Single Well Microseismic Monitoring Leveraging Hybrid Cable Combining Both DAS and Traditional Geophones
Bibliographic record
Abstract
Abstract The single monitoring well configuration is a favorable option for microseismic monitoring considering risk and cost. It has commonly been used in various industries for decades. When using a single monitoring well, we rely among other things on the waveforms’ polarization information to accurately locate detected microseismic events. Additionally, using a large array aperture reduces hypocenter's uncertainty. Instead of solely relying on 3C geophones to achieve such objectives, we propose to combine 3C sensors and distributed acoustic sensing (DAS) equipment. It is quite a cost-effective solution, and it enables us to leverage each system's strength while minimizing their respective limitations when considered individually. We present the technical feasibility of such a hybrid microseismic monitoring system using data acquired during a monitoring campaign performed in the Montney formation, Canada. In this dataset, the optic fiber (DAS) is located in the wireline cable used to deploy the 3C geophones; themselves located at the bottom of the DAS wireline cable. Though different acquisition systems are employed for the geophone array and the DAS array, both datasets are GPS time stamped so that data can be processed properly. We scan the DAS data using an STA/LTA event detection, and we integrate with the 3C geophone data. We find the microseismic waveform in both the DAS and the geophone sections and confirm the arrival times are consistent between DAS and geophones. Once datasets are merged, we determine hypocenters using a migration-based event location method for such hybrid array. The uncertainty associated with the event located using the hybrid DAS – geophone array is smaller than for any of the systems looked at independently thanks to the increased array aperture. This case study demonstrates the viability and efficiency of the next generation of a single well acquisition system for microseismic monitoring. Not only does it lower event location uncertainty, but it is also more reliable and cost-effective than the conventional approaches.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".