Full-waveform inversion using deviated well distributed acoustic sensing vertical seismic profiling data: A case study
Bibliographic record
Abstract
ABSTRACT With the development of fiber-optic seismology, distributed acoustic sensing (DAS) has made significant progress in vertical seismic profiling (VSP). The integration of full-waveform inversion (FWI) with DAS data acquired through well-bore optical fibers presents a promising frontier for subsurface characterization. Although the successful implementation of FWI can yield precise velocity models essential for reservoir monitoring and imaging, its application to DAS data has been limited predominantly to vertical wells. In such conventional approaches, the strain rate measurements from DAS are typically converted to vertical particle velocities at corresponding channel locations before applying standard FWI algorithms. However, this methodology faces significant limitations when extended to deviated wells, wherein the conversion to vertical particle velocity becomes inapplicable. Addressing this challenge, our study introduces a novel FWI strategy that enables the processing of DAS VSP data across various well configurations. The core innovation lies in the conversion of DAS measurements into scalar particle displacement, an approach that maintains the essential phase information while accommodating amplitude and frequency variations. Through the rigorous derivation of the relationship between DAS data and particle displacement in wavefields, we establish that the fundamental distinction between these measurements resides solely in their frequency components and relative amplitudes, with phase characteristics remaining intact. This displacement-based conversion method offers unprecedented flexibility, allowing for the application of conventional FWI to DAS data from deviated wells through appropriate amplitude adjustments based on wave velocity and incident angles at channel positions. We demonstrate this approach with a comprehensive set of DAS VSP data collected from an offshore inclined well, where the DAS data are directly fed into the FWI algorithm after amplitude scaling and simple preprocessing. FWI reduces data misfit and enhances velocity updates, and the inverted model provides an improved prestack depth migration image along with angle-domain common-image gathers.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".