EOR Potential in the Middle East: Current and Future Trends
Bibliographic record
Abstract
Abstract The majority of enhanced oil recovery (EOR) projects are being executed in the the U.S., Canada, Venezuela, Indonesia and China. The volume of oil produced by EOR methods increased considerably from 1.2 MMBD in 1990 to 2.5 MMBD in 2006 (Sandrea and Sandrea 2007). Current total world oil production from EOR is approaching 3 MMBD representing about 3.5% of the daily global oil production (Sandrea and Sandrea 2007). Thermal and CO2 methods are the major contributors to EOR production, followed by hydrocarbon gas injection and chemical EOR. Other more esoteric methods, e.g., microbial, have only been field tested, without any significant quantities being produced on a commercial scale. In recent years, the number of EOR projects has increased with escalating oil prices. The number of EOR projects in the Middle East (ME) has also increased over the past decade. In some countries like Oman, there has been no choice but to implement EOR projects aggressively due to dwindling "easy oil." Other countries in the region have also started to think EOR, and are including them in their strategic short-, medium- and long-term development plans. Furthermore, there are many projects on the drawing board and appropriate screening studies and EOR pilots are being pursued region-wide. This paper reviews the current ME EOR projects from full-field development to field trials, including those on the drawing board. The option of advanced secondary recovery (ASR) — also known as improved oil recovery (IOR) — technologies before full-field deployment of EOR is also discussed. A case is made that they are a better first option before deployment of capital-intensive EOR projects. The ME’s general drive towards "ultimate" oil recovery — instead of immediate oil recovery — is highlighted in the context of EOR. Some of the enablers for EOR in the ME are also discussed in the paper. It highlights the opportunities and challenges of EOR specific to the region.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".