Rig Safety and Reliability Incidents Caused by Software: How They Could Have Been Prevented
Bibliographic record
Abstract
Abstract The Canadian Defense Force and the US Coast Guard list software along with fire, explosion, flood, and earthquake as risks that produce extreme vulnerabilities to maritime technology systems. More than half the US's imported oil comes through the Gulf of Mexico. Fifteen percent of the US daily oil supply comes through the Louisiana Offshore Oil Port system just 20 miles off of New Orleans. Chances are the port won't be shut down by a bang but by a mere keystroke! The control software running these offshore oil and gas drilling and production platforms is extremely vulnerable to unproven, unmanaged and untested software releases. These problems exist because the equipment manufacturers, maritime contractors and operators have very little incentive to fix the vulnerabilities. Their driving goal is to make their operations a profitable as possible while complying with the minimal HSSE rules and regulations. In most cases they have little knowledge of software and systems engineering and can neither evaluate vulnerabilities nor devise solutions once problems arise. Many organizations share the blame for this lack of incentive. This presentation will recount some recent safety disasters and near misses caused by software vulnerabilities. Attendees will learn how to ensure that complex software systems function not only on first use but throughout their life cycle. The author and his team have performed software verification and validation on systems from Norway to Singapore, from Brazil to Angola, and through out the Gulf of Mexico.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".