CO2 Near-Miscible Flooding for Tight Oil Exploitation
Bibliographic record
Abstract
Abstract Tight oil resources have become increasingly important as massive hydraulic fracturing techniques breakthrough. Water flooding is generally applied to tight oil reservoirs; however, the oil recovery achieved by water flooding is quite low. A CO2 miscible flooding process is regarded as a primary enhanced oil recovery (EOR) technique for conventional oil reservoirs as CO2 can extract oil even at a high water cut. Furthermore, many CO2 field trials in low permeability reservoirs have been recorded as successful. As CO2 utilization efficiency drops when formation permeability goes down, CO2 injection in a miscible condition for tight oil exploitation may not be as profitable as that in conventional oil reservoirs. In tight formations, there exist small pore throats, even at nanoscale. As the confined space in nanopores may shift a phase envelop and lower CO2 minimum miscible pressure (MMP), operating a well in a near-miscible region where pressure is slightly less than MMP as measured in the lab may result in a good chance of miscibility for some parts of a tight oil reservoir. In this paper, equations of state (EOS) calculations are conducted in order to see the effects of confinement on a CO2 injection process in tight oil reservoirs. On the basis of Cardium reservoir properties, numerical reservoir simulations are run to investigate the effects of confinement caused by a small pore throat size in 50 nm and 10nm on the CO2 injection process. Comparisons of CO2 near-miscible and miscible processes are made with various pore throat sizes. Results show that confinement effects in tight formations help to lower the bubble point pressure and boost an oil rate during CO2 injection. However, CO2 EOR efficiency goes down as formation pressure approaches MMP as mearsured in the lab. It is not necessary for CO2 injection to operate in an above MMP condition in tight formations, where a nanopore size is present. In this way, the volume of CO2 injected can be reduced. For tight oil reservoirs with a small pore throat size, a CO2 near-miscible process is more suitable than miscible flooding.
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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 | 0.000 |
| 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".