CO<sub>2</sub> solubility measurements in brine under reservoir conditions: A comparison of experimental and geochemical modeling methods
Bibliographic record
Abstract
Abstract The dissolution of CO 2 in brine (solubility trapping) is one of the most secure and permanent trapping mechanisms when considering CO 2 geological storage. In addition, CO 2 dissolution in brine is an important mechanism of CO 2 enhanced oil recovery as it improves sweep efficiency and increases oil displacement. Currently, there is a range of experimental methods that has been used to measure CO 2 solubility in brine and a critical review of these methods is presented here. Several different geochemical models that can be used to calculate CO 2 solubility in brine are also reviewed and the importance of selecting the correct equation of state (EoS) is addressed. Furthermore, the validity of the experimental results was ascertained through a comparison of the published experimental results with those produced through geochemical modeling. The geochemical modeling software, HydraFLASH, can be used to accurately calculate CO 2 solubilities under a number of conditions provided the correct EoS is selected. For the purpose of CO 2 ‐water systems, the Valderrama‐Patel‐Teja EoS is the most accurate as it is designed to be used for systems containing polar and non‐polar compounds. The published experimental results were compared with those obtained through the geochemical modeling, to ascertain the most accurate means of measuring CO 2 solubility. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".