Tight Gas Production Performance Using Decline Curves
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Traditional ("Arps") decline analysis is the most common reservoir engineering tool used for production performance forecasting. It has several advantages over other techniques in that it is simple to use, requires minimal data and is well understood by the industry. Currently, however, these methods are being misused in unconventional applications, such as tight gas. Production perfomance from tight gas reservoirs is characterized by steep initial decline rates and long periods of transient flow. If decline analysis is performed using this transient production data, the main assumption of boundary dominated flow (BDF) is violated and inaccurate forecasts may result. The goal of this work is to understand the behaviour of tight gas reservoirs during transient flow so that the familiar Arps method may be applied. The effects of different tight gas production responses (bilinear, linear, pseudo-radial, boundary dominated) are investigated. Finally, a methodology for applying traditional decline curve analysis to tight gas, with reference to long term transient flow, is presented.
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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 | 0.000 |
| 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) | 0.000 | 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