A Study of IOR by CO2 Injection in the Gullfaks Field, Offshore Norway
Bibliographic record
Abstract
Abstract This paper describes the feasibility study of a large-scale miscible CO2-WAG (MWAG) injection scheme in the Gullfaks Field, offshore Norway. We describe the reservoir engineering workflow and simulation techniques, the predicted production and injection profiles, and the main infrastructure solutions under consideration. Compositional cross-section models and recently available streamline-tracer simulation techniques are employed to scale up from element models to a fast, full-field simulator with a high degree of flexibility. The starting point for the workflow is a set of black oil and streamline front tracking models, history matched on coarse and fine grids. A fast, finely gridded streamline model is used to identify the MWAG injection targets, define injection well locations and completion strategy. Fine gridded cross-sections are extracted and used in a compositional simulator to study and quantify the miscible displacement process. These are the used to derive scaling parameters used in a simple, ultra-fast streamline-tracer model, scaling the MWAG process up to field level. The streamline-tracer model interactively optimises solvent allocation and generates production predictions on a well-by-well basis. Water flood recovery and incremental IOR are predicted simultaneously in a single simulation run. In addition, the general economic limitations and example technical solutions for implementation of a CO2 MWAG on the Gullfaks Field are briefly described.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".