Improved oil recovery using CO2 as an injection medium: a detailed analysis
Bibliographic record
Abstract
The main goal of any improved oil recovery (IOR) is to displace the remaining oil in a reservoir; it is achieved by improving the volumetric efficiency and enhancing the oil displacement. Carbon dioxide is considered to have high potential to improve the production efficiency of the reservoir. This process is gaining a lot of relevance these days as one of the best IOR techniques because when CO 2 dissolves in heavy oil, it reduces the oil viscosity, increases the oil swelling, improves the gravity segregation of oil and the internal drive energy. Consequently, this improves the oil recovery from the reservoir. Oil recovery using CO 2 is a win/win technique because it enhances the oil recovery and can be used as a CO 2 storage option in reservoirs to reduce the greenhouse gas levels in the atmosphere. In the present study, the reservoir simulation is used to predict the reservoir’s behavior using different production scenarios. A reservoir model is constructed using Eclipse and is used to optimize the well. The objective of this study is to enhance understanding of improved oil recovery for a typical reservoir located offshore on Australian continental shelf. The second part of this study focuses on carrying out an economic analysis of the best IOR scenario, with the maximum oil recovery, by analyzing key variables, such as oil prices, capital costs, operation and maintenance costs, CO 2 prices and taxes. The results obtained indicated that proper well optimization performed in high oil saturation areas using sensitivity analysis and optimizing the values of injection and production increases the oil recovery and maximum sweep of the reservoir. The economic analysis carried out on the chosen optimum scenario 4 was found to be very economical with total savings of $173 M.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".