A novel approach to gas cycling enhanced oil recovery (GCEOR) evaluation in unconventional porous media
Bibliographic record
Abstract
Unconventional production suffers from three specific weaknesses: rapid production decline, infill well interference and poor recovery. Gas-Cycling Enhanced Oil Recovery (GCEOR) has the potential to improve all three of these limitations. Many authors use reservoir simulation to evaluate the benefits of GCEOR. These forecasts are often uncalibrated and lack fundamental experimental data. Traditional, axial core testing, is not practical for unconventional rock. The Darcy equation (Q = k A ΔP/ μ L) describes why axial core testing is impractical. For a permeability of 100 nD and a viscosity of 0.5 mPa-s, with typical ΔP, the time required to inject one hydrocarbon pore volume (HCPV) of fluid is 22 weeks. Using radial flow with the same conditions, approximately three hours would be required to inject one HCPV. Patented equipment design allowed GCEOR experimentation at full reservoir conditions in porous media ranging from 10 nD up to 2400 nD (Duvernay and Montney) using fluids ranging from gas condensate gas to 40 API oil. Some of the conclusions, based upon more than 70 primary depletions followed by multiple cycles of GCEOR Huff and Puff, were: 1. Geological heterogeneities play a major role in GCEOR performance. 2. Well-designed GCEOR performs like gas storage; injection gas volumes were less than 5 Mscf per incremental barrel of hydrocarbon liquid recovered by GCEOR. 3. Acid gases may also be sequestered using GCEOR in unconventionals. 4. GCEOR for hydrocarbon liquids recovery applied to gas condensate fluids performs well compared to primary depletion hydrocarbon liquid recovery. This paper describes laboratory-scale testing that can help to optimize GCEOR in unconventional porous media.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".