Semianalytical Solution for CO<sub>2</sub> Leakage through an Abandoned Well
Bibliographic record
Abstract
Capture and subsequent injection of carbon dioxide into deep geological formations is being considered as a means to reduce anthropogenic emissions of CO2 to the atmosphere. If such a strategy is to be successful, the injected CO2 must remain within the injection formation for long periods of time, at least several hundred years. Because mature continental sedimentary basins have a century-long history of oil and gas exploration and production, they are characterized by large numbers of existing oil and gas wells. For example, more than 1 million such wells have been drilled in the state of Texas in the United States. These existing wells represent potential leakage pathways for injected CO2. To analyze leakage potential, modeling tools are needed that predict leakage rates and patterns in systems with injection and potentially leaky wells. A new semianalytical solution framework allows simple and efficient prediction of leakage rates for the case of injection of supercritical CO2 into a brine-saturated deep aquifer. The solution predicts the extent of the injected CO2 plume, provides leakage rates through an abandoned well located at an arbitrary distance from the injection well, and estimates the CO2 plume extent in the overlying aquifer into which the fluid leaks. Comparison to results from a numerical multiphase flow simulator show excellent agreement. Example calculations show the importance of outer boundary conditions, the influence of both density and viscosity contrasts in the resulting solutions, and the potential importance of local upconing around the leaky well. While several important limiting assumptions are required, the new semianalytical solution provides a simple and efficient procedure for estimation of CO2 leakage for problems involving one injection well, one leaky well, and multiple aquifers separated by impermeable aquitards.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".