Risk-based reliability assessment of flexible risers for offshore oil and gas production in the Orphan Basin
Bibliographic record
Abstract
Risers operating in ultra deepwater fields require a high level of proven reliability. The Orphan Basin, offshore Newfoundland, is much deeper than other basins in the region at over 2500 m water depth. For this basin, reliability issues are exacerbated due to risks introduced through the coupling of ultra deepwater and ice prone waters. Since fields that require ice management are most often developed using an FPSO with a disconnectable buoy, the unique loads created through flexible riser operation when the buoy is disconnected need to be considered to fully assess the likelihood of riser failure. A site specific, risk-informed approach to assess flexible riser reliability for the Orphan Basin development using flexible riser technology is required and thus presented. The Failure Modes, Effects, and Criticality Analysis (FMECA) was used as the baseline analysis method to determine leading failure mechanisms. A fuzzy TOPSIS-based decision model was then implemented to enhance failure prioritization considering ten (10) interdependent risk circumstances. A probabilistic TOPSIS approach was also completed to validate the results. The failure risks of carcass collapse, lateral buckling, radial buckling, and armour layer degradation due to corrosion were the main failure mechanisms identified through all approaches. A comparison to OREDA industry data was made to validate the findings. The effectiveness of mitigation of the highest risk failure modes was analyzed and mitigation strategies proposed. The unmitigated failure modes were further analyzed through Finite Element Analysis using OrcaFlex. The OrcaFlex model considered an FPSO with a disconnectable buoy, a Production flexible riser which varied in length to meet a Lazy-S wave configuration from 1500 m to 2900 m. The resultant compression when the buoy was dropped to 200 m below mean sea level (MSL) was assessed through quasi-static analysis, and the static loads at buoy hang-off for a connected scenario were extracted. This data was used to calculate the propensity of the riser to fail due to collapse, lateral buckling or radial buckling due to the Reverse End Cap Effect (RECE). The factors of safety against failure were plotted considering recognized industry allowable utilizations for each water depth. Non-linear behavior was observed for all three failure modes studied, suggesting that risk increases disproportionately to increased depth. To formalize this relationship, a novel Risk Amplification Factor (RAF) is proposed. The analysis suggests that at water depths greater than approximately 2000 meters, standard flexible riser design, operation and maintenance approaches may not support an adequate level of flexible riser reliability due to increased risks associated with collapse, lateral buckling and the RECE. Based on this risk escalation, a risk-based inspection (RBI) plan is proposed for flexible risers in UDW ice-prone fields such as the Orphan Basin, to optimize monitoring intervals and focus inspection resources on the most vulnerable riser components.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".