Flow and EOR Behaviors of Shale Oil in the Huff-n-Puff Process Using Water and CO<sub>2</sub> Systems: Considering the Elastic Depletion Effect
Bibliographic record
Abstract
How to enhance shale oil recovery after the horizontal well and hydrofracturing production becomes increasingly important. Here, the flow and enhanced oil recovery (EOR) behaviors of shale oil during the huff-n-puff process using water and CO 2 systems considering the elastic depletion effect were investigated by the precisely designed physics simulation experiments. During the elastic depletion process, distinct flow behaviors of oil and water were observed for the cores with varying permeability. Notably, cores with low permeability exhibited higher oil recovery than those with high permeability due to the strong forced imbibition effect and extended production time, which is advantageous for spontaneous imbibition to enhance the oil recovery. In the water huff-n-puff process, considering the elastic energy of the formation fluid can significantly enhance oil recovery values, reaching up to 50%, compared to approximately 10% when this energy is not accounted for. The early stage of water huff-n-puff primarily relies on the process itself, while the later stage is dominated by the elastic energy of the formation fluid. The effect of forced imbibition in the initial stage is significantly greater than that of spontaneous imbibition. Moreover, the addition of CO 2 to the water huff-n-puff process markedly increased the recovery rate. When CO 2 and formation water were injected at the same pressure difference, the efficiency of injecting CO 2 first followed by water was higher than that of the reverse order. Furthermore, the injection of carbonated water further enhances the imbibition efficiency, with better huff-n-puff effects observed in the low-permeability cores than in the high-permeability ones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".