Solubility of CO2 in Brine and Reservoir Oil for Depleted Reservoirs in China
Bibliographic record
Abstract
Many countries play an important role to carbon capture, utilization, and Storage (CCUS) to reduce emission. Geological storage is an important approach to cope with climate change. Live oil combined with saline aquifers and gas reservoirs are among the candidates for carbon dioxide (CO2) sequestration, owing to their proven sealing capabilities and characterization. CO2 mainly from power industrial was captured to improve oil recovery in some oilfields in China, and solubility is a key factor to evaluate storage ability of reservoir and saline aquifer. In this paper, CO2 sequestration mechanisms in deplete reservoir and saline aquifer were explained. An experimental method to test solubility of CO2 in emulsion system of liver oil and brine was developed. The experimental results showed that with the salinity increased, the solubility of CO2 in reservoir oil increased slightly, and the solubility of CO2 in brine decreased. Salinity has little impact on solubility of CO2 in reservoir oil. With the pressure increased, the solubility of CO2 in reservoir oil and brine both increased. Molecular diameter of reservoir oil and white oil have inverse proportion relationship with the solubility Research on solubility of CO2 in brine and reservoir oil for depleted reservoir in China enabled us to better predict and deal with the challenges for rapid development of carbon storage.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".