Low Tension Gas Flooding as a Novel EOR Method: An Experimental and Theoretical Investigation
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Bibliographic record
Abstract
Abstract Low Tension Gas (LTG) flooding is a novel EOR process which can address challenging reservoir conditions such as high salinity, high temperature, and tight rock. Current process understanding is limited, and a joint experimental and modeling approach allows for both interpretation and insight into the complex interactions between the key process parameters of salinity gradient, foam strength, microemulsion phase behavior, and phase desaturation in order to achieve a physically correct and predictive process model. We performed a series of corefloods in high permeability Berea sandstones (~500 mD) to demonstrate the impact of salinity gradient on the LTG process and interactions between key mechanisms such as microemulsion phase behavior and foam stability. In order to provide additional insight into the experimental study and improve understanding of the LTG process, we used our newly developed LTG simulator which we built within CMG GEM. The results demonstrate that decreasing slug injection salinity can lead to a 15% increase in residual oil in place (ROIP) recovery over a slug injected at optimum salinity, with earlier breakthrough and steeper recovery slope. In addition, there is evidence of a late time pressure buildup as salinity is decreased through mixing with drive salinity which is indicative of increasing foam stability. This may be due to an inverse relationship between oil-water IFT and foam stability and thus designing an optimal salinity gradient for an LTG process requires balancing oil mobilization due to ultralow IFT and effectively displacing mobilized oil with adequate foam mobility control. We introduce and show the strength our compositional LTG simulator in a pioneering laboratory and simulation study that sheds light on the interaction between salinity, microemulsion phase behavior, and foam strength. Our conclusions indicate a significant departure from traditional ASP understanding and methodology when designing an LTG salinity gradient and serve as a foundation for future investigation.
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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 | 0.001 |
| 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 it