A New Foamy Oil-Assisted Methane Huff-N-Puff Method for Enhanced Heavy Oil Recovery in Thin Reservoirs
Bibliographic record
Abstract
Abstract A considerable portion of heavy oil resides in thin formations. In such reservoir environments, thermal recovery methods tend to be neither effective nor economical, due to significant heat loss and water resource requirements. The methane huff-n-puff process for enhancing heavy oil recovery in thin formations is an alternative that has the advantages of availability of resources and economic efficiency. However, the principal problem with such a process is the high mobility of the gas phase, relative to the oil phase, during the production cycle. In order to overcome this limitation, this paper presents a new enhanced methane injection technique, called the foamy oil-assisted methane huff-n-puff method (FOAM H-n-P). In this process, an oil-soluble foaming agent and methane are injected in a cyclic manner, but in two separate slugs. The methane provides the solution gas drive energy, and the presence of the foaming agent promotes the formation of an in situ foamy oil by trapping the released gas, which significantly enhances the effectiveness of the solution gas drive mechanism. A new experimental procedure was developed to prove that the selected foaming agent has an effective foaminess in conventional heavy oil, and to investigate, quantitatively, the effects of various process parameters on foamy oil stability in the FOAM H-n-P process. A series of experiments were conducted to evaluate FOAM H-n-P performance and to examine the effect of various foaming agent injection parameters on oil recovery. The experimental results show that foaming oil stability increases with greater foaming agent concentration, greater initial heavy oil height, a higher degree of gas bubble dispersiveness, and smaller dispersed gas bubble size. The FOAM H-n-P process can substantially improve heavy oil recovery performance. The most efficient case increases oil recovery by 43.29% OOIP, compared to the traditional methane huff-n-puff process. The above knowledge will have significant importance for the development of heavy oil resources in thin reservoirs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".