Disposal of Carbon Dioxide, a Greenhouse Gas, for Pressure Maintenance in a Steam-Based Thermal Process for Recovery of Heavy Oil and Bitumen
Bibliographic record
Abstract
Abstract The world is moving towards a carbon constrained economy and there would be pressure to reduce greenhouse gas (GHG) emissions. The steam-based thermal processes are energy intensive and hence, high carbon dioxide (CO2), a greenhouse gas (GHG), emissions. There are potentials for the reduction of CO2 emissions by capturing and disposal of CO2 for pressure maintenance during the later stage of these thermal operations. This paper describes the numerical investigation on the feasibility of using CO2 for pressure maintenance during a steam-assisted gravity drainage (SAGD) wind-down process in an Athabasca bitumen reservoir in Northern Alberta, Canada. It is found that SAGD wind-down process with CO2 injection is feasible although oil recovery performance is slightly sacrificed. The oil recovery performance during the wind-down period with pure CO2 injection is slightly better than those with other gas injections such as flue gas (i.e., a mixture of nitrogen (N2) and CO2) and pure N2 mainly because CO2 acts as a solvent in reduction of the bitumen viscosity. From a GHG storage point of view, CO2 storage capacity of approximately 2 × 107 sm3 (or 37,244 tonne) in a single Athabasca-type SAGD pattern with dimensions of 100 m wide, 500 m long and 25 m thick can be achieved. CO2 balance indicates that CO2 stored during SAGD wind-down processes is less than the CO2 emitted from steam generation during the initial SAGD period. However, "CO2 credit" in the order of 10,000 tonne can still be claimed in a single SAGD pattern because using CO2 as steam substitute during the wind-down process results in less CO2 emission compared to continuously operating the SAGD process. Recently, numerical prediction in this study has been validated qualitatively by the experimental observations of SAGD pressure maintenance from a low-pressure/low-temperature CT experiment and a high-pressure/high-temperature 2-D experiment conducted at the Alberta Research Council (ARC).
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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 | 0.000 |
| 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".