Application of compressive seismic imaging at Lookout Field, Alaska
Bibliographic record
Abstract
Abstract In 2015, a new seismic data set was acquired over the Lookout Field in Alaska in anticipation of development. The survey was acquired using principles of compressive seismic imaging (CSI). This was the first implementation of this technology on land for ConocoPhillips and was a test of its robustness in Arctic conditions. Although there were challenges specific to CSI in acquisition and processing, the test was a success. Since then, CSI design has been used for multiple surveys. The Lookout survey had general imaging objectives for the overburden and reservoir zone, including increased horizontal and vertical resolution, higher signal-to-noise ratio, and amplitude preservation. There were also specific objectives of a more accurate structural image beneath the Fish Creek Slumps, fault identification in the reservoir, and visibility of reservoir edges. Comparisons of the Lookout 2015 CSI data to the existing exploration-quality legacy data show an overall improved image and that the 2015 survey was successful in meeting the reservoir-specific objectives. CSI principles enable more efficient use of acquisition resources, providing the ability to acquire more seismic data than a high-density conventional seismic shoot while maintaining comparable data quality. The impact of CSI is unlocking development-grade seismic data at exploration-grade cost.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".