Development of Shale Gas Prediction Models for Long-Term Production and Economics Based on Early Production Data in Barnett Reservoir
Bibliographic record
Abstract
This study examined the relationship between the early production data and the long-term performance of shale gas wells, including the estimated ultimate recovery (EUR) and economics. The investigated early production data are peak gas production rate, 3-, 6-, 12-, 18-, and 24-month cumulative gas production (CGP). Based on production data analysis of 485 reservoir simulation datasets, CGP at 12 months (CGP_12m) was selected as a key input parameter to predict a long-term shale gas well’s performance in terms of the EUR and net present value (NPV) for a given well. The developed prediction models were then validated using the field production data from 164 wells which have more than 10 years of production history in Barnett Shale, USA. The validation results showed strong correlations between the predicted data and field data. This suggests that the proposed models can predict the shale gas production and economics reliably in Barnett shale area. Only a short history of production (one year) can be used to estimate the EUR and NPV of various production periods for a gas well. Moreover, the proposed prediction models are consistently applied for young wells with short production histories and lack of reservoir and hydraulic fracturing data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".