Correlation/Prediction of CO2 Solubility in Single-Salt and Multi-Salt Brines Using PR EOS Incorporated With Modified Alpha Functions and BIP Correlations
Bibliographic record
Abstract
Abstract Accurate prediction of CO2 solubility in brine is of high significance for carbon capture, utilization, and storage (CCUS); however, no generalized methodology has been made available to successfully tackle such a technical challenge. In this study, a pragmatic and robust technique has been developed and generalized to correlate the CO2 solubility in single-salt brines using the Peng-Robinson equation of state (PR EOS) together with the modified alpha functions and binary interaction parameter (BIP) correlations. A comprehensive CO2 solubility database is firstly built to cover a large range of temperatures, pressures, salt concentrations, while five different salts (i.e., NaCl, KCl, CaCl2, MgCl2, and Na2SO4) are involved. By integrating with the modified alpha functions and BIP correlations, such a generalized model is found to yield an average absolute relative deviation (AARD) of 5.24%, 5.82%, 7.64%, 6.04%, and 4.61% for the correlated CO2 solubility in the aforementioned five single-salt brines, respectively. Compared with the existing correlations, the PR EOS-based model is better than the existing OLI, SP 2010, and DS 2006 models but only slightly inferior to the PSUCO2 model under certain conditions. By combining a new BIP correlation, the newly proposed model has been extended to predict the CO2 solubility in multi-salt brines with an AARD of 7.21%. In addition to its simplicity and high accuracy, this new model can be used to not only determine other physical properties including density, enthalpy, and interfacial tension of CO2-brine systems, but seamlessly integrate with any reservoir simulators, allowing for accurately evaluating and predicting performance of CO2 enhanced oil recovery and storage capacity under various conditions.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".