Multiwell Analysis of Multifractured Horizontal Wells in Tight/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. Long horizontal wells completed with multiple-fracturing stages (MFHW) are the most popular method for exploiting shale gas reservoirs today and therefore, development of analysis methods for analyzing production data from these wells has gained tremendous attention in the last decade. The analysis methods developed so far are aimed to obtain understanding of fracture length, fracture conductivity, stimulated reservoir volume (SRV), contacted gas-in-place and other information for a MFHW being analyzed. Although single well analysis methods are of tremendous value, the industry also needs analysis methods for analyzing a group of MFHWs. In this paper, analysis methods developed for single well analysis of MFHWs are extended to analyze a group of MFHWs. These analysis methods are proved to be very useful for cases that adjacent wells are in communication (ex. fracturing one well affected the production of the adjacent wells). It is shown how these methods help engineers to diagnose and characterize the communication between MFHWs and use the results to optimize the size of frac job and spacing between horizontal wells in tight/shale gas plays.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".