A Review of Early Opportunity-Analysis on CO 2 Sequestration and Enhanced Oil Recovery for Iran
Bibliographic record
Abstract
In recent years, greenhouse gases (GHGs) such as carbon dioxide have increased in the atmosphere and caused some concerns about climate change. The table published by International Energy Agency shows that from 1990 to 2007, Iran after China has had the highest rate of increase in carbon dioxide emission. In 1990, Iran produced a total of 175 million tons of carbon dioxide to the atmosphere while in 2007 this rate has reached to 466 million tons. Geological sequestration is one way to reduce the CO 2 content in the atmosphere. There are several options for sequestrating CO 2 in geological sinks. Mature oilfields are one of the most favorable targets for the CO 2 sequestration. Injecting CO 2 into these reservoirs can increase the amount of oil produced in addition to offsetting some of the CO 2 storage expenses. Most of the CO 2 injection aspects into the reservoirs for the purpose of Enhanced Oil Recovery have been known for decades. The economics and incentives for combined EOR and sequestration process are less clear at this time, but a first step in the development process should be to do studies in order to investigate ways for both producing oil efficiently and maximizing storage of the carbon dioxide. This study looks at such scenarios that reduce the CO 2 emissions using the existing oil reservoirs as sink. The goal of this research is to better understand the potential for simultaneous enhanced oil recovery and CO 2 sequestration in oil reservoirs over a range of conditions. Key words : CO 2 sequestration; Enhanced Oil Recovery; Iran
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".