Uncertainty Quantification Through the Assimilation of CO2 Plume Size from 4D Seismic Survey
Bibliographic record
Abstract
Abstract Geological models of saline aquifers used for CO2 storage inherently involve uncertainties due to limited data. This requires innovative approaches to quantify the impact of geological uncertainties on CO2 plume size and monitoring strategies. To address this issue, data assimilation and history matching have been widely employed. These approaches use diverse measurement, monitoring and verification (MMV) data such as pressure measurements, saturation logs, and surface monitoring data to reduce uncertainties associated with simulations. However, in carbon storage, 4D time-lapse seismic images are crucial and can provide valuable input for assessing uncertainties in CO2 storage models by providing estimates of CO2 plume migration at certain time intervals. In this study, a methodology is proposed to quantify the uncertainties in geological models for CO2 storage by the assimilation of CO2 plume size data derived from 4D seismic images taken at different injection periods. To consider a wide range of uncertainties, data-driven proxy models are developed using high-fidelity coupled reservoir-geomechanics simulations data to overcome the prohibitive computational issues on numerous realizations (>1000). The trained proxy models are used to forecast the CO2 plume size at multiple time intervals for a large sample of newly generated geomodels. A sample rejection procedure is implemented to quantify uncertainty and filter consistent, or history-matched geological realizations. The proposed workflow is implemented for an existing geological CO2 storage site in Western Canada. The proxy model is not only capable of predicting CO2 plume evolution with high accuracy but also shows a notable computational time reduction. A considerable reduction in geological model uncertainty is achieved using the proposed methodology. Among the 10,000 geological realizations, only 926 realizations are accepted as posterior models. The uncertainty quantification method proposed in this study effectively addresses geological model uncertainties based on available seismic survey and provides valuable insights into consideration of the geological uncertainty in CO2 storage modeling and design of MMV program for CO2 storage projects.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".