First Downhole Application of Distributed Acoustic Sensing (DAS) for Hydraulic Fracturing Monitoring and Diagnostics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The first E&P downhole field trial of Distributed Acoustic Sensing (DAS) fibre optic technology was conducted by Shell Canada 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 fibre of several kilometres in length. Using a combination of the measurement of backscattered light and advanced signal processing, the DAS interrogator system segregates the fibre into an array of individual "microphones". To date the technology has been mainly applied 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 versus the production benefit is crucial in a tight sand and shale gas developments as, after drilling costs, the completion is the largest single cost component of the well. Recordings were made while tools were run in hole, bridge plugs set, perforations shot and during the fracture stimulation treatment(s). The technology proved sufficiently reliable and sensitive to detect and monitor these in-well activities. The fidelity of the recordings made during hydraulic fracturing and flow back operations, provided a step-change improvement in the ability to perform real-time and post-job diagnostics & 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 could drive down completion costs and lead to increased well productivity and ultimate recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it