Factors Affecting Huff-n-Puff Efficiency in Hydraulically-Fractured Tight Reservoirs
Bibliographic record
Abstract
Abstract Evaluation of enhanced liquid recovery from tight or shale reservoirs is currently of great interest to operators. One reason is the low primary oil recovery of tight/shale reservoirs, which ranges between 5-10% (Shoaib and Hoffman, 2009) even after expensive multi-stage hydraulic fracture stimulation. Enhanced recovery using the "huff-n-puff" process could be an effective solution to increase recovery without drilling new wells. There are a number of published lab and simulation studies that investigate the efficiency of huff-n-puff in tight/shale reservoirs. These studies, however, have yielded contradictory results. For example, Chen et al. (2014) concluded that huff-n-puff has a negative impact on recovery while Yu et al. (2014) concluded that it improves recovery by 2-9%. These conflicting results underscore the need for further research. The current study, therefore, endeavors to investigate possible causes of these discrepancies. Compositional numerical simulation is used to investigate key simulation model setup and reservoir controls on huff-n-puff efficiency in tight reservoirs. Some of these controls have never been investigated for tight reservoirs, such as the influence of grid refinement, in-situ fluid composition, and fracture pore volume/hydraulic fracture representation. One important finding of this work is that grid refinement, and fracture pseudo width, greatly impact huff-n-puff results. The combination of coarse gridding and improper fracture representation through the pseudo width approach can lead to falsely optimistic incremental recovery associated with huff-n-puff relative to primary recovery. While the findings presented herein are useful in explaining possible causes of the discrepancies in results reported in previous work, they can also be used to improve huff-n-puff design. For example, the combination of fine fracture spacing in multi-fractured horizontal wells and increased fracture complexity can positively influence incremental recovery obtained from huff-n-puff. Further, the results suggest that huff-n-puff timing (with respect to primary production operations) should be carefully considered. This study will help simulation engineers improve their evaluations of huff-n-puff in tight/shales reservoirs. Additionally, it will help operators decide which reservoir is suitable for huff-n-puff operations to improve liquids recovery. Application of the findings of this study to actual field scenarios will be presented as a separate work.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".