Experimental and computational evaluation of cyclic solvent injection in fractured tight hydrocarbon reservoirs
Bibliographic record
Abstract
Abstract Multi-fractured horizontal wells have enabled commercial production from low-permeability (‘tight’) hydrocarbon reservoirs but recoveries remain exceedingly small (< 5–10%). As a result, operators have investigated the use of solvent (gas) injection schemes, such as huff-n-puff (HNP), to improve oil recovery. Previous HNP laboratory approaches, classified primary as ‘flow-through-matrix’ and ‘flow-around-matrix’ typically (1) are not fully representative of field conditions at near-fracture regions and (2) require long test times, even when performed on fractured cores. The objectives of this proof-of-concept study are to (1) design and implement a new experimental procedure that better reproduces HNP schemes in near-fracture regions and (2) use the results, simulated with a compositional lab-calibrated model, to explore the controls on enhanced hydrocarbon recovery in depleted tight oil plays. Performing multiple CO2 and (simplified) lean gas HNP cycles, the integrated experimental and simulation approach proposed herein achieves the ultimate recovery factors in a significantly shorter time frame (25–50%) compared to previous studies. The integrated experimental and computational approach proposed herein is valuable for core-based evaluation of cyclic solvent (CO2, CH4) injection in tight hydrocarbon reservoirs for (1) hydrocarbon recovery and (2) subsurface greenhouse (CO2, CH4) gas disposal/storage applications.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".