Methane Huff-n-Puff in Eagle Ford Shale – An Experimental and Modelling Study
Bibliographic record
Abstract
Injection pressure, soaking duration, and depletion strategy are crucial operational parameters for a successful gas Huff-n-Puff (HnP) pilot. There is limited experimental data on efficiency of natural gas (C 1 ) HnP, particularly in the Eagle Ford Formation. This study aims to optimize this technique using an organic shale sample from this formation. We use a state-of-the-art visualization cell for real-time monitoring of gas-oil interactions under 1) bulk-phase and 2) core HnP conditions to investigate synergy between these two experiments. We consider two injection pressures of 22.55 and 36 MPa with soaking durations of 200 and 480 h to assess their impact on gas diffusion in oil. We develop a mathematical scaling technique that incorporates HnP field data to select appropriate depletion strategies for lab-scale experiments. We adopt a hybrid depletion strategy consisting of fast and slow depletions. We estimate bulk-phase and apparent diffusion coefficients of C 1 in oil and quantify the tortuosity of the shale sample. Compositional analysis reveals key oil-recovery mechanisms, including solution-gas drive, oil swelling, and oil vaporization. Longer soaking intervals result in more gas diffusion into the core under both pressure conditions, with higher injection pressure leading to more diffused gas. Bulk-phase and apparent diffusion coefficients are on the order of 10 -8 and 10 -10 m 2 /s, respectively, with an average tortuosity of 1.66. Both coefficients decrease by increasing pressure due to suppressed gas mobility. Compositional data reveals the extraction of C 5 to C 9 oil components through the oil vaporization mechanism, with minimal pressure impact on the composition of extracted oil. Following a single-cycle C 1 HnP, ultimate oil recovery factors range from 27.9 to 46.1 % of the initial oil-in-place, with recovery increasing with higher pressure and longer soaking durations.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".