Convert or Not to Convert? Practical Scoping Considerations for Converting Pipelines to Carbon Dioxide Service
Bibliographic record
Abstract
Abstract With the move to Net Zero there has been increased demand to utilize pipelines to transport carbon dioxide. As part of these efforts, repurposing hydrocarbon pipelines is an attractive option. The advantages of repurposing existing pipelines rather than building new ones include minimum disturbance to people and the environment, and reduced capital expenditure. While existing documents provide guidance of an overall process to convert, they do not delve into granularity surrounding scoping metrics to screen potential opportunities from impractical applications. Interdependencies of key considerations such as capacity, operating pressure, fluid composition and phase composition will be described with a sequential decision-based methodology proposed. The purposed methodology is a front-end project screening tool recommended to be used in collaboration with business development prior to investing in detailed engineering evaluations of selected candidate pipelines. This further evaluation is required after screening to evaluate the current state of the asset, define specific flow capacities under both summer and winter conditions, class location assessment, detailed consequence modelling and project risk registry to capture all risks. Cost considerations applicable to the detailed assessment will be discussed to support high level assessment of project viability. Examples of typical hydrocarbon pipelines in Alberta are provided to demonstrate the application of the methodology in scoping potential conversion projects. From these examples, it is evident that conversion to gas phase service is typically feasible whereas there are fewer pipelines capable of conversion to dense phase service. Even with conversion being technical feasible, the project must be commercially viable. Gas phase conversion candidates may struggle to meet capacity demands. Whereas, dense phase conversion candidates may be hindered by additional costs associated with modifications identified in detailed engineering. The final decision on whether to convert or not to convert depends on accurate detailed assessments of all the details including the cost of the assessment itself. This paper provides an approach to screen projects for viability before undertaking detailed engineering assessments to provide a more tactical approach to building out carbon dioxide pipeline networks.
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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 | 0.069 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".