The Impact of Vapor/Liquid-Equilibria Calculations on Scale-Prediction Modeling
Bibliographic record
Abstract
Summary Vapor/liquid-equilibria (VLE) calculations, particularly involving the phase behavior of carbon dioxide (CO2) and hydrogen sulfide (H2S), are used in scale-prediction modeling. In this work, the impact of VLE calculations for CO2- and H2S-rich gas phases and for acid- and sour-gas mixtures on scale-prediction calculations is evaluated. Three equations of state (EOSs)—Soave-Redlich-Kwong (SRK) (Soave 1972), Peng-Robinson (PR) (Peng and Robinson 1976), and Valderrama-Patel-Teja (VPT) (Valderrama 1990)—are implemented in the Heriot-Watt model and used in VLE calculations. The solubility of single-component CO2 and H2S in water and the solubility of a gas mixture in water were compared with experimental data in terms of the absolute relative deviation (ARD). The solubility data were then used in PHREEQC (USGS 2016) to calculate the impact of using different EOSs on carbonate and sulfide scales, particularly on calcium carbonate (CaCO3) and iron sulfide (FeS). Average ARDs of 6.04, 4.10, and 3.77% between experimental and calculated values for CO2 solubility in water were obtained for the SRK, PR, and VPT EOSs, respectively. Similarly, for H2S solubility in water, average ARDs of 6.49, 6.66, and 6.48% were obtained for each EOS, respectively. For the solubility of sour- and acid-gas mixtures in water, average ARDs of 13.92, 13.25, and 10.78% were obtained, respectively. It has thus been concluded that the VPT EOS performs better than the SRK and PR EOSs in VLE calculations for the analyzed data. The errors introduced in VLE calculations have been found to impact the calculation of the amount of CaCO3 precipitated, with consequences for scale-inhibitor selection. Higher deviations were found in the amount of CaCO3 precipitation for gas mixtures when compared with a single-component, CO2-rich phase. Furthermore, the large errors occurring in VLE calculations for H2S solubility have not been found to impact the calculation of the amount of FeS precipitated when H2S is in excess with respect to Fe2+. In addition to this, a case study that was performed by use of formation-water data from the Brazilian presalt revealed that the choice of EOS can cause errors of 6 kg of precipitate during each day of production. Scale-prediction calculations carried out with PHREEQC demonstrate that VLE calculations can have a high impact on mineral precipitation. Thus, it is recommended that the best VLE model available should always be used for scale-prediction modeling, particularly when mixtures of gases are present.
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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 | 0.000 | 0.000 |
| 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".