Using Natural Gas Liquid for EOR in a Huff-N-Puff Process – A Feasibility Study
Bibliographic record
Abstract
Abstract This is a feasibility study investigating the application of natural gas liquid (NGL) in a Huff-n-Puff process for enhanced oil recovery from unconventional tight-oil reservoirs. We use a state-of-the-art high-pressure and high-temperature visualization cell to capture real-time NGL-oil interactions throughout the experiment, both in bulk-phase conditions and in the presence of a core sample. We utilize an ultratight Eagle Ford shale sample extracted from horizontal section of a wellbore. The experiments are conducted at a reservoir pressure and temperature of 3,200 psig and 133℃, respectively with NGL being injected at a liquid state. Our findings indicate the notable solubility of NGL in oil, primarily due to NGL's intermediate hydrocarbon components. During the soaking stage, these intermediate hydrocarbon components of oil partition into the NGL, resulting in enhanced solubility of NGL in oil and a subsequent decrease in oil volume. This observation is confirmed by the gradual color change of NGL to amber. We hypothesize that the NGL is spontaneously and forcefully imbibed into the oil-saturated core plug, displacing the oil, resembling a counter-current surfactant imbibition process. However, due to strong solubility of NGL in oil and the active hydrocarbon component's extraction mechanism, the produced oil is dissolved in NGL rather than forming oil droplets on the rock surface. Following the depletion stage, we observe two sequential oil production stages: 1) a prolonged single-phase flow stage until reaching the saturation pressure of the NGL, with total system compressibility as the dominant oil-recovery mechanism and 2) a two-phase flow region with solution-gas drive as the key oil-recovery mechanism. Remarkably, after one cycle of NGL HnP, most of the oil is recovered which surpasses the recovery factors observed in natural gas or CO2 HnP studies.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".