Increasing Oil Recovery with CO2 Miscible Injection: Thani Reservoir, Abu-Dhabi Giant Off-Shore Oil Field Case Study
Bibliographic record
Abstract
Abstract This paper aims to study the miscibility features of CO2 miscible injection to enhanced oil recovery from Thani-III reservoir. A Comprehensive simulation model was used to determine multi contact miscibility and suitable equation of state with CO2 as a separate pseudo component using one of the industry’s standard simulation software. Experimental PVT data for bottom hole and separator samples including compositional analysis, differential liberation test, separator tests, constant composition expansion, viscosity measurements and swelling tests for pure CO2 were used to generate and validate the model. In addition to that, simulation studies were conducted to produce coreflooding and slimtube experimental models, which were compared with the conclusions drawn from experimental results. Results of this study have shown comparable results with the lab experimental data in regards to minimum miscibility pressure (MMP) calculation and recovery factor estimation, where the marginal errors between both data sets were no more than 7% at its worst. Results from this study are expected to assist the operator of this field to plan and implement a very attractive enhanced oil recovery program, giving that other factors are well accounted for such as asphaltene deposition, reservoir pressure maintenance, oil saturation, CO2 sequestering and choosing the most appropriate time to maximize the net positive value (NPV) and expected project gain.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".