Selection Criteria for Miscible-Gases to Enhance Oil Recovery in Unconventional Reservoirs of North America
Bibliographic record
Abstract
Abstract Unconventional resources have played a significant role in changing oil industry plans recently. Shale formations in North America such as Bakken, Niobrara, and Eagle Ford have huge oil in place, 100-900 Billion barrels of oil in Bakken only. However, the predicted primary recovery is still low as 5-10%. Therefore, seeking for techniques to enhance oil recovery in these complex plays is inevitable. In this paper, two different approaches have been integrated to investigate the feasibility of three different miscible-gases which are CO2, lean gases, and rich gases. Firstly, numerical simulation methods of compositional models have been incorporated with Local Grid Refinement (LGR) of hydraulic fractures to mimic the performance of these miscible gases in shale-reservoirs conditions. Furthermore, implementation of a diffusion model in the LS-LR-DK (logarithmically spaced, locally refined, and dual permeability) model has been conducted. Secondly, different molar-diffusivity rates for miscible gases have been simulated to find the diffusivity level in the field scale by matching the performance of some EOR pilot-tests which were conducted in Bakken formation of North Dakota, Montana, and South Saskatchewan. This study approved that diffusion flow is dominated in these types of reservoirs. Therefore, the injected CO2 needs a significant molar-diffusivity into formation-oil, so it can penetrate into shale-matrix and enhance oil production. However, some of CO2 Pilot-tests showed a good match with the simulated cases which have low molar-diffusivity between the injected CO2 and the formation-oil. Accordingly, the rich and lean gases have shown a better performance to enhance oil Recovery in these tight formations. However, rich gases need long soaking periods and lean gases need large volumes to be injected for more successful results. Furthermore, number of huff-n-puff cycles has a little effect on the injected-gases performance; however, the soaking period has a significant effect. This research project demonstrated how to select the best type of miscible gases to be injected in unconventional reservoirs according to the field candidate conditions and operating parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".