Thief zone identification through seismic monitoring of a CO 2 flood, Weyburn Field, Saskatchewan
Bibliographic record
Abstract
Located in the Williston Basin (Figure 1) in Southeastern Saskatchewan, Weyburn Field implemented eight years ago a EOR project in order to maximize recovery from the fields main producing unit: a carbonate reservoir known as the Midale Beds. To date, Weyburn Field has produced 335 million barrels of oil and has an estimated 1.4 billion barrels OOIP (Davis and Roche, 2006). This paper demonstrates that Time‐Lapse and Multicomponent seismic data analysis is an effective tool for monitoring injection through the detection of changes in reservoir properties such as porosity, fluid distribution, and fracture density. The monitoring of these changes directly informs the design of the EOR project, thus optimizing field recovery. Evaluation of P‐wave Time‐Lapse and S‐wave data resulted in the following conclusions regarding production in Weyburn field: 1‐ The Midale Beds are experiencing a downward loss in in the west corner of the study area. Shifting the location of the nearby injection well is recommended. 2‐ Throughout the field, P‐wave time‐lapse shows that is largely confined to NW‐SE fracture orientation identified after interpretation of the 2000 S‐wave data.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".