Field Cases of Hydraulic Fracture Stimulation Diagnostics Using Fiber Optic Distributed Acoustic Sensing (DAS) Measurements and Analyses
Bibliographic record
Abstract
Abstract Deciding on the optimum spacing between fractures and selecting the optimum fracture treatment parameters is a key challenge in designing the hydraulic fracture stimulations of Unconventional Gas and Liquid Rich Shale (UGLRS) wells. To make those decisions more effectively and more rapidly, (downhole) hydraulic fracture diagnostic tools can be used which provide a better understanding of how and where fractures initiate and what the distribution of fluid and proppant volume is downhole. One emerging technology, fiber optic distributed acoustic sensing (DAS) has the potential of providing such key diagnostic insights during hydraulic fracturing operations in real-time. This paper describes some of the background technology and presents the results of several hydraulic fracture stimulation (HFS) diagnostic case studies. The results illustrate how DAS has been used to perform real-time monitoring for both open-hole multi-stage fracturing and "Cemented Plug & Perf Completions". DAS has provided valuable insight as to the stimulation effectiveness. The technique has also provided insights into effective zonal isolation when using mechanical isolation during the hydraulic-fracturing process that would otherwise not have been possible. It also complements other HFS diagnostic technologies (e.g. tracers, micro-seismic, distributed temperature sensing (DTS), production logs (PLT)). DAS monitoring of hydraulic fracture stimulation can help accelerate the learning curve and drive performance improvements. Installation of fiber optic cables early in a field's life or when entering a new geological/geo-mechanical situation can allow for accelerated optimization of future wells.
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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) | 0.001 | 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 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".