Preformed Particle Gel for Conformance Control: Factors Affecting Its Properties and Applications
Bibliographic record
Abstract
Summary Preformed particle gel (PPG) is a particled superabsorbent crossklinking polymer that can swell up to 200 times its orginal size in brine. The use of PPG as a fluid-diverting agent to control conformance is a novel process designed to overcome some distinct drawbacks inherent in in-situ gelation systems. This paper introduces the effect of gelant compositions and reservoir environments on the two properties of PPG: swollen gel strength and swelling capacity. Results have shown that PPG properties are influenced by gelant compositions, temperature, brine salinity, and pH below 6. Temperature increases PPG swelling capacity but decreases its swollen gel strength. Salinity decreases PPG swelling capacity but increases its swollen gel strength. PPG is thermostable at an elevated temperature of 120°C if a special additive agent is added into its gelant as a composition. PPG is strength- and size-controlled, environmentally friendly, and not sensitive to reservoir minerals and formation water salinity. Two field applications are introduced to illustrate the criteria of well candidate selection and the design and operation process of PPG treatments. Field applications show that PPG treatment is a cost-effective method to correct permeability heterogeneity for the reservoirs with fractures or channels, both of which are widely found in mature waterflooded oil fields.
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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 | 0.001 | 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) | 0.001 | 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".