Case Studies of a Simple Yet Rigorous Forecasting Procedure for Tight Gas Wells
Bibliographic record
Abstract
Abstract The dominant flow regime observed in many hydraulically-fractured tight/shale gas wells is linear flow. This flow regime may continue for several years, and will ultimately become boundary-dominated flow, at much later times. Nobakht et al. (2010) introduced a simplified method of production forecasting for tight/shale gas wells which exhibit extended periods of linear flow. The method is simple as it relies principally on a plot of inverse gas rate versus square root time, and it is rigorous in that it is based on the theory of linear flow and combines the linear flow transient period with hyperbolic decline during boundary-dominated flow. In the present work, this simplified method is reviewed and applied to almost 90 wells producing from the Montney formation in N.E. British Columbia, Canada. The vast majority of these wells exhibit linear flow for extended periods of time. The advantages of the simplified forecasting method are: (1) It is not biased towards any flow regimes, as no superposition time functions are used; (2) Reliable forecasts can be obtained without invoking pseudo-time and its associated complexities; and (3) The only parameter that needs to be specified externally is the drainage area. The method can be used for forecasting horizontal wells with multiple hydraulic fractures. By assigning different drainage areas to each fracture, a relationship can be developed between expected ultimate recovery (EUR) and original gas in place (OGIP) assigned to each fracture. This translates into recovery factor versus number of fracture stages. The resulting forecasts can be used directly to examine the economics of multi-stage fracturing.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".