Keynote Presentation: Microseismic Data Integration: How Connecting the Dots can Help Solve the Unconventionals Puzzle
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
Summary Unconventional reservoirs are generally developed using hydraulic fracturing. Having a good understanding of the hydraulic fracture characteristics helps in optimally and efficiently developing the reservoir. Microseismic monitoring has proven to be a valuable technique to monitor hydraulic fracturing operations. During the hydraulic fracture treatment fluid is injected in the reservoir and cracks form, which results in the occurrence of microseismic events. The monitoring and interpretation of this microseismic events can lead to a better understanding of the hydraulic fracture characteristics. Microseismic monitoring of hydraulic fracturing is generally used to assess the fracture parameters like hydraulic fracture height, length, orientation, and complexity. However, it is a challenge to retrieve information like effective (producing) fracture parameters and hydraulic fracturing efficiency. Besides, the value of information from microseismic would become larger when it can be used to go beyond retrospective analysis, and can help to facilitate the prediction of the hydraulic fracture behavior. In order to solve this unconventional puzzle and to maximize the learnings from microseismic data, it is required to evaluate this microseismic data along with other sources of data.
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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 | 0.001 | 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 it