Porosity prediction from seismic attributes of the Ordovician Trenton-Black River groups, Rochester field, southern Ontario
Bibliographic record
Abstract
Abstract This article integrates three-dimensional (3-D) seismic attributes and log data to determine porosity distribution of the Ordovician Trenton-Black River groups within the Rochester field, southern Ontario. The rocks are composed of tight limestone, parts of which were dolomitized to form porous reservoir rock. Previous studies of the Trenton-Black River dolomite reservoirs have indicated a close relationship between faulting and reservoir development, but few published studies have attempted to examine these relationships using 3-D seismic data. This study explores the stratigraphy and structure of the Rochester fault-related dolomite reservoir using 3-D seismic data and neural networks to predict porosity. By predicting porosity using seismic attributes, vertical and lateral distributions of porosity that can be used to guide development and exploration drilling for optimal hydrocarbon recovery were obtained. The sites of highest porosity were found to be along and within the fault zones. Faults extending from the basement into overlying Paleozoic rocks are composed of several short-plane, vertical, and subvertical fault segments. However, some of these faults appear to have originated and died within the Paleozoic rocks; they cannot be traced to the basement because of little or no offset where they penetrate the basement. Although the five identified attributes are considered important in exploration for fault-related dolomite reservoirs, the single most important attribute to employ is the amplitude envelope because the other attributes are mathematically related to it. Furthermore, the sags that are seen in the Rochester field are due to the combined effects of low-velocity pushdown and faulting. Methods and results presented in this study can be used to explore and develop fault-related dolomite reservoirs elsewhere in similar geologic settings.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".