Seismic detection of fractures from injection: A field example
Bibliographic record
Abstract
Detection of fractures using geophysical methods has proven elusive. A significant amount of theory has been developed, however, convincing applications of the theory to observed data have been lacking. In this paper we show a clear example of seismic amplitude changes due to controlled fracturing from injection at the Alpine field in Alaska. We use the ability of time-lapse seismic data to remove the background medium so that seismic amplitude changes due to the creation of open fluid (gas or liquid) filled fracture systems are clearly exposed around injectors. Additionally we utilized rock-model-based seismic forward modeling to test against three different models of fractured media. We conclude that the Kuster-Toksoz randomly distributed fracture model is the most appropriate for Alpine. Using this model we are able to calibrate the fracture parameters (fracture pressure, fracture aspect ratio, and fracture porosity) to match observed data. Our results show that fracturing can introduce 4D velocity changes significantly larger than what would be expected from using velocity-pressure trends from core measurements alone. Finally, using time-lapse AVO modeling we show that at Alpine it may be possible to discriminate between gas- and oil/water-filled fractures. It is more of a challenge to discriminate oil- and water-filled fractures or to invert separately for fracture aspect ratio and fracture porosity.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".