A novel injectivity decline prediction model for waterflooding with analytical solutions and field applications
Bibliographic record
Abstract
Well injectivity decline during waterflooding is primarily attributed to retention of injected particles within pores, subsequently blocking flow channels in near-wellbore regions. Developing a predictive model to describe this problem holds significant value as it can inform the development of strategies aimed at preventing or mitigating such damage. Previous research has typically assumed a linear suspension flow or a constant filtration coefficient, which does not represent the near-wellbore suspension flow very well. In this paper, an analytical model for the radial suspension transport in porous media is derived based on the Langmuirian blocking filtration mechanism. Considering the dimensionless distance from the wellbore as a small parameter, we attain the analytical solution through an asymptotic expansion. To provide a basis for comparison, we also obtain numerical solutions using Shampine’s code, which is based on the explicit central finite difference method. Comparison of the analytical and numerical solutions shows that their difference errors remain below 5% under waterflooding conditions. Based on the analytical solution for retained particle concentration, expressions for injection pressure, damage factor and damaged zone radius are also derived and are also expressed explicitly. In the end, we discuss two practical applications of our model: evaluation of existing acidizing jobs and designing new acidizing jobs, based on real field data from Tarim Basin, western China. The results indicate our model is practical in field operations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".