Experimental Evaluation on Feasibility of Water Huff-N-Puff Processes in a Naturally Fractured Oil Reservoir With Low Permeability
Bibliographic record
Abstract
Abstract In this study, a pragmatic and integrated technique has been developed to experimentally evaluate the feasibility of water huff-n-puff processes in a naturally fractured oil reservoir with low permeability. Firstly, the pore-throat structures of core samples collected from a naturally fractured oil reservoir with low permeability were measured. Subsequently, water huff-n-puff experiments integrated with the nuclear magnetic resonance (NMR) measurement were conducted. By analyzing the oil-water production profiles, oil recovery factor, residual oil distribution and saturation variation at various stages, production performance and microscopic oil-sweeping characteristics of core samples with and without natural fractures were evaluated and analyzed. Compared to core samples without natural fractures, the oil recovery factor of core samples with natural fractures increases from 28.38% to 35.63%. The oil in pores connected by smaller throats is clearly displaced through imbibition, the production characteristics are primarily governed by the displacement from natural fractures and larger throats, and thus implementing water huff-n-puff techniques in naturally fractured oil reservoirs with low permeability has proven to be both effective and feasible. Natural fractures effectively reduce the oil-water flow resistance at the injection and production stage, resulting in an increase in the oil-sweeping range and enhancing imbibition through an increased contact area of oil-water-rock as well. The oil recovery factor of a single cycle decreases gradually with the increase of cycles. The findings presented herein provide a basis for the assessment and optimization of water huff-n-puff operations in naturally fractured oil reservoirs with low permeability.
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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 | 0.001 |
| 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 | 0.001 | 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".