Effect of Heterogeneity in a Horizontal Well With Multiple Fractures on the Long-Term Forecast in Shale Gas Reservoirs
Bibliographic record
Abstract
Abstract Shale gas reservoirs have become a significant source of gas supply in North America owing to the advancement of drilling and stimulation techniques to enable commercial development. The most popular method for exploiting shale gas reservoirs today is the use of long horizontal wells completed with multiple-fracturing stages (MFHW). The stimulation process may result in bi-wing fractures or a complex hydraulic fracture network. However, there is no way to differentiate between these two scenarios using production data analysis alone, making accurate forecasting difficult. For simplicity, often hydraulic fractures are considered bi-wing when analyzing production data. A conceptual model that is often used for analyzing MFHWs is that of a homogeneous completion; in which all fractures have the same length. However, fracture lengths that are equal in length are rarely if ever seen (Ambrose et al., 2011). In this paper, production data from heterogeneous MFHW (i.e., all fracture lengths are not the same) drilled in extremely low permeability reservoirs is studied. First, the simplified forecasting method of Nobakht et al. (2010) developed for homogeneous completions is extended to heterogeneous completions. For one specific case, the Arps decline exponent is correlated to the heterogeneity of the completion. It is found that Arps’ decline exponent to be used after the end of linear flow increases with the heterogeneity of the completion. Finally, it is shown that ignoring the heterogeneity of the completion can have a great effect on the long-term forecast of these wells.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".