Combining Expert Knowledge and Automation to Maximize Pipeline Route Optionality and Defensibility: A Case Study of the Aurora Pipeline
Notice bibliographique
Résumé
It has become increasingly difficult to successfully develop pipeline projects in North America. This stems from complex matters including environmental opposition, Indigenous rights, regulatory uncertainty, investor indecision and evolving policy. To manage these challenges, developers are advised to consider a route development methodology that provides both optionality and defensibility. This can be achieved through a process that characterizes the landscape based on level of constraint related to environmental and social factors, construction and operational limitations, strategic drivers and cost. Such a process must be analytically robust and able to adapt to new information and priorities emerging throughout the development phase. Particularly in the case of large-scale pipeline projects, traditional routing methods may prove too costly and time-consuming to undertake this analysis in a practical manner. Consequently, proponents may be left with fewer and less defensible route options. Recently, the Aurora Pipeline Team sought to advance preliminary corridor routing under a paradigm of maximum optionality and defensibility in evaluating pipeline routes across northern British Columbia, inclusive of strategic interconnections. Implementing Golder Associates Ltd. automated routing decision support system called “GoldSET” the team was able to rapidly perform a robust corridor options analysis covering over 400,000 km2. This systematic, data-driven process involved subject matter expert assessment of the level of constraint or opportunity associated with individual data layers in consideration of multiple, thematic scenarios. Having consolidated and mapped the aggregated level of constraint across northern BC, routes were generated along paths of least constraint with segments tested for agreement across multiple scenarios. In total, 72 routes comprising more than 50,000 km in total length were developed and evaluated for feasibility. This refinement process ultimately resulted in an interconnected network of approximately 180 pre-screened route segments totaling nearly 12,237 km of potential routes. The advantage provided in subsequent stages of the project was the ability to recognize, quantify and evaluate the tradeoffs between segments, and adapt the route as fatal flaws were encountered. During ensuing, constructability-focused phases of the routing process, optionality had been pre-established, and route changes were able to be made quickly where required. The automated process, in companion to subject matter expert participation, also provided a clear and defensible rationale as to why routes were considered optimal, and how potential impacts to sensitive features were addressed. The evaluation was completed in far less time and more cost-effectively than otherwise possible with traditional methods.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».