Efficient numerical simulation and method optimization for pressure distribution calculation in petroleum seepage field
Bibliographic record
Abstract
In this paper, a pressure distribution model of seepage ield based on complex reservoir conditions is established based on a inite element mathematical model.Due to the non-homogeneity and multiple low characteristics of the reservoir, the mathematical model of fractured horizontal wells based on reservoir and fracture is established by solving the inite element equations of oil-phase pressure and water-phase saturation under the two-dimensional oil-water two-phase inite element model.Through numerical simulation of the coupling between the permeability change of the fractured fracture and the bedrock in the oil seepage ield, the in luence of different fracture parameters on the pressure distribution is analyzed, and each parameter is optimized.Investigations of stress-strain, porosity and permeability in time and space in low-permeability reservoirs found that in the region near the bottom of the well, each parameter varies more, while the farther away from the bottom of the well region the less affected it is.The relative position of the fracture to the well has a large effect on the production of fractured horizontal wells, but this parameter can be arti icially regulated.Repeated fracturing cumulative oil incremental analysis found that "fracture network bandwidth, main fracture half-length and main fracture in low capacity" have the greatest in luence on the high permeability strip, the factors of angular wells and low permeability zones, and the repeated fracturing cumulative oil incremental simulation of each fracture parameter has the greatest effect on the fracture network bandwidth, main fracture half-length and main fracture in low capacity under the coupled model of Well 3 (23.25%),and the optimal values of the parameters are 100m, 100m, 100m, 100m, 100m, 100m and 100m respectively.optimal values of the parameters are 100 m, 15010 -3 m 2 m, 20 m and 4510 -3m 2 m, respectively.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".