The Why, What, Who, When, and Where of Carbon Capture and Storage in Southern Ontario
Bibliographic record
Abstract
This paper reviews the five Ws (Why, What, Who, When, and Where) of carbon capture and storage in southwestern Ontario. This area is home to nearly one quarter of Canada’s population and approximately three-quarters of one million people work in the manufacturing sector. Fifteen of the province’s top 20 CO2 emission point sources are in this area. The industries responsible for these emissions include steel mills, refineries and petrochemical plants, and cement plants. These industries are part of the hard-to-abate sector, in that CO2 is used or generated as an integral part of the industrial process. As such, eliminating or even reducing emissions from these industries is a difficult task. Carbon capture and storage (CCS) projects aim to sequester that gas in sedimentary basins over periods exceeding several thousand years. To this end, deeply buried (> 800 m) porous and permeable rocks (a repository) must be overlain by impermeable rocks that act as a seal, preventing the upward migration of CO2 into the atmosphere. The possibility that injection activities could trigger seismicity is but one of the additional considerations. When operational, CCS projects have a negative carbon footprint and the desirability of developing and using this technology has been established for over 20 years. True CCS projects differ from carbon capture, utilization, and storage (CCUS) projects in that the former are only designed with sequestration in mind. One type of CCUS project involves using CO2 for enhanced oil recovery (EOR) and this technology has been employed for several decades. Cambrian sandstones are the most suitable injection targets for CCS in southwestern Ontario because previous oil and gas drilling has shown the rocks to have the necessary characteristics. They are buried below 800 m, can be tens of metres thick, and have adequate porosity and permeability. However, the Cambrian section is lithologically and stratigraphically heterogeneous and oil, gas, and brine can all be present in the pore space. The extent to which this complexity will affect CO2 injection has not yet been evaluated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".