Experimental Investigation on CO2-Crude Oil Phase Behaviour and CO2 Displacement Performance in a Tight Reservoir
Bibliographic record
Abstract
Abstract In this study, a pragmatic and integrated technique has been employed to experimentally investigate the mechanisms and potential of enhancing oil recovery in a tight reservoir through CO2-crude oil phase behaviour together with CO2 displacement performance. More specifically, the petrophysical properties together with microscopic pore-throat structure of tight core samples were firstly measured by using high-pressure mercury intrusion (HPMI) experiments measurement. Subsequently, the compositions and physical properties of crude oil, CO2 solubility, and the CO2-crude oil phase behaviour were thoroughly analyzed through experimental techniques including gas chromatographic (GC) analysis, constant composition expansion (CCE) experiments, and CO2 displacement experiments. By integrating CO2 displacement experiments with NMR testing, the microscopic residual oil saturation together with its distribution was further evaluated. The majority of pore-throats within the core samples displays significant heterogeneity with a predominantly narrow distribution. The porosity of the core samples is primarily governed by throats with a radius ranging from approximately 0.01 μm to 0.10 μm, where larger throats contribute significantly to permeability and smaller throats play a more prominent role in shaping porosity. The presence of dissolved CO2 can significantly reduce viscosity and induce oil swelling. Upon reaching a total concentration of 0.5 mol% of the injected CO2, the saturation pressure is increased by 1.54 times, reducing oil viscosity by over 75%. The CO2 displacement efficiency is less influenced by permeability but significantly affected by the adopted pressure difference. As the pressure difference increases, the CO2 breakthrough time is delayed, resulting in an improved oil recovery efficiency. Simultaneously, the effective radius of CO2 drainage decreases; however, at 6–8 MPa, the reduction magnitude in throat radius becomes slower. The findings presented herein serve as a theoretical foundation and provide technical support for the feasibility assessment of CO2 displacement in tight reservoirs to enhance oil recovery while optimizing CO2 injection strategies.
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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 | 0.001 |
| Scholarly communication | 0.000 | 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".