Experimental Investigation of Hydrocarbon Gas Huff-N-Puff Injection into the Live-Oil Window of Eagle Ford
Bibliographic record
Abstract
Abstract The hydrocarbon gas huff and puff (HnP) technique has been used to improve oil production in unconventional oil reservoirs where excess capacity of produced gas is available and hydrocarbon prices are in a range to result in an economically viable case. Eagle Ford (EF) is one of the largest unconventional oil plays in the United State where HnP has been applied for enhanced oil recovery (EOR) at reservoirs within various oil windows. Our previously published Huff-n-puff results on dead oil with produced gas from Eagle Ford (EF) showed the recovery factor of hydrocarbon varying from 40 to 58%. The objective of this paper is to extend the experiments to live oil with EF core plugs to investigate the mechanisms of HnP which are affected by the composition of injected gas and resident oil, injection and soaking time as well as injection/depletion pressure gradient. Eagle Ford live oil and natural gas produced from the target area were used for HnP tests. Four representative core plugs were used with the tests conducted at reservoir conditions (125 °C and 3,500 psi). The live oil experiments with four reservoir core plugs showed an improvement in oil recovery with recovery factor (RF) varying from 19.5 to 33 % in six cycles of HnP, whereas the primary depletion on the same core plug showed RF below 11 %. A lower recovery factor of HnP from live oil saturated core in this study was observed as compared to dead oil saturated core reported in a previous publication. It is attributed to a lesser diffusion effect on mass transfer between injected gas and resident oil when the core is saturated with live oil. This behavior is displayed by the pressure decline curve during the first soaking period. A sharper diffusion pressure decline occurred in the dead oil saturated core plug where a higher concentration gradient between injected gas and resident oil drives a faster gas transport into the oil due to the molecular diffusion during the soaking period.
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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) | 0.001 | 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".