Equations of state for basin geofluids: algorithm review and intercomparison for brines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Physical properties of formation waters in sedimentary basins can vary by more than 25% for density and by one order of magnitude for viscosity. Density differences may enhance or retard flow driven by other mechanisms and can initiate buoyancy‐driven flow. For a given driving force, the flow rate and injectivity depend on viscosity and permeability. Thus, variations in the density and viscosity of formation waters may have or had a significant effect on the flow pattern in a sedimentary basin, with consequences for various basin processes. Therefore, it is critical to correctly estimate water properties at formation conditions for proper representation and interpretation of present flow systems, and for numerical simulations of basin evolution, hydrocarbon migration, ore genesis, and fate of injected fluids in sedimentary basins. Algorithms published over the years to calculate water density and viscosity as a function of temperature, pressure and salinity are based on empirical fitting of laboratory‐measured properties of predominantly NaCl solutions, but also field brines. A review and comparison of various algorithms are presented here, both in terms of applicability range and estimates of density and viscosity. The paucity of measured formation‐water properties at in situ conditions hinders a definitive conclusion regarding the validity of any of these algorithms. However, the comparison indicates the versatility of the various algorithms in various ranges of conditions found in sedimentary basins. The applicability of these algorithms to the density of formation waters in the Alberta Basin is also examined using a high‐quality database of 4854 water analyses. Consideration is also given to the percentage of cations that are heavier than Na in the waters.
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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 it