Thermal Effects on Fines Migration: Insights from Sand Pack Experiments
Bibliographic record
Abstract
Mobilisation of in situ fine particles within oil sands reservoirs plays a critical role in permeability reduction and pore throat blockage, ultimately impairing reservoir performance and diminishing well productivity during thermal recovery operations. Variations in reservoir fluid conditions, such as changes in salinity and temperature, trigger the detachment, transport, and redeposition of fines within porous media. This study introduces a novel high-pressure high-temperature (HP-HT) sand retention testing (SRT) facility designed for evaluating formation damage by fines migration in SAGD producer wells, under salinity change and elevated temperature conditions. Such an integrated approach accounting for conditions closer to near-wellbore SAGD producers has not been explored in previous SRT methodologies. Laboratory tests were conducted on synthetic sand mixtures replicating the particle size distribution (PSD) and sand composition of the McMurray Formation, packed over a slotted liner coupon as a common sand control device used in SAGD producer wells. Produced fines concentration analysis, permeability measurements, and post-mortem retention profile analysis were employed to explain the fines transport mechanisms. The results highlighted the influence of repulsive electrostatic forces in mobilising, transport mechanisms and retention of fine particles at elevated temperature and low salinity conditions. The findings of this paper provide a deeper understanding of fines migration in SAGD reservoirs, delivering insights for optimising field strategies to mitigate fines-related flow restrictions and enhance bitumen recovery efficiency.
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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".