Design and Construction of Pipeline Integrated Oil Storage Caverns
Bibliographic record
Abstract
In response to industry demand, Hardisty Caverns Limited Partnership (HCLP) has developed cost effective underground storage facilities with a capacity to store 480,000 m3 (3 million barrels) of crude oil. This project is unique through the integration of existing underground salt caverns into a significant North American crude oil transportation hub. Annually, 64 million cubic meters (400 million barrels) of oil move through this hub. This project utilizes existing caverns developed in the late 1960’s. Significant work was required to upgrade the cavern facilities and to construct new surface facilities to integrate the caverns into the crude oil transportation hub. Remote operation of the facility is performed from a control centre in Edmonton. In this paper, the key features of the design and construction of the Hardisty Cavern Storage Project will be presented. Of particular interest are the unique challenges presented due to hydraulic considerations related to cavern operation with multiple product characteristics and to provide crude oil movements exchanges between the cavern storage facilities and both low flow rate feeder pipelines and high flow rate transportation pipelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".