Integration of borehole and seismic data to unravel complex stratigraphy : case studies from the Mannville Group, western Canada
Bibliographic record
Abstract
Understanding the stratigraphic architecture of geologically complex reservoirs, such as the heavy oil deposits of Western Canada, is essential to achieve an efficient hydrocarbon recovery. Borehole and 3-D seismic data were integrated to define the stratigraphic architecture and generate 3-dimensional geological models of the Mannville Group in Saskatchewan. The Mannville is a stratigraphically complex unit formed of fluvial to marine deposits. Two areas in west-central and southern Saskatchewan were examined in this study. In west-central Saskatchewan, the area corresponds to a stratigraphically controlled heavy oil reservoir with production from the undifferentiated Dina-Cummings Members of the Lower Cretaceous Mannville Group. The southern area, although non-prospective for hydrocarbons, shares many similarities with time-equivalent strata in areas of heavy oil production. Seismic sequence stratigraphic principles together with log signatures permitted the subdivision of the Mannville into different packages. An initial geological model was generated integrating seismic and well-log data Multiattribute analysis and neural networks were used to generate a pseudo-lithology or gamma-ray volume. The incorporation of borehole core data to the model and the subsequent integration with the lithological prediction were crucial to capture the distribution of reservoir and non-reservoir deposits in the study area. The ability to visualize the 3-D seismic data in a variety of ways, including arbitrary lines and stratal or horizon slicing techniques helped the definition of stratigraphic features such as channels and scroll bars that affect fluid flow in hydrocarbon producing areas. Small-scale heterogeneities in the reservoir were not resolved due to the resolution of the seismic data. Although not undertaken in this study, the resulting stratigraphic framework could be used to help construct a static reservoir model. Because of the small size of the 3-D seismic surveys, horizontal slices through the data volume generally imaged only small portions of the paleogeomorphologic features thought to be present in this area. As such, it was only through the integration of datasets that the geological models were established.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| 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".