A Laboratory Workflow for Characterization of Scaling Deposits in Thermal Wells
Bibliographic record
Abstract
Previous studies have shown that different parameters such as reservoir conditions (e.g., pressure, temperature, and brine chemistry) and wellbore hydraulics influence the scaling tendency of minerals on the surfaces of completion tools in conventional resources. Although different studies have investigated the suitable conditions for the precipitation of scaling minerals, there is still a lack of understanding about the composition of the scaling materials deposited on the surfaces of completion tools in thermal wells. In this study, we presented a laboratory workflow combined with a predictive toolbox to evaluate the scaling tendency of minerals for different downhole conditions in thermal wells. First, the scaling indexes (SIs) of minerals are calculated for five water samples produced from thermal wells located in the Athabasca and Cold Lake areas in Canada using the Pitzer theory. Then, different characterization methods, including scanning electron microscopy (SEM) with energy dispersive X-ray spectrometry (EDS), inductively coupled plasma mass spectrometry (ICP-MS) and colorimetric and dry combustion analyses, have been applied to characterize the mineral composition of scale deposits collected from the surfaces of the completion tools. The results of the SI calculations showed that the scaling tendency of calcite/aragonite and Fe-based corrosion products is positive, suggesting that these minerals can likely deposit on the surfaces of completion tools. The characterization results confirmed the results of the Scaling Index calculations. The SEM/EDS and ICP-MS characterizations showed that carbonates, Mg-based silicates and Fe-based corrosion products are the main scaling components. The results of dry combustion analysis showed that the concentration of organic matter in the scale deposits is not negligible. The workflow presented in this study provides valuable insight to the industry to evaluate the possibility of scaling issues under different downhole conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".