Experimental and Theoretical Study of Calcium Sulphate Precipitation in Porous Media Using Glass Micromodel
Bibliographic record
Abstract
Mixing of two incompatible waters in water injection projects is usually associated with mineral scale formation and deposition in porous media. Deposition process dramatically affects the performance of water injection scenarios by reduction of porosity and mainly permeability of the rock. In this study, a series of experiments has been conducted to investigate the effect of different parameters on the gradual process of Calcium Sulphate precipitation. These include temperature, concentration of mixing brines, pressure, and flow rate. Due to the visual nature of the glass micromodel, a glass sandstone pattern with water-wet characteristics was used as porous medium to easily observe the scaling formation and distribution. In addition, tracing the movement of the solid particles is highly facilitated in this newly suggested experimental setup. The captured photos in microscopy scanning show that the deposition is initiated from the walls of the pores and throats and extend toward the middle space of porous medium and solid crystals look like chicken roost. For better understanding of the effect of any aforementioned parameter, the related permeability reduction curve versus injected pore volume of the brine solutions was plotted. The results indicated that as the temperature, brine concentration, and flow rate increase the scaling tendency increases as well. The pressure has a minor role on the process development. Deposition of CaSO4 manifests a functional form of permeability reduction due to the effect of different parameters. Therefore, an exponential functionality (correlation) was proposed which incorporates all physical parameters that affect the behavior of the system in dimensionless form. Reynolds number, scaling index, and deviation from equilibrium conditions are the backbone of this correlation. The adjustable exponents of the equation was determined and optimized by means of Genetic Algorithm optimization scheme. This meaningful correlation can also predict the core extracted data with reasonable accuracy.
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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 | 0.000 | 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) | 0.001 | 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".