Methodology for obtaining fractal parameters using petrophysical and pressure transient data for naturally fractured reservoirs
Bibliographic record
Abstract
Este trabajo introduce una nueva metodologia donde los parametros fractales; la dimension fractal (dmf) y el indice de conectividad (θ) se pueden calcular mediante el analisis de datos petrofisicos y de transientes de presion. Esta metodologia identifica, valida y analiza YNF sin participacion de matriz; los parametros fractales (dmf y θ) se calculan mediante la aplicacion de la tecnica derivada en la respuesta de transiente de presion del pozo y por la aplicacion del analisis de rango re-escalado basado en datos de registros de pozos. La aplicacion practica de esta metodologia se demuestra a traves de los resultados obtenidos de un caso de campo en el que se midio cuantitativamente la densidad de fractura (dmf) y la conectividad entre las fracturas (θ). Este estudio muestra que estos parametros fractales desempenan un papel importante en el comportamiento de la produccion. En la actualidad, los parametros fractales pueden obtenerse analizando unicamente el flujo pseudo estacionario a partir de la respuesta de presion de fondo. En casos de campo, pocas pruebas de pozo son lo suficientemente largas como para identificar este periodo de flujo. Asimismo, la metodologia introducida en este trabajo permite calcular los parametros fractales analizando unicamente la respuesta del transiente de presion y datos petrofisicos, obteniendo resultados confiables para que puedan reducir la incertidumbre en el desarrollo de un campo
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".