Review of CO2-kerogen interaction and its effects on enhanced oil recovery and carbon sequestration in shale oil reservoirs
Bibliographic record
Abstract
Shale oil resources have proven to be quickly producible in large quantities and have recently revolutionized the oil and gas industry. The oil content in a shale oil formation includes free oil contained in pores and trapped oil in the organic material called kerogen. The latter can represent a significant portion of the total oil and yet production of shale oil currently targets only the free oil rather than the trapped oil in kerogen. Shale oil reservoirs also have a substantial capacity to store CO2 by dissolving it in kerogen. In this paper, recent progress in the research of CO2-kerogen interaction and its applications in CO2 enhanced oil recovery and carbon sequestration in shale oil reservoirs are reviewed. The relevant topics reviewed for this relatively new area include characterization of organic matter, supercritical CO2 extraction of oil in shale, experimental and simulation study of CO2-hydrocarbons counter-current diffusion in organic matter, recovery of oil in kerogen during CO2 huff ‘n’ puff process, and changes in microstructure of shale caused by CO2-kerogen interaction. The results presented in this paper show that at reservoir conditions, supercritical CO2 can spontaneously replace the hydrocarbons from the organic matter of shale formations. This mass transfer process is the key to releasing organic oil saturation and maximizing the capacity of carbon storage of a shale oil reservoir. It also presents a concern of the structure change of organic materials for long term CO2 sequestration with shale or mudstone as the sealing rocks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".