Effect of Well Interference on Shale Gas Well SRV Interpretation
Bibliographic record
Abstract
Abstract The stimulated reservoir volume (SRV) estimated from daily production rate and pressure is a vital parameter for appraising shale gas wells’ fracturing effect and production potential. However, when well interference occurs, the SRV estimation from rate-normalized pressure (RNP) analysis is compromised. This paper illustrates diagnosis of well interference and how it affects SRV calculation. China is the third country to exploit the shale gas technology breakthrough after the United States and Canada. The Jiaoshiba shale gas play is the most successful shale gas reservoir in China with some wells’ cumulative production over 0.1 billion cubic meters in the first year. Production rate data has shown jumps in water production during hydraulic fracturing of neighboring wells. By combination of hydraulic fracturing process and production data, we detect the existence of well interference from the adjacent well, when well interference happens and the influence it imposed on the target well. We analyzed two pairs of target and neighboring shale gas well pairs using the RNP and its derivative. The log-log diagnostic plots for nearly all of the wells see unit slope, indicating boundary dominated flow within 1 year. Some wells see two unit slopes possibly indicating a change in the SRV after hydraulic fracturing in a neighboring well. Well interference may be caused by interaction between primary hydraulic fractures and/or secondary natural fractures activated during hydraulic fracturing. Interwell interference has had a significant influence on the SRV interpretation. Well interference has drawn people's attention in recent years, but its impact on SRV interpretation is rarely reported. This research may help to characterize shale gas's SRV and related parameters and to optimize well spacing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".