Analysis of Production Data Using the Beta-Derivative
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Bibliographic record
Abstract
Abstract This paper presents a new insight into rate transient analysis using the beta-derivative function (β-derivative). Production rates and flowing pressures from tight gas and shale gas wells were analyzed using various implementations of the betaderivative to emphasize different features of the data and, as a result, reveal characteristic information about flow regimes and the extent to which the reservoir has been drained. The beta-derivative was applied to rate, pressure and normalized rate, and the effect of skin on the β-derivative was also investigated. The intent was to determine which format is the most useful for diagnosing the dominant flow regimes or the sequence of flow regimes that have occurred while producing from an unconventional hydrocarbon reservoirs (tight gas, shale gas and light tight oil). It was found that the classic signature of the β-derivative is altered by the presence of skin. Also, the derivative based on constant rate is different from that based on constant pressure. The beta-derivative's diagnostic value was compared to that of the Bourdet Derivative and the Primary Derivative The β-derivative has significant diagnostic value for identifying power-law type of flow regimes (such as wellbore storage, linear flow, bilinear flow, boundary-dominated flow, etc) because it possesses a recognizable unique character for each of these flow regimes. For instance, the β-derivative is 0.5 for linear flow, 0.25 for bilinear flow and 1.0 for boundary dominated flow. In addition, since the β-derivative is dimensionless, it can be used to differentiate the performance of wells producing from the same field or from different resource plays. The new plotting functions presented in this paper are not intended to replace existing diagnostic functions but can be used in conjunction with them to enhance production data analysis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it