Evaluation of Air Injection as an IOR Method for the Giant Ekofisk Chalk Field
Bibliographic record
Abstract
Abstract The Ekofisk fractured chalk reservoir located in the North Sea south-west of Norway has been exploited successfully for more than three decades, largely due to injection of sea water. In a study concluded in 2004, air injection was evaluated as a method for additional hydrocarbon recovery beyond the secondary waterflood recovery. Supported by the European Commission through the fifth framework program, the Ekofisk Field owners joined forces with leading European research institutes and a contractor to investigate the potential of air injection as a cost effective IOR method. Through screening studies, extensive laboratory experiments, reservoir simulations, design of processing facilities and project feasibility evaluations, an extensive knowledge base of the air injection process for light oil fractured reservoirs was established. In the present paper technical results will be presented. Recovery mechanisms related to an air injection process in a fractured light oil reservoir have been studied through laboratory experiments and reservoir modeling. The laboratory experiments verified air injection as a potential IOR method for a light oil fractured chalk field. Laboratory experiments were performed in order to study kinetic properties such as activation energies and ignition temperatures. In addition, diffusion coefficients were estimated through laboratory experiments and verified by numerical simulations. Potential weakening of the chalk due to heat and CO2 was evaluated based on laboratory experiments and geo-mechanical modeling. Combustion tube experiments were conducted in order to study propagation of the combustion front through porous media. Finally, a field scale air injection feasibility study was performed. The outcome of this study, including an evaluation of potential production benefits and main cost items involved in an air injection project, is presented.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 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 teacher head, 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".