Single Versus Multiwell Microseismic Recording: What Effect Monitoring Configureation Has On Interpretation
Bibliographic record
Abstract
Abstract Three monitoring wells with permanently installed 3C geophones were used to locate the microseismicity induced from a steam production cycle. This dataset of 52 high signal-to-noise ratio events were used to examine the location of events by progressively decimating the number sensor arrays. The three-array solutions were contrasted with different array combinations achieved by turning off one or two of the arrays. Event locations revealed the nature and magnitude of the limitations having incomplete coverage of the treatment zone. Most steam and fracture treatments are monitored by a single observation well. Parameters, such as stimulated reservoir volume fracture azimuth and fracture dimensions in treatments like CSS, hydraulic fracturing, SAG-D, are estimated from the distribution of microseismic event locations. By taking three array locations as ground truth, the array configurations that most accurately reflect the actual fracture geometry are determined. The observed distributions of the events relocated with decimated arrays show significant changes in overall fracture trend, geometry, and location accuracy. Progressive decimation of the number of arrays increases the inaccuracies of event locations, which results in the scatter of event locations. Decimation of the number of arrays changes the dimensions and azimuth of fractures, which are readily apparent upon comparison with the three-array solutions. The least biased decimated solutions are those using two arrays, one on either side of the treatment zone. Single array solutions show the most scatter, with the recording distance also controlling the degree of event mislocation. This array decimation analysis shows how the array configuration and number for arrays affect the interpreted fracture volumes, geometries, and accuracy of event location. The different viewing angles from the single and dual array decimated subsets, as well as the different distances from the event clusters to the geophones are important considerations in mitigating the biases due to limiting the recording coverage.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".