Petroleum reservoir characterization using downhole microseismic monitoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Imaging of microseismic data is the process by which we use information about the source locations, timing, and mechanisms of the induced seismic events to make inferences about the structure of a petroleum reservoir or the changes that accompany injections into or production from the reservoir. A few key projects were instrumental in the development of downhole microseismic imaging. Most recent microseismic projects involve imaging hydraulic-fracture stimulations, which has grown into a widespread fracture diagnostic technology. This growth in the application of the technology is attributed to the success of imaging the fracture complexity of the Barnett Shale in the Fort Worth basin, Texas, and the commercial value of the information obtained to improvecompletions and ultimately production in the field. The use of commercial imaging in the Barnett is traced back to earlier investigations to prove the technology with the Cotton Valley imaging project and earlier experiments at the M-Site in the Piceance basin, Colorado. Perhaps the earliest example of microseismic imaging using data from downhole recording was a hydraulic fracture monitored in 1974, also in the Piceance basin. However, early work is also documented where investigators focused on identifying microseismic trace characteristics without attempting to locate the microseismic sources. Applications of microseismic reservoir monitoring can be tracked from current steam-injection imaging, deformation associated with reservoir compaction in the Yibal field in Oman and the Ekofisk and Valhall fields in the North Sea, and production-induced activity in Kentucky, U.S.A.
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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 | 0.000 | 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 it