Real-time NMR experimental study of displacement–imbibition coupling in tight/shale oil reservoirs: Lithological variations, influencing factors, and key insights
Bibliographic record
Abstract
Displacement–imbibition coupling production is a pivotal technology for enhancing oil recovery (EOR) in waterflooded tight/shale oil reservoirs. However, the microscopic fluid transport mechanisms across different pore scales remain inadequately understood. This study presents an innovative real-time nuclear magnetic resonance (NMR) experimental system integrated with MRI-based image processing to dynamically monitor oil–water distribution and quantify local oil saturation during injection–shut-in–production. This approach enables quantitative evaluation of pore utilization across different pore size ranges and reveals the impacts of various driving forces on oil displacement efficiency. The results show that displacement–imbibition coupling production employs multiple mechanisms to achieve balanced contributions from pores of all size scales. The displacement–imbibition oil production mainly consists of three stages: displacement-dominated injection, capillary-driven imbibition during shut-in, and displacement–imbibition coupling effects during production. Pressure oscillations significantly enhance matrix–fracture exchange by lowering pore-throat entry thresholds and redistributing pressure fields. Quantitative analysis shows that large pore dominate early displacement, while small pore contribute more during imbibition. Lithology and pore-throat connectivity critically influence displacement efficiency; vitric tuff outperforms argillaceous siltstone by up to 11.8%. Notably, greater fracture complexity increases the oil–water contact area, enhancing capillary imbibition, reducing reliance on displacement forces, and increasing the contribution of displacement–imbibition coupling effects to oil displacement efficiency by 15.35%. Artificially modifying the pressure field to induce pressure oscillations, effectively utilizing the high conductivity of fractures, and fully leveraging the displacement–imbibition coupling effects within matrix pores are crucial for achieving optimal EOR. Lastly, a new concept of nonlinear flow zoning is introduced to describe spatial variations in flow behavior under complex coupling conditions. These experimentally validated insights into matrix–fracture interactions provide theoretical support for designing improved waterflooding strategies and optimizing oil recovery in tight and shale reservoirs.
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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 | 0.000 |
| 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 | 0.000 | 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".