Evaluation of CO <sub>2</sub> Storage Potential in the Deep Mannville Coals of Alberta
Bibliographic record
Abstract
Deep unmineable coal seams are a promising geologic sink in which to store CO 2 permanently, providing a potential pathway to achieving a net-zero emission future. Unlike other porous sedimentary formations (i.e., sandstone) where structural trapping is the dominant storage mechanism, the primary storage mechanism in coal is gas adsorption. Importantly, CO 2 adsorbs onto coal to a greater degree than other typical natural gas components, including CH 4 , providing tremendous retention capacity for carbon sequestration. However, a significant challenge to the long-term injection of CO 2 into coal seams is the loss of injectivity and permeability associated with the adsorption-induced matrix swelling effect. In this study, the results of a field demonstration (pilot) of CO 2 sequestration in the deep Mannville coals of Alberta are provided. The pilot consists of a vertical injection well, used to inject water (pre-CO 2 ) and CO 2 into the coals, and a closely spaced observation well, used to evaluate pressure responses (in the coal and bounding strata) and fluid compositions (in the coal) during injection. A reservoir simulation study was performed in order to guide pilot operations, history-match the pilot data, and predict CO 2 storage and potential migration. The numerical model was set up to include both coal and non-coal bounding strata (multi-layer model) in order to simulate CO 2 , natural gas, and water flow within the coal and to evaluate potential migration into bounding strata. The extended Langmuir model was employed to model the competitive adsorption behavior of CO 2 and CH 4 in the coal, and the Palmer–Higgs model was used to simulate the effect of geomechanical anisotropy, effective stress, and volumetric adsorption strain on permeability evolution during CO 2 injection. As a result, coal permeability was dynamically updated as a function of gas composition and pore pressure in the simulation model. Prior to the pilot demonstration, pre-field simulation results suggested that the operator-specified amount of CO 2 (~1,500 tonnes) can be safely injected into the target Mannville coal seam (at 1,500 m) in less than 7 days. The pre-field simulation model, populated with site-specific geologic information, provided critical guidance to operational design. The reservoir model was then calibrated to both pre-CO 2 water injection/falloff data and to CO 2 injection/falloff data obtained from the field pilot. Matching of the water injection/falloff data was used to derive critical reservoir properties, unaffected by CO 2 adsorption, such as permeability as a function stress. Furthermore, permeability anisotropy was quantified. Matching of the subsequent CO 2 injection/falloff data (a total of 1,512 tonnes was actually injected) was used to assess the impact of CO 2 on coal transport properties and to evaluate the CO 2 storage potential of the Mannville coal seam at the pilot site. Through this analysis, it was determined that injection could be conducted without a significant loss of injectivity and that the coal exhibited strong geomechanical anisotropy. This study successfully demonstrates the feasibility of CO 2 sequestration in the deep Mannville coals in the studied area.
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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) | 0.001 | 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".