Significance of Multiple CO2 Plume Management and Pressure Interactions during Geological CO2 Storage Operation based on the Canadian Aquistore CCS Project
Bibliographic record
Abstract
Abstract Carbon capture and storage is a viable short-to-medium term strategy to reduce emissions and support a low-carbon economy. However, geological storage of CO2 in Giga-tonne scales requires many injection wells, leading to complex integration of multiple CO2 plumes. Based on a decade of CO2 storage operation at the Canadian Aquistore CCS project, this paper explores the potential CO2 plume commingling and subsequent pressure buildup during clustering of large-scale geological CO2 storage operations. To assess the significance of multi-well CO2 injection on storage capacity and injectivity, we constructed a static regional model that characterizes the storage and sealing formations. To understand the potential repositories, traps, and sealing mechanisms, the regional model considered 23 wells in the region to build a robust structural framework, utilizing the geological information, interpreted 3D horizons, well tops, and reservoir properties populated based on 3D seismic porosity volumes. A section of the regional model was selected to match the Aquistore injection history and to evaluate the impact of multiple injection wells and CO2 plumes within the area of interest. The dynamic reservoir model was calibrated to the injection data up to year 2020. The history matched simulation model was then used to predict the injection history for the next three years, until 2023. The simulation forecast was compared against actual field data in the same timeframe to assure the simulation model is capable to predict the CO2 storage operation. Different CO2 injection scenarios were employed for dynamic reservoir simulations for the next 20 years with varying injection rates. The injection pressure was constrained to a value below 95% of fracturing pressure determined from an earlier mini-frac test. We monitored the commingling of the CO2 plumes from different wells under a range of reservoir and operational uncertainties. To address the regulatory concerns, the degree of pressure interference between each adjacent CO2 injectors was used to observe the influence of each CO2 injection well before and after the integration of individual CO2 plumes. Lastly, enhanced operational and adaptive pressure management strategies for the CO2 injection wells were discussed to mitigate the potential subsurface risks, maintaining the injection rates and storage capacities within safe limits. Utilizing eight years of actual field data from the Aquistore CO2 injection site is an asset to better understand the CO2 plume evolution and the associated regional pressure buildup in saline aquifers. The modeling results provide valuable insights into the safe and long-term management of multiple CO2 plumes, and showcase the importance of clustering of large-scale geological CO2 storage projects for operators and regulators.
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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) | 0.006 | 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".