Analyzing Flowing Production Data with Standard Pressure Transient Methods
Bibliographic record
Abstract
Abstract Interpretation of pressure transient data using the derivative curve has proven to be an effective method for quantifying and qualifying well/reservoir information. Analyzing pressure response data affected by multiple rate changes is well understood and readily done using this standard approach. Daily production data (Time-Rate-Pressure) adheres to the same physics and theoretical description as standard multi-rate drawdown data. Therefore, this form of data can be analyzed in a similar manner. This paper shows that the multiple flow rates and pressures forming production data can be transformed into an equivalent single rate data set for direct analysis using standard methods based on the associated derivative curve. The transformation requires nothing more than careful superposition and the calculation of the normal radial flow derivative curve. We show that this approach avoids two of the biggest difficulties with using rate-time type curves for the analysis of production data: the lack of methods for determining the regions in the data representing the proper flow regimes to apply the appropriate analysis, and the calculation of the correct pseudoequivalent production time function. A method for incorporating a derivative smoothing technique is included to improve the ability to interpret field data, which can often be erratic and difficult to analyze. After a brief presentation of the necessary theory, the applicability of this approach using both simulated examples and field data will be demonstrated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".