Miscible Gas Injection in Heterogeneous Carbonate Reservoirs with Extensive Baffles
Bibliographic record
Abstract
Abstract Miscible gas displacement is a promising enhanced oil recovery method in carbonate reservoirs technologically and economically. Though miscibility can be well understood using core-scale experimental and numerical models, the miscible displacement process in complex heterogeneous and multi-dimensional carbonate reservoirs is more complicated and needs further analysis. In this paper, we present our effort in probing the miscible gas displacement characteristics in a carbonate reservoir with baffles based on data from a real field. Using compositional modeling, we evaluate miscible gas injection process in heterogeneous carbonate reservoir with baffles. Together with the detailed miscible front development in specific parts of the reservoir, the analysis also takes advantage of gravity stabilization realized by implementing the corresponding injector-producer pattern. This paper presents an analysis of miscible gas displacement process, which can be considered as an extension from a simple one-dimensional model to more complex heterogeneous and multi-dimensional system. Using data from a real carbonate reservoir with baffles, we analyze the CO2 injection for the drive processes. The analysis is combined with the consideration of the effects of baffles and wettability. Mainly due to the multi-dimensional flow, our results show that the minimum miscibility pressure is higher than one-dimensional system. Besides, our results indicate gravity stabilization could be well maintained by proper injector-producer pattern. We also report the sensitivity analysis for wettability changes in carbonates. This paper offers a study to analyze and evaluate miscible gas flooding process in heterogeneous carbonate reservoirs. Using a real case data, the study can help reservoir engineers to better design miscible gas flooding for other similar situations.
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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 | 0.000 | 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 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".