Identification of a Minimum Dataset for CO2-EOR Monitoring at Weyburn, Canada
Bibliographic record
Abstract
CO 2 leakage at geological carbon storage (GCS) sites, driven by increased system pressure and higher CO 2 saturations, represents a major risk to secure containment of injected CO 2 . For long-term GCS monitoring, it is critical to determine a level of material information needed to minimize leakage risks while keeping costs under control. This study demonstrates a goal-oriented, retrospective design concept called minimum data set requirement (MDR) for the Weyburn-Midale Project (WMP), a commercial-scale, CO 2 -injection enhanced oil recovery site in Canada that has been extensively characterized for R & D purposes. More than a decade of research at the WMP site has led to an extensive collection of site characterization data (thousands of wells with geophysical logs and cores, seismic surveys), a situation that is unlikely to be true for many other GCS projects around the world. The main purpose of this study is to perform a retrospective design of the WMP to identify the MDR. By screening existing data retrospectively, our MDR identification process seeks to establish a level of data needed to define a sufficient reservoir model for guiding post-EOR monitoring, under user-defined performance metrics. Our starting point is an existing history-matched WMP reservoir model and three datasets consisting of logs from hundreds of wells and seismic survey. An iterative Monte Carlos approach is taken here to systematically and gradually reduce the level of information used in parameterizing a geological model, from which conditional stochastic realizations of model properties are generated and simplified reservoir models are developed. Results show that (a) the minimum dataset for predicting CO 2 migration depends on the heterogeneity and anisotropy of selected parameters of the field, (b) parameterization scheme for data reduction should be flexible and also objective oriented and problem dependent, and (c) for the Phase 1A area of the Weyburn field about 80% out of the 403 wells can be eliminated without having detrimental impact on the simulated pressure field.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".