CO2 flooding effects and breakthrough times in low-permeability reservoirs with injection–production well patterns containing hydraulic fractures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Comprehensive studies on CO 2 breakthrough times and flooding effects are crucial for optimizing CO 2 flooding strategies. This study utilized numerical simulations to investigate the effects of hydraulic fractures, permeability, and CO 2 injection rates on CO 2 breakthrough times and cumulative oil production. Nonlinear relationships among the respective variables were established, with Sobol method analysis delineating the dominant control factors. The key findings indicate that although hydraulic fracturing shortens CO 2 breakthrough time, it concurrently enhances cumulative oil production. The orientation of hydraulic fractures emerged as a pivotal factor influencing flooding effectiveness. Furthermore, lower permeability corresponds to lower initial oil production, while higher permeability corresponds to higher initial daily oil production. When reservoir permeability is 1 mD, oil production declines at 1000 days, and at 2 mD, it declines at 700 days. At a surface CO 2 injection rate of 10,000 m 3 /d, the daily oil production of a single well is approximately 7.5 m 3 , and this value remains relatively stable over time. The hierarchical order of influence on CO 2 breakthrough and rapid rise times, from highest to lowest, is permeability, well spacing, CO 2 injection rate, porosity, and hydraulic fracture conductivity. Similarly, the order of influence on cumulative oil production, from highest to lowest, is well spacing, porosity, permeability, CO 2 injection rate, and hydraulic fracture conductivity. This work analyzed the impact of geological and engineering parameters on CO 2 flooding and oil production and provided insights to optimize CO 2 injection strategies for enhanced oil recovery.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it