Hydraulic Frac-Hit Height and Width Direct Measurement by Engineered Distributed Acoustic Sensor Deployed in Far-Field Wells
Bibliographic record
Abstract
Abstract Hydraulic fracture height and width are key parameters for completion design and evaluation of unconventional resources. Traditional measurement technologies like microseismic, tilt-meters, chemical or radioactive tracers, pressure temperature gauges, etc. either have low resolution or rely on sensitive models which can cause a high degree of uncertainty. Recent frac-hit measurement methods include the use of Distributed Acoustic Sensing (DAS) deployed in far-field wellbores offset from the treatment well. The DAS system directly measures the fracture propagation every second through the completion, with a few meters' spatial resolution sampled at 0.25-meter intervals along the monitored fiber well. Cross-well fiber optic monitored far-field strain (FFS) results suggested elastic stress effects, intense inelastic fracture expansion and closure events which provide identification and measurement of frac height on a TVD plot, as well as width by measured depth along the wellbore laterals. Vertical wells or the heel-section of horizontal wells are suitable for frac-hit height (FHH) measurement; fracture azimuth and wellbore geometries need to be considered for precise evaluation. A specially designed engineered constellation fiber cable was tested and utilized in this method in combination with a true phase coherence DAS interrogator with a 20 dB improved sensitivity (Signal-to-Noise-Ratio (SNR)) for both low and high frequency ranges DAS. The optic fiber can be either permanently installed outside the casing or temporarily deployed inside a monitor well. Comparable results can be achieved by the engineered fiber system and have been presented within case studies for both horizontal and vertical well sections. In addition, Distributed Temperature Sensing (DTS) and data from downhole gauge can confirm any temperature or pressure changes resulting from frac driven interactions (FDI). With this approach, fracture azimuth, frac-hit corridor (FHC) width and FHH can be determined with a high degree of accuracy and resolution. Completion engineers were able to optimize frac models in real-time and further change completion schedules during the frac treatment.
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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 | 0.000 | 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".