Velocity-based Formation Damage Characterization Method for Produced Water Re-injection: Application on Masila Block Core Flood Tests
Bibliographic record
Abstract
With increasing environmental regulations, more and more produced water is being re-injected; however, water injection programs may have low efficiency due to formation damage around the injected wellbore. Traditionally, formation damage was treated as a deep bed filtration (DBF) type of process characterized by laboratory-based damage parameters. These parameters inquire expensive concentration measurement, and lab-scaled results are not usually applicable for field cases. Recent studies on formation damage are more attracted to history-based approaches using an empirical damage equation to capture the uniqueness of each case study. In our previous work, such empirical (velocity based) model was studied and shown to be more practical than (and equivalent to) the DBF model. A robust characterization method was developed to calculate the damage parameters explicitly, and it was successfully tested against offshore field data. In this work, the method has been applied for analysis of a series of core flood tests on cores from the Masila Block field in Yemen and compared with measured damage parameters. Good agreement with lab-measured values validates the characterization method. The accuracy of the method is comparable to the DBF approach, while it is simpler and more suitable for implementing in reservoir simulators.
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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 | 0.001 | 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".