Pipeline routing challenges for upstream PNG LNG project*
Bibliographic record
Abstract
The Kikori Basin in Papua New Guinea is the host environment for the gas production and onland transport facilities for ExxonMobil’s PNG LNG. The remoteness of the basin, its vast expanses of intact primary tropical forest, ruggedness, varied and low density population, and the localised impacts of the existing oil and gas industry provided considerable environmental and social challenges to routing and siting of project facilities and infrastructure. Meeting the project’s demanding permitting schedule, while retaining flexibility in design scope for contractor execution, necessitated that the routing process advance at two scales. One was a broad scale that settled a route for project environmental impact assessment using data at the scale of existing regional mapping supplemented by rapid assessment field surveys on the ground; and another a fine scale using pre-construction surveys to identify small-scale constraints to be avoided by tactical routing at a local scale of tens or hundreds of metres for environmental management planning. Reducing potential impacts on the environment was a project priority and the routing process used was integral to this. The approach allowed the project to overcome ubiquitous high value environmental constraints under the scrutiny of project lenders focussed on satisfying industry’s international good practice environmental and social guidelines. This paper will expand on the routing process, including the methods used and key players. The lessons will provide valuable awareness of issues and hurdles to be overcome for other companies intent on developing future oil and gas developments in Papua New Guinea and similar difficult geographies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".