Complementary Surveillance Microseismic and Flowback Data Analysis: An Approach to Evaluate Complex Fracture Networks
Bibliographic record
Abstract
Abstract This paper presents an integrated workflow which complementarily utilizes flowback data analysis and surveillance microseismic analysis to characterize fracture networks and stimulated reservoir volume (SRV). The workflow helps to 1) differentiate !"effective" and "ineffective" SRV and fracture half-length (ye) respectively, 2) understand how effective fracture volume (Vf) changes during flowback, and 3) explore the effects of key operational parameters on the fracture network created after well stimulation. The workflow comprises four main steps: 1) estimating SRV and fracture parameters from surveillance microseismic interpretation and flowback data analysis; 2) comparative analysis of estimated SRV, ye and Vf values; 3) Calculating volumetric ratios (e.g. flowback load recovery) to evaluate the effectiveness of fracturing and flowback operations; and 4) investigating possible relationships between operational designs and estimated reservoir and fracture parameters. The application of this workflow on an eight-well pad completed in the Horn River Basin (HRB) shows that the SRV and ye from microseismic interpretation are generally several times larger than those from flowback data analysis. This indicates that over half of the stimulated rock does not contribute to gas production. Besides, a large percentage of the effective fracture network closes during early-time (the first 200 hrs) flowback. The results show that SRV increases as total perforation clusters increases, and this relationship appears to be formation-dependent. Also, the estimated Vf seems to be smaller for wells that are opened later for flowback. This observation might be due to inter-well communication and wellbore storage effects.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".