Performance evaluation of fracturing-huff-n-percolation-puff (FHnPP) processes in a hydrocarbon reservoir
Bibliographic record
Abstract
Due to its superior performance on the efficient exploitation of a small fault-block reservoir, a novel technique of fracturing-huff- n -percolation-puff (FHnPP) has received increasing attentions in recent years. In order to clearly identify and understand the associated mechanisms, reservoir simulations have been conducted to evaluate performance of an FHnPP process in a hydrocarbon reservoir. A series of simulation scenarios are designed to evaluate and identify dominant factors based on both single-factor and orthogonal schemes. The FHnPP performance can be understood as follows, i.e., created (micro-)fractures are extended from the surrounding water-zone deeper into formation during water injection, this process rebuilds the pressure field, enabling more trapped oil to be subsequently driven backwards the well after such (micro-)fractures are closed or partially-closed. Surfactants effectively reduce the water/oil interfacial tension (IFT), but it only increases oil production at early times. The existence of secondary fractures slightly enhances oil recovery at early puff-period after which such a positive impact is gradually vanished. A higher matrix permeability yields a higher ultimate oil recovery, but such a yielded positive effect from fracturing is then degraded. Moreover, the residual permeability of fractures during production (i.e., the puff process) negatively affect oil recovery, while a longer length of fracture results in more produced oil. Also, both injection rate and soaking time positively affect the oil recovery though the latter is insignificant. The orthogonal analysis indicates that, sensitivity of the dominant factors affecting oil recovery varies from each other, while the sensitivity of FHnPP's advantages to those factors is found also unequal. In the target reservoir with the optimized FHnPP parameters, significant oil increment (i.e., a recovery factor ( RF ) of 3.59% (i.e., 609.1 m 3 oil)) can be achieved compared with that of the traditional huff- n -puff (THnP) process. This numerical study not only proves the feasibility and advantages of the FHnPP technique, but also deepens our understanding of its performance and identifies the dominating factors.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".