An Experimental Investigation of the Effect of pH and Salinity on Sand Control Performance for Heavy Oil Thermal Production
Bibliographic record
Abstract
Abstract In this paper, we present the results of an experimental investigation on the effect of pH and salinity on slotted liner performance in terms of sanding and retained permeability for heavy oil thermal production. This work is an advancement of the existing knowledge in the literature which indicates that pH and salinity could highly affect the mobilization, flocculation and deflocculating of clays (mainly Illite and Kaolinite) in oil sands formations. Water with different pH, in the range of 6.8 to 8.8, and salinities, in the range of 0 to 1.4 % was injected into sand pack samples supported with multi-slot coupon in a Sand Retention Testing (SRT) facility. Measurements included pressure drops along the sand pack and across the slotted liner coupon as well as the produced sand/fines for different flow rates. These measurements were used to assess the effect of the pH and salinity on fines migration within the sand pack, capability of the slotted liner to produce the fines, pore and slot plugging, sand production and the retained permeability. We observed that the pressure drops, fines production and the retained permeability are highly dependent on the pH and salinity of the injected fluid. In low pH and high salinity environment, clay is not mobilized resulting in low pressure drops and high retained permeabilities. On the other hand, an increase in pH value or a decrease in salinity leads to significant clay mobilization and a remarkable reduction in retained permeability. This paper provides a thorough experimental investigation of the pH and salinity effect on slotted liner performance. The effect of the pH and salinity is usually ignored in screen control testing while it could highly control the clay mobilization and retained permeability. Results of this study could trigger wide reconsideration in sand control approaches particularly by altering the pH in the near wellbore zone.
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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 | 0.001 |
| 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) | 0.002 | 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".