A MultiParameter Methodology for Skin Factor Characterization: Applying Basic Statistics to Formation Damage Theory
Bibliographic record
Abstract
Abstract The following paper describes a Skin Factor characterization methodology that has been developed and successfully applied in fields operated by BP in Colombia, South America. The method is based on basic statistic correlations that are applied for the ranking of different measured or estimated damage parameters; the primary purpose of the method is to weight the different formation damage mechanisms taking place in the complex reservoirs of the Colombian Foothills in such a way that multicomponent skin characterization maps can be estimated. The presence of compositional fluids, active tectonics environments, stacked reservoirs and well access issues all account for the above mentioned complexity. By the application of this methodology, the design of chemical stimulations has become more efficient as the output of the method, which is a Multi-Parameter characterization of the skin, is available for all the wells; in this manner, stimulation packages include components for the control of the main skin mechanisms in the ratios estimated by the model. The model is being continuously updated through the incorporation of measured and estimated damage related variables such as physical chemical analysis of back flowed samples (after stimulations), output from mineral and organic scaling index estimation models, laboratory studies and well intervention records, among others; all of them taken into account for the entire life of a particular well. Fed by the Multi-Parameter model, a skin characterization mapping tool has been developed and has become a key input in the periodically reviews of well productivity; stimulation and well intervention options are being efficiently ranked in terms of benefit leading also to a better planning of well work campaigns.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".