Adapted sequential Gaussian simulations with Bayesian approach to evaluate the CO<sub>2</sub> storage potential in low porosity environment
Bibliographic record
Abstract
Sequential Bayesian simulations are used to model the porosity distribution and assess the CO2 storage potential in the Beauharnois Formation of the Saint‐Flavien reservoir (Québec, Canada). The low porosity Beauharnois Formation is characterized by a complex geology, mostly composed of dolostones with a strong presence of limestone, sandstone, and shale. In such a complex geological environment, we transform the porosity distribution into a normal one to artificially stretch the range of porosity. This allows a clearer definition of the statistical relation between acoustic impedance (AI) and porosity, and a better identification of petrophysical families in the reservoir unit. Guided by seismic derived AI cubes, 250 realizations of porosity are simulated by Bayesian sequential simulations (BSS), all respecting the initial porosity well logs, the a priori porosity distribution, and the statistical relation between AI and porosity. All realizations present different but realistic distributions of porosity. We estimate the connectivity between zones with porosity greater than 1.0%. The average porosity in the connected pockets is approximately 1.4% for all three selected realizations. We estimate 0.5Mt to 1.25Mt of CO2 could be injected in the 3D model representation of the Beauharnois Formation in Saint‐Flavien, with a CO2 storage efficiency factor of 27% to 36%.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".