Case study of hydraulic fracture monitoring using multiwell integrated analysis based on low-frequency DAS data
Bibliographic record
Abstract
Abstract Distributed acoustic sensing (DAS) is an effective technique for hydraulic fracture monitoring. It can potentially constrain fracture propagation direction and time while monitoring strain perturbation, such as stress shadowing. In this study, we acquired passive DAS and distributed temperature sensing (DTS) data throughout the entire fracturing operations of adjacent production wells with varying offset lengths from the fiber-optic cable in the Montney tight gas area. We applied data processing techniques to the DAS data to extract low-frequency components (less than 0.5 Hz) and to construct the strain rate and cumulative strain maps for detecting responses related to fracture hits along the fiber-optic cable. We used low-frequency DAS (LF-DAS) results to estimate the fracture hit position and time, and in certain cases, to additionally estimate the fracture connection. By integrating LF-DAS results with DTS results, we detected the temperature changes around the compression response near the fracture hit position and time. Furthermore, we observed that timing of the fracture hit can be constrained more precisely by using high-frequency DAS data (greater than 10 Hz). We estimated the fracture propagation direction and speed from the estimated fracture hit position and time. The fracture propagation direction deviated slightly from a perpendicular line to the fiber direction. In addition, as estimated from the first fracture hit time, the fracture length and fluid injection volume had a proportional relationship. Due to challenges associated with the data, it is important to design data acquisition geometry and fracturing operations on the premise of acquiring LF-DAS data. It is also important to apply an additional noise reduction process to the 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".