Tank Volume Measurement Systems: Volume Measurement Uncertainty Analysis
Bibliographic record
Abstract
Enbridge Inc. operates the world’s longest crude oil and products pipeline system. The company owns and operates Enbridge Pipelines Inc. (the Canadian portion of the Enbridge crude oil mainline) and a variety of affiliated pipelines in Canada and the United States, including Enbridge Energy Partners, L.P. that operates the Lakehead Pipeline System in the United States. Much of the main pipeline system has been in operation for over 55 years. The mainline system has been in operation for over 55 years. The mainline system is comprised of approximately 15,000 kilometers (9000 miles) of pipeline, 3 million barrels of cavern storage and 3.7 million barrels of tankage (352 tanks) which collectively are integral to transporting more than 2.2 million barrels per day of crude oil and refined products. Enbridge’s measurement needs and practices have continuously evolved, with radar gauging on tanks becoming the standard over the past few years. This paper discusses a number of issues associated with the accuracy of volume measurements in upright, cylindrical petroleum product storage tanks with floating roofs. It includes the analysis and discussion of the process of determining tank volumes and the overall accuracy of a tank volume measurement system. It also includes a discussion of the influence and relevance of each measurement variable on the overall accuracy of tank volume measurements, in order to provide pipeline operators with some insight into determining which variables are most important to the process of tank volume measurement. Finally, it discusses issues regarding tank volume measurement system accuracy, as opposed to overall tank volume measurement accuracy. The focus of this paper is not on the uncertainty analysis process itself, but rather on understanding the results of the uncertainty analysis performed for tank volume measurement systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".