Quantifying Thermally Driven Fracture Geometry During CO2 Storage
Bibliographic record
Abstract
The desired lifetime for CO2 injection for sequestration is several decades at a high injection rate (up to 10 bbl/min or 2,400 tons/day per injector). Government regulations and geomechanical design constraints may impose a limit on the injection rate such that, for example, the bottomhole pressure remains less than 90% of the hydraulic fracture pressure. Despite injecting below the critical fracture pressure, fractures can nevertheless initiate and propagate due to a thermoelastic stress reduction caused by cool CO2 encountering hot reservoir rock. Here we develop a numerical model to calculate whether mechanical and thermal equilibrium between the injected CO2 and the reservoir evolves, such that fracture growth ceases. When such a condition exists, the model predicts the corresponding fracture geometry and time to reach that state. The critical pressure for fracture propagation depends on the thermoelastic stress, a function of rock properties and the temperature difference between the injected fluid and the reservoir (ΔT). Fractures will propagate as long as the thermoelastic stress and the fluid pressure at the fracture tip exceed a threshold; we calculate the extent of a fracture such that the tip pressure falls below the thermoelastically modified fracture propagation pressure. Fracture growth is strongly dependent upon the formation permeability, the level of injection pressure above fracture propagation pressure, and ΔT.
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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) | 0.000 | 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 | 0.001 | 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".