Distributed Acoustic Sensing for Hydraulic- Fracturing Monitoring and Diagnostics
Bibliographic record
Abstract
This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 140561, ’First Downhole Application of Distributed Acoustic Sensing for Hydraulic-Fracturing Monitoring and Diagnostics,’ by M.M. Molenaar, SPE, Shell; D.J. Hill, QinetiQ OptaSense; and P. Webster, E. Fidan, SPE, and B. Birch, SPE, Shell, prepared for the 2011 SPE Hydraulic Fracturing Technology Conference and Exhibition, The Woodlands, Texas, 24-26 January. The paper has not been peer reviewed. Distributed acoustic sensing (DAS) is a fiber-optic technology that enables detecting, discriminating, and locating acoustic events with a standard telecom single-mode fiber several kilometers in length. Use of a combination of the measurement of backscattered light and advanced signal processing enables the DAS interrogator system to segregate the fiber into an array of individual “microphones.” One downhole application of DAS is monitoring hydraulic fracturing of tight-sand and shale-gas reservoirs. Introduction Fracture monitoring is key to understanding and optimizing hydraulic-fracturing treatments. The diagnostics focus on determining stimulation effects such as fracture geometry, proppant placement in the fracture, and fracture conductivity. Historically, completion engineers have been limited to the use of surface wellhead rates and pressures, and occasionally downhole pressures, as their main sources of real-time information. To address such limitations, Shell Canada has been deploying distributed-temperature-sensing (DTS) systems in tight-sand and shale-gas wells for hydraulic-fracturing diagnostics. The passive nature of fiber-optic sensors allows intervention- and interference-free operation. The inherent long-term reliability of such sensors, combined in a downhole-deployable single optical-fiber cable, makes fiber-optic technology an effective platform for permanent sensing in producing wells. DAS technology has potential for improvements in management of assets across the well’s life cycle, from exploration to operation. Measuring Wellbore Acoustic Disturbances at the Surface Surface Interrogator System. The DAS interrogator unit transforms a standard telecom (single-mode) fiber of sever-al kilometers in length into an array of microphones. This works because the interference of back-reflected laser light is affected by acoustic disturbances along the optical fiber. The technology can be deployed in new wells or used in most existing wells in which standard telecom optical fibers have been deployed for DTS purposes. The DAS system uses a technique called coherent optical time-domain reflectometry, which involves the successive transmission of short pulses of highly coherent light down an optical fiber, and the observation of the very small levels of back-scattered signal caused by nonhomogeneities in the glass core. The system relies on sensing of vibro-acoustic disturbances in the vicinity of the fiber-optic cable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".