The Effect of Unconventional Oil Reservoirs’ Nano Pore Size on the Stability of Asphaltene During Carbon Dioxide Injection
Bibliographic record
Abstract
Abstract Carbon dioxide (CO2) has been proven to be an extremely successful enhanced oil recovery method to increase oil recovery from hydrocarbon reservoirs. It has also been proposed as a novel production method for unconventional shale reservoirs with nano pores as well. One of the main drawbacks of CO2 injection is asphaltene precipitation and deposition, which may result in severe pore plugging, and thus a significant decrease in oil recovery. Even though asphaltene precipitation during CO2 injection in conventional oil reservoirs has been researched extensively, not much research has been conducted to evaluate asphaltene precipitation and pore plugging in unconventional nano pores. This research investigates the impact of several factors on asphaltene precipitation and deposition, and asphaltene pore plugging in nano pores. Composite nano-filter membranes with 10, and 100 nm pore size were used to conduct all experiments. A specially designed high pressure high temperature filtration vessel was constructed and utilized to accommodate both the filter membrane, and the crude oil. The impact of varying the CO2 injection pressure, temperature, filter membrane pore size, and the CO2 soaking time on asphaltene deposition, and pore plugging were investigated. Results showed that higher CO2 injection pressures resulted in a higher oil recovery, a lower asphaltene concentration in the unproduced, bypassed oil, and a higher asphaltene concentration in the produced oil compared to the lower CO2 injection pressures. An opposite trend was observed with the temperature however, due to the temperature resulting a severe disturbance in the asphaltene thermodynamic equilibrium with the other crude oil components. Increasing the pore size resulted in a less severe asphaltene pore plugging, whereas increasing the CO2 soaking time resulted in an increase in the asphaltene deposition and pore plugging. This research performs an experimental study to show the main factors that will impact asphaltene precipitation, deposition, and pore plugging in nano pores during CO2 injection. This may help in improving oil recovery from CO2 injection projects in unconventional shale reservoirs, especially those with a high asphaltene percentage in their crude oils.
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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".