Experimental investigation of hybrid enhanced oil recovery techniques for Ugnu Heavy Oil on Alaska North Slope
Bibliographic record
Abstract
The Alaska North Slope (ANS) is endowed with a substantial reservoir of heavy oil, estimated at 12–18 billion barrels, primarily concentrated within the Ugnu reservoirs. These deposits, situated at depths ranging from 2000 to 4000 feet, lie in close proximity to the permafrost and have undergone biodegradation, resulting in in-situ viscosities reaching thousands of centipoise. Following the success in recovering the somewhat less heavy, viscous oils through polymer injection, the deposits in Ugnu Formation are garnering significant interest. Although thermal recovery methods are commonplace for heavy oils, applying these methods on ANS is impractical, given the adjacency to continuous permafrost. Therefore, non-thermal hybrid enhanced oil recovery (cEOR) methods, such as solvent (e.g., CO 2 ) and low salinity water (LSW), or LSW and polymer-based techniques, emerge as the primarily feasible options for recovering these vast heavy oil resources. This study experimentally investigates, via systematically carried out fluid property and phase behavior tests and a series of sand-pack coreflood experiments, the potential to enhance the recovery of Ugnu heavy oils. The coreflood experiments reveal the synergistic effect of combining liquid-CO 2 with LSW to be the most promising approach in this study as a water alternating gas (WAG) process results in the cumulative recovery factor of 83.5%, doubling the recovery obtained by continuous low salinity waterflood. Additionally, the liquid-CO 2 -LSW WAG process demonstrated an additional benefit for CO 2 storage, with about 25% of the pore volume of the liquid-CO 2 injected being sequestered at the end of the injection process. This significant recovery improvement is attributed to a substantial reduction of oil viscosity upon contact with the liquid CO 2 during the soaking period, with a reduction of up to 95% of the original oil viscosity. Meanwhile, in-situ emulsion generation was observed in the oil produced from the continuous LSW flooding. This was also evident by the increased differential pressure across the sand-pack compared to that of the liquid-CO 2 alternating LSW process. The promising results of this study indicate significant potential for liquid-CO 2 alternating LSW injection as an effective cEOR technique for Ugnu heavy oils.
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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 | 0.000 | 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) | 0.001 | 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".