Spatial AHP Enables Highly Effective Pipeline Routing Evaluations
Bibliographic record
Abstract
The Analytical Hierarchy Process (AHP) is employed to structure and prioritize the criteria (issues) that most strongly affect pipeline routing decisions for offshore projects. An AHP model is created incorporating these criteria and the pair-wise comparisons technique is used to establish weighting of the criteria. The collaborative pair-wise comparison approach allows all team members to explore and sound out each other's perspectives on the importance of each of the routing assessment criterion in a disciplined way that builds consensus around the model adopted for the cases under investigation. This AHP model is then tagged to the geomatics database automatically linking expertise in the pipelining disciplines with advanced geomatics capabilities for assessing export pipeline routing schemes to directly account for key considerations like flow assurance, spanning, and seabed hazard avoidance. The model is then applied to challenging pipeline planning cases for offshore Western Australia. The results track the rankings of each routing option under each of the identified and prioritized route selection criteria as the actual site data is applied along the entire length of the pipeline. Sensitivities to variations in the weightings of the evaluation criteria are investigated in a way that confirms the robustness of the routing recommendations. The paper clarifies how the issues and key technical information are efficiently captured and applied within full field development planning studies that reflect real-world information (geomatics). The ability to easily accommodate changes in the engineering/technical basis and/or corporate priorities is highlighted, as well as the consensus-building strengths of this advanced decision-support methodology.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".