Advances in Understanding the Scaling Potential for Thermal Wells: A Mechanistic Study
Bibliographic record
Abstract
Abstract Scale deposition and its treatment are crucial part of any thermal recovery method. High temperature variation, phase change associated with steam condensation and flashing, and complex flow dynamics of the wells make the thermal wells more susceptible to scale deposition. Several studies evaluated the type of scales collected from plugged sand screens; however, more investigation is required to address the reservoir conditions and wellbore hydraulics affecting the scaling potential of minerals at downhole conditions. A laboratory workflow combined with a predictive modeling toolbox to evaluate scaling tendency of minerals for different downhole conditions has been developed. First, saturation indices (SI) for different minerals were calculated at reservoir temperature and pressure using water chemistry analysis and the Pitzer theory. Then, the mineral composition of deposited materials collected from thermal wells in Athabasca and Cold Lake area were characterized using Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray Spectrometry (EDS), Total Organic Carbon (TOC) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) analyses. Finally, a comparison analysis was performed between predictive and characterization results. The results of SI calculations showed that Mg-based silicates and Fe-based minerals are positive (SI>5) even at high temperatures (T>430 K). This indicates that the possibility of deposition for these minerals is high. Carbonates (calcite and aragonite) minerals are the most common depositing minerals. However, the extent of scaling index of carbonates is controlled by the concentration of Ca, HCO3, and CO3 in the water sample. The characterization results confirm the results of modeling part. The results of SEM/EDS, ICP-MS analyses showed that carbonates, Mg-based silicates, and Fe-based corrosion products are the most common depositing materials among all minerals. The workflow presented in this study will help the industry to evaluate the scaling potential for thermal wells at different downhole conditions to make a proper decision to prevent plugging of the completion tools.
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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".