First Downhole Application of Distributed Acoustic Sensing for Hydraulic-Fracturing Monitoring and Diagnostics
Bibliographic record
Abstract
Summary The first exploration-and-production downhole field trial of distributed acoustic sensing (DAS) fiber-optic technology was conducted during the completion of a tight gas well in February 2009. DAS is a novel technology that allows the detection, discrimination, and location of acoustic events on a standard telecom single-mode fiber several kilometers long. Using a combination of the measurement of backscattered light and advanced signal processing, the DAS interrogator system segregates the fiber into an array of individual microphones. To date, the technology has been applied mainly in the defense and security industries. One of the most exciting applications for downhole application of DAS is in the area of hydraulic fracturing of tight-sand and shale-gas reservoirs. Balancing the cost of hydraulic-fracture stimulation with the production benefit is crucial in tight-sand and shale-gas developments because, after drilling costs, the completion is the largest single cost component of the well. Recordings can be made while tools are run in hole, bridge plugs are set and perforations are shot and during the fracture-stimulation treatment. The technology is sufficiently reliable and sensitive to detect and monitor these in-well activities. The fidelity of the recordings made during hydraulic-fracturing and flowback operations provides a step-change improvement in the ability to perform real-time and post-job diagnostics and analyses of the stimulation. The different case studies presented in this paper will illustrate how, even in its earliest form, DAS has the potential to enhance the capability of monitoring and understanding in-wellbore activities. The technology enables the optimization of hydraulic-fracturing design and execution, which can drive down completion costs and lead to increased well productivity and ultimate recovery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".