Combining Expert Knowledge and Automation to Maximize Pipeline Route Optionality and Defensibility: A Case Study of the Aurora Pipeline
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".