Simulation study of CO <sub>2</sub> geological storage in a tight sand reservoir with hydraulic fractured horizontal well
Bibliographic record
Abstract
Carbon capture, utilization, and storage (CCUS) is a vital technology for reducing greenhouse gas emissions. While numerous studies investigate CO2 storage in conventional geological formations, such as depleted oil reservoirs and saline aquifers, this study provides a novel assessment of CO2 sequestration in a depleted oil and gas reservoir characterized by tight sandstone in British Columbia, Canada. Advanced numerical simulations evaluate the feasibility and challenges of CO2 storage in low-permeability formations, focusing on its behavior in a hydraulically fractured horizontal well with 17 fracture stages. Unlike in conventional reservoirs, CO2 exhibits limited migration, remaining concentrated in fractured zones and demonstrating enhanced storage stability. Over a 10-year injection period, more than 100,000 tons of CO2 were successfully injected at a rate of 15,000 m3/day. The study also quantifies the contributions of various trapping mechanisms: residual trapping (10%), solubility trapping (2.9–14.3%, with the highest solubility under ideal conditions), and structural trapping (0.6%). These findings provide new insights into the potential and limitations of CO2 storage in unconventional reservoirs, highlighting both the challenges and unique advantages associated with tight sand formations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".