A Way to Improve Water Alternating Gas Performance in Tight Oil Reservoirs
Bibliographic record
Abstract
Abstract Primary recovery remains as low as 5-10 % of original oil in place (OOIP) in tight oil reservoirs, even with horizontal wells and massively hydraulical fracturing applied. Water flood helps to maintain the pressure and CO2 contributes to oil swelling, viscosity reduction and wettability alteration; in addition, CO2 and water have a chance to improve oil recovery. Furthermore, a water alternating gas (WAG) process gives a higher oil recovery compared to continuous water or gas injection. The WAG performance can be improved by mobility control, wettability alteration and interfaical tension management. Chemical additives like polymer or foam can help to improve mobility, but they are limited to large porous media. The common pore diameter is approximately 30 nm to 2,000 nm in tight sandstone reservoirs and 2nm to 50nm in shale reservoirs. Alkaline can cause a reduction in interfacial tension. However, a candidate for alkaline flood should have an acid number above 0.5 mg OH- /g oil, corresponding to oil with API below 30. The surfactant particles with a diameter of around 10nm to 30nm can reduce interfacial tension while nanoparticles with a diameter of 1nm to 7 nm can affect disjoining pressure at interface and alter wettability; both of them can be candidate additives in improving WAG performance. Moreover, low salinity water exchanges ions in a reservoir, resulting in water film instability and wettability alteration. It can be an alternative solution in improving WAG performance. In this paper, an analytical model of the WAG process is studied. Afterwards, numerical reservoir simulations are made for surfactant, low salinity water and nanofluid additives in improving WAG performance.
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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 | 0.001 |
| 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".