Fiber Optic Distributed Acoustic Sensing of Multiple Fractures in a Horizontal Well
Bibliographic record
Abstract
Abstract This paper highlights the current state of fiber optic distributed acoustic sensing (DAS) technology by reviewing its application to hydraulic fracture diagnostics in a multi fractured horizontal well (MFHW). It will be shown that, with the advent of DAS, a gap in the feedback — which could previously occur using various hydraulic fracture diagnostic options — has been filled. Results are shared that were obtained from the first successful application of high resolution DAS during the placement of multiple hydraulic fractures in a horizontal well that was completed with an open hole packer and frac valve system. Observations of the real time acoustic soundfield in the near wellbore (NWB) region during hydraulic fracturing are presented as high resolution images. These images have enabled an analysis of key dynamic aspects of the fracturing process. In examining the resultant data, it has become apparent that DAS has overcome some limitations intrinsic in other diagnostic tools such as distributed temperature sensing (DTS), microseismic monitoring, and tracer programs. An overview of the well design is provided as well as selected samples from the dataset which highlight some of the events that were observed during the hydraulic fracturing process. Sample images are used to demonstrate the current capability of DAS measurement, selected both from the real time soundfield display (SFD) and from processed high resolution soundfield maps. DAS processing methods are briefly discussed, as well as two categories of field observations— which highlight some of the mechanical reliability aspects of the swell packer/ball actuated frac sleeve system, as well as some aspects of the near wellbore region during hydraulic fracturing such as single or multiple fracture initiation sites and the general behaviour of wellbore fluids over the course of the fracture treatment. Distributed acoustic sensing using a single mode optic fiber has been described in recent literature (Molenaar, 2011) for applications involving the recording of acoustic events during various stages of well completion and stimulation. The current paper provides further description on how DAS works and shares results from a high resolution DAS survey, obtained while placing multiple hydraulic fractures in a horizontal well, completed in a tight sand using an openhole ball actuated valve system with swell packers for fracture isolation. Earlier findings are supported, in particular that DAS will enable an improved understanding of in-wellbore activities and, in so doing, that it will enable optimization of hydraulic fracturing design and execution. It is recognized that much is yet to be learned in the processing of fiber optic DAS data, but also that it would be beneficial to share the work that has been completed to date to facilitate accelerated development of DAS processing technology.
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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".