Analysis of the impact of geological and engineering parameters on productivity in tight oil reservoirs
Bibliographic record
Abstract
Abstract Tight oil resources are abundant, but the factors affecting production capacity are complex. In this paper, focusing on tight oil reservoirs, three research works were conducted. First, using the numerical simulation software, a numerical model of tight oil reservoirs was established. Second, the influence of geological parameters such as porosity and permeability on oil production were analyzed. Third, the influence of rock compression coefficient and injection fluid on tight oil production were analyzed. Results show that: (a) When the porosity is 0.05, the cumulative oil production in the first 6 years is the highest, while in the later stage of the simulation, the cumulative oil production with a porosity of 0.1 is the highest. (b) The higher the permeability, the greater the cumulative oil production. The cumulative oil production under different permeability conditions are 1392.044, 2178.805, 2939.1704, and 4038.0878 m 3 , respectively. (c) Under tight reservoir conditions, the impact of different rock compression coefficients on the daily oil production of oil and gas reservoirs is not very significant. (d) The recovery effect is optimal when using the N 2 injection scheme. The effectiveness of the CH 4 scheme is second, and there is a certain gap compared to the N 2 scheme. The development plan of injecting water has the worst effect. However, compared to the depletion development model, the cumulative oil production by injecting N 2 , CO 2 , CH 4 , and water has all increased.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".