Solubility product constants for natural dolomite (0–200 °C) through a groundwater-based approach using the USGS produced water database
Bibliographic record
Abstract
The calculation of a reliable temperature dependent dolomite solubility product constant (Ksp−dol) has been the subject of much research over the last 70 years. This study evaluates log10(aCa2+/aMg2+) values using PHREEQC (Pitzer approach) for a screened subset (n = 11,480) of formation waters in the U.S. Geological Survey National Produced Waters Geochemical Database V2 (PWGD), an extensive inventory of 165,960 formational waters from a range of sedimentary lithologies in North America up to 6.6 km depth (Blondes and others, 2016). Through extensive ground truthing against datasets sourced from Texas Gulf Coast basin and the Mississippi Salt Dome basin we establish both the geochemical data from the PWGD and a new geothermal model of the US that is used to determine temperatures at-formation-depth to be reliable data sources. The vast majority (90%) of PWGD samples have log10(aCa2+/aMg2+)-temperature values that are interpreted to be indicative of calcite-dolomite equilibrium and buffering by the bulk mineral solubilities. Using statistical models with different parameterisations (different Maier-Kelly formulas, mixed-effects models with various random effects and linear models) log10(aCa2+/aMg2+) values are regressed against the estimated at-formation-depth temperatures to determine Ksp−dol between 0 and 200 °C. This process relies on the well constrained calcite solubility product constant (Ksp−cal). Local effects that modify log10(aCa2+/aMg2+) values are evaluated through the addition of random effects to the mixed model which both improves the statistical reliability of the Ksp−dol model and enables the determination of Ksp−dol values for local dolomite phases. The nature of these local effects is open to interpretation, but we suggest the primary influence on log10(aCa2+/aMg2+) values is the stoichiometry of the dolomite phase systematically modifying log10(aCa2+/aMg2+) values. We discount the influence on log10(aCa2+/aMg2+) values of dolomite order, the solution ionic strength, equilibration with anhydrite and chlorite group minerals, illitization of smectite and albitization of feldspar. For the dolomite solubility equation; the mixed-effects model (model J23) chosen as most representative yields a pKsp−dol (log10Ksp−dol); We determine pKsp°−dol to be −17.27 ± 0.35 (25 °C, 1 atm) which is close to prior estimates, including the most recent experimental value reported by Bénézeth and others, 2018 (pKsp°−dol = −17.19 ± 0.3) validating the groundwater regression analysis approach of this study.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".