Returning Pipelines to Service Following a Mw7.5 Earthquake: Papua New Guinea Experience
Bibliographic record
Abstract
Abstract The Papua New Guinea Liquefied Natural Gas (PNG LNG) project is a joint venture with participation by ExxonMobil, Oil Search Limited (OSL), Kumul Petroleum, Santos, JX Nippon Oil and Gas Exploration and Mineral Resources Development Company, and began production in 2014. As described in a previous IPC paper, the project, operated by ExxonMobil PNG Limited (EMPNG) sustained a M7.5 earthquake and approximately 300 aftershocks in 2018, epicentered directly under key facilities. Around 150 km of high-pressure gas and condensate pipelines in the rugged PNG highlands were affected but did not lose containment or pressure. Immediately following the M7.5 event, EMPNG began efforts to assess and inspect the pipelines in order to ensure public safety, and, at the appropriate time, restore LNG production. The technical efforts took place along the pipeline Right of Way (ROW) in a remote jungle environment, which, following the earthquake, was also a disaster zone in which the few available resources were prioritized towards humanitarian relief. Due to resource constraints, the pipeline field inspection team typically numbered only two or three specialists. The inspection team drew heavily on analysis work, ongoing since project startup in 2014 and in progress when the earthquake occurred, that simulated the condition of the ROW and pipe stress state following earthquake events similar in magnitude to what actually occurred. The body of existing analysis work allowed the field team to compare aerially observed ROW ground movements to previously modeled cases, and rapidly infer pipe stress state without actually measuring pipe deformation on the ground. Due to resource constraints, that latter activity, if required before startup, would have significantly delayed project restart. The worldwide network of technical resources that had been assisting with ongoing simulations was quickly re-directed to analyzing actual observed ground deformations, efficiently supporting the small field team from outside the disaster zone. After restart, field inspection activities continued, observations were categorized, and an Earthquake Recovery (EQR) organization was initiated to execute ROW repairs. Just as the initial inspection work was aided by pre-earthquake analyses, EQR activities have been expedited by the extensive ROW maintenance program that had been ongoing prior to the earthquake. This paper and accompanying oral presentation present details of the inspection and recovery, and show that the extensive simulations, preparations and maintenance programs supported by EMPNG during project operations prior to the earthquake enabled a rapid and efficient response when the earthquake actually occurred, and thus provided enormous value to the business.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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; both teacher heads agree on what is shown here.
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".