Seismic reservoir characterization of the Gassum Formation in the Stenlille aquifer gas storage, Denmark — Part 1
Bibliographic record
Abstract
Abstract Seismic reservoir characterization plays an important role in carbon capture and storage analysis. The Havnsø anticlinal structure in Denmark is a prospective CO2 storage site due to its proximity to two large emission sources—a coal-fired power station and a nearby refinery. Although legacy 2D seismic lines over the area outline the anticlinal structure, their quality is insufficient for quantitative interpretation. Earlier studies have shown that the natural gas stored in the Stenlille aquifer exhibits a seismic response similar to the modeled CO2 fluid in the Havnsø structure. Thus, seismic reservoir characterization carried out on the Stenlille aquifer gas storage in terms of identifying spatial distribution of gas and outlining faults would provide insight regarding value addition that seismic data can bring into the proposed CO2 storage at Havnsø. Using the available poststack seismic data, we apply an integrated reservoir characterization analysis. After performing the adequate data conditioning, the impedance of the target Stenlille Formation is estimated through generation of an accurate low-frequency model. Thereafter, multiattribute analysis was used to generate volumetric estimates of porosity, gamma ray, and water saturation within the target formation so that the spatial distribution of gas can be mapped. The resulting porosity and gamma-ray volumes indicate encouraging results and were used for Bayesian classification to predict the probability of the more important lithofacies, namely, sand, shale, moderate-porosity sand, and moderate-porosity shaly sand, which enabled the mapping of high-porosity/facies zones in the two aquifer storage levels. Independently, we make use of unsupervised machine learning applications for seismic facies prediction and compare them at the two storage levels, which will be presented in the part 2 of this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".