the probabilistic method in assessing and characterizing the risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".