Analysis of potential CO2 leakage through abandoned wells using a semi-analytical model
Bibliographic record
Abstract
Potential injection sites for geological CO2 storage include deep formations in mature sedimentary basins. Many of these basins have a long history of oil and gas exploration and production and the vicinity of the injection site may therefore be perforated by hundreds of wells, potentially penetrating into the injection formation. Geosequestration models must therefore be able to simulate plume spreads over large spatial areas (of order 1,000 km2), while resolving the local dynamics in all the wells. Furthermore, many of these wells are abandoned and their locations and hydraulic properties might be uncertain or unknown. Therefore, risk assessment based on Monte Carlo simulations may be necessary to estimate the resulting uncertainty in the leakage. In this paper, we present a semi-analytical model that simulates the evolution of CO2 plumes and leakage in multiple brine aquifers pierced by multiple passive wells over decadal to century time scales. The model’s equations and state variables are obtained from the self-similarity of the plume shapes and are defined solely at well locations. Since the model does not require domain discretisation in the traditional numerical sense, it is highly computationally efficient, potentially thousands of times faster than existing numerical multiphase simulators. This paper demonstrates the insights gained by applying this model to a potential injection site in the Alberta Basin, Canada, involving more than 500 existing wells over a domain that is 900 km2. Different leakage measures and statistics are presented and discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 source (direct Gemma or distilled Codex), 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".