The Impact of Brine Saturation and Distribution on Lean Gas Huff-n-Puff EOR Performance of Tight Oil Reservoirs: Examples from the Montney Formation (Canada)
Bibliographic record
Abstract
Oil recovery from low-permeability (‘tight’) oil reservoirs remains low despite the application of modern drilling and completions technologies, which has increased interest in trialing enhanced oil recovery (EOR) schemes. Cyclic gas injection (Huff-n-Puff, HNP) is a promising approach to EOR for these reservoirs. However, the underlying mechanisms of EOR using the HNP scheme in tight reservoirs are not yet fully understood. This laboratory study investigates the performance of lean gas (80%C1 + 20%C2; approximating produced gas compositions from the field) HNP using low-permeability core plug samples from the Montney Formation of Canada. An objective of the study was to evaluate the effects of induced fractures, and brine saturation and distribution, on the efficiency of lean gas HNP performance. Both intact and artificially fractured core plugs were studied. The introduction of fractures into the low-permeability core plugs improved recovery factors by 17.5–18.5%. However, the presence of brine limited oil production from both intact and fractured core plugs. Notably, when brine was concentrated along the fracture surfaces, the recovery factor dropped significantly, down to just 1.2% of original oil in place (OOIP). This reduction is primarily attributed to the low solubility of methane and ethane (C1 + C2) in brine, which hinders the injectant’s ability to diffuse into the core matrix and mobilize oil. The findings of this study will be of interest to operators evaluating the potential of cyclic gas injection in low-permeability reservoirs.
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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 | 0.001 | 0.001 |
| 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".