Bibliographic record
Abstract
Abstract Chemical flooding is one of the classical EOR methods, together with thermal methods and gas injection. It is not a new method; indeed, the first polymer flood field pilots date back to the 1950s while the first surfactant-based pilots can be traced back to the 1960s. However, while both gas injection and thermal methods have long been recognised as field proven and are being used at a large scale in multiple fields, it is not the case for chemical EOR. Although there have been over 500 polymer flood pilots recorded, and almost 100 surfactant-based field tests, large scale field applications are few and far between. This situation seems to be evolving however, as more and more large scale chemical projects get underway. This paper proposes to review the status of chemical EOR worldwide to determine whether it is finally coming of age. The status of chemical EOR projects worldwide will be reviewed, focusing on recent and current large-scale field developments. This will allow to establish what is working and where the industry is still encountering difficulties. This review will cover North America, South America, Europe, the Middle East, Asia and Africa. It is clear that polymer flooding is now indeed becoming a well-established process, with many large-scale projects ongoing or in the early stages of implementation in particular in Canada, Argentina, India, Albania and Oman in addition to China. Strangely enough, the US lags behind with no ongoing large-scale polymer flood. The situation is more complex for surfactant-based processes. At the moment, large-scale projects can only be found in China and – although to a lesser extent – in Canada. The situation appears on the brink of changing however, with some large developments in the early stages in Oman, India and Russia. Still, the economics of surfactant-based processes are still challenging and there is some disagreement between the various actors as to whether surfactant-polymer or alkali-surfactant polymer is the way to go. This review will demonstrate that polymer flooding is now a mature technology that has finally made it to very large-scale field applications. Surfactant-based processes however, are lagging behind due in part to technical issues but even more to challenging economics. Still there is light at the end of the tunnel and the coming years may well be a turning point for this technology.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".