Keynote Presentation: Microseismic Data Integration: How Connecting the Dots can Help Solve the Unconventionals Puzzle
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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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.078 | 0.027 |
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".