Seismic-based porosity prediction in the Silurian Niagaran Formation reefs of Northern Michigan: An integrated case study
Bibliographic record
Abstract
Summary Identifying Lower Silurian Niagaran (Guelph) Formation reefs with 3D seismic in the Michigan Basin has been very challenging from the onset of the use of this technology. Gaining experience in recognizing diagnostic seismic signatures and refining processing flows has led to the ability to define these structures far more reliably. While production can be very prolific, the heterogeneity encountered within individual reefs has led to many poor producers and dry holes due to lack of primary porosity, compartmentalized reservoirs, and/or salt occlusion. The objective of this study was to integrate seismic and geology to better predict porosity distribution. A combination of near angle (0-15°) stacks and high resolution processing was used to create a seismic volume in which various attributes were correlated with well porosities. Coherence, spectral decomposition, wavelet analysis, and Rock Solid Attributes were cross plotted against the well properties. Areas identified as having porosity from each analysis were combined to generate predicted distribution of porosity across the reef. A horizontal well was subsequently drilled into this predicted porosity to test this hypothesis, with successful results. The same methodology will be further tested on a different reef. Seismic acquisition has been completed and processing is underway. Drilling is anticipated to be completed prior to October, 2014.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".