Workflow using sparse vintage data for building a first geological and reservoir model for CO<sub>2</sub> geological storage in deep saline aquifer. A case study in the St. Lawrence Platform, Canada
Bibliographic record
Abstract
Abstract Among all geological CO2 storage possibilities, deep saline aquifers are of great interest due to their worldwide repartition and their important storage volume. We present a workflow using available vintage data with poor 2D seismic coverage for building a first geological and reservoir model for CO2 geological storage in the deep saline aquifers of the St. Lawrence Platform in the Bécancour area (Québec, Canada). In order to optimize the sparse available geoinformation using a geostatistical method, we krige the tops of the geological formations recorded at 11 wells using surfaces modeled from seismic horizons picked on 99.4 line‐km of 2D seismic reflection data. Modeled geological horizons show a good compromise between the geometric structure expressed by the variograms and the interpreted variations evaluated from seismic horizons. Using available well logs, distribution of porosity and permeability are computed for generating multiple realizations of the petrophysical properties of the targeted aquifer by sequential Gaussian simulations. The scarcity of available petrophysical data in the targeted aquifer generates high variability between the different realizations. Due to this uncertainty, the population of the 3D geological model with petrophysical properties that are required for further geostatistical simulations of CO2 injection do not allow to achieve reliable results. The methodology presented in this paper shows the possibilities and limits of using vintage data, and provides evidence that geophysical data acquired in a 3D fashion are important to fully characterize a reservoir for CO2 geological storage. © 2012 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".