Quantitative Analytical Model of the Formation Damage by Gel Particles
Bibliographic record
Abstract
Formation damage by gel particles has become one of the most important problems in mature reservoirs. The objective of the quantitative analytical model is to identify an analytical model to the best fit of the preformed particle gels (PPGs) filtration test results. This work will analyze the experiments results of low permeability core samples to evaluate the effect of various brine concentrations and particle sizes. This study used a linear analytical model relationship between cumulative volumes versus filtration time with a good fits result. Linear curve equations for the best fitting equation was obtained. According to quantitative analytical model for all of our filtration tests, the cumulative filtration test volume (Vcf) was explained in this paper. Quantitative analytical model results showed the value of the slop m increases as the injection pressure increases. Compared with the experiments, the results show that, if the value of the intercept b > 2 the damage occurred because the gel particles invasion started into the core surface. Results from the quantitative analytical model were indicated to have a good fitting with almost all of the experimental results. It is the first time to use quantitative analytical model to analysis the formation damage by the PPGs. The results can be used to select the best gel treatment design.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".