Experimental Investigation on the Spontaneous Imbibition Behaviour of Shale Reservoirs
Bibliographic record
Abstract
Abstract In a shale reservoir, its pore-throat structure is dictated by the abundant micro/nano-pores and throats, complicated by the intricate connectivity, which leads to significant capillarity effect. Physically, shut-in operations followed a hydraulic fracturing process may enhance oil production/recovery via spontaneous imbibition. In this study, a pragmatic and integrated technique has been developed to experimentally characterize the microscopic imbibition behaviour within pores of various scales in a shale reservoir. More specifically, the petrophysical properties together with pore-throat structure were firstly measured by using core samples collected from a shale oil reservoir. Subsequently, spontaneous imbibition experiments at high-temperature and high-pressure integrated with the nuclear magnetic resonance (NMR) measurement were employed to continuously monitor the oil-water distribution and saturation variation within the pores at different stages. By quantitatively characterizing the imbibition rate and oil recovery factor, the spontaneous imbibition behaviour together with its characteristics of multiscale pores and throats were investigated and analyzed. The imbibition equilibrium time for the core plugs typically ranges from 96 to 120 h. During the initial 24 h of imbibition, the rate of imbibition is higher, resulting in a rapid oil recovery up to 8.0%–9.6%, which accounts for approximately 65.0–70.0% of the total oil recovery. Imbibition behaviour is more likely to occur in pores connected with small throats. The findings presented herein serve as a solid foundation for optimizing the shut-in time after hydraulic fracturing operations in shale reservoirs so as to increase oil production/recovery via spontaneous imbibition.
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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 | 0.000 | 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 | 0.000 |
| 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".