Velocity Estimation Below the Well Bottom by using FWI: Application to Walkaway Synthetic Seismic Data
Bibliographic record
Abstract
Summary Fluid overpressures in porous media, are systematically a risk for drilling. The estimation of the P-wave velocities below the well bottom is one of the possible ways to detect such overpressures. Borehole seismic data has already been used for velocity estimation, then for overpressure prediction. In this paper, we propose to apply the full-wave inversion technique to synthetic walkaway data. We first analyze the sensitivity of the full-wave inversion results with respect to different acquisition geometries by considering synthetic inversion 2D experiments. Then, we study the reliability of the estimation when adding coherent noise to the synthetic data. The main conclusions are that the non-linear full-wave inversion is reliable for P- and S-wave estimations. The robustness against noise or complex rheology effects can be improved using the polarization constrained in the inversion. If anisotropic or viscoelastic effects are present in the data, a correct guess for the starting model is required. The full wave inversion is able to find the correct trend for Thomsen and Q-factors parameters. However, the estimation of the compressive Q-factor in the target layer below the well bottom in order to better constrain the pore pressure prediction seems difficult to achieve presently.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".