Research on Segmented Pressure Prediction and Drilling Boundary of Encrypted Horizontal Wells in Water Drive Ultra-low Permeability Reservoirs—Taking Oil Fields as an Example
Bibliographic record
Abstract
This article proposes a comprehensive prediction method that combines precise geological modeling of the target area for accurate quality control and precise numerical simulation of key parameter differentiation zoning to address the difficulty of predicting formation pressure in the development of encrypted horizontal wells in ultra-low permeability reservoirs in oil fields. By establishing a three-dimensional fine geological model and combining reservoir engineering theory with numerical simulation technology, the formation pressure at the horizontal well finger, root, and middle positions was displayed with an error control of 2%, achieving high-precision prediction of the formation pressure in encrypted horizontal wells. Research has shown that the heterogeneity of ultra-low permeability reservoirs is significant, and the anisotropy of permeability has a controlling effect on pressure distribution. By fitting and predicting differentiated zones such as oil-water interface, reserves, relative permeability, and measures, the prediction error is reduced compared to traditional reservoir engineering methods; Based on the pressure prediction results and combined with the pilot well field test, the shut in pressure of the water wells around the encrypted horizontal well in the ultra-low permeability reservoir was optimized, and the pressure limit was raised by 3MPa; After optimization, the drilling time is shortened, the block pressure is maintained at a good level, the drilling effect of the block is better than expected, and the production loss of old wells is reduced. The overflow flow caused by drilling is also reduced, and the overall operating cost is lowered. The technical research results provide reference for the encryption adjustment of similar oil fields.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".