Selection of decline curve analysis method using the cumulative production incline rate for transient production data obtained from a multi-stage hydraulic fractured horizontal well in unconventional gas fields
Bibliographic record
Abstract
This research presents the selection of an appropriate decline curve analysis (DCA) methodology according to the transient production performance of a multi-fractured horizontal well in an unconventional gas field by using the cumulative production incline rate (IQp) The IQp introduced in this research is the change of cumulative production per unit time divided by the cumulative production. The IQp was confirmed for use as an indicator that could generalise and quantify a production decline through the analysis of well production data. The IQp values of eight and four DCA methods were applied to the production data generated by a reservoir simulation. The Duong method forecasted the production trends most accurately when the IQp exceeded 0.25%, while the YM-SEPD method was the most preferable for the rates below 0.05%. In addition, the analysis of production data for shale gas wells in Canada and the USA matched the simulation results, confirming that it would be reasonable to use the IQp in order to select a DCA method to forecast the production of unconventional gas wells. [Received: February 23, 2017; Revised: December 7, 2017; Accepted: December 9, 2017]
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".