Numerical Simulation of Chemical Flooding and Its Application for Horizontal Wells
Bibliographic record
Abstract
Numerical simulation of permeation fluid mechanics for three phase water, oil and gas-chemical compound combination flooding of the polymer, surface active agents and alkali in porous media is discussed in this paper. In view of petroleum geology, geochemistry, computational mechanics of flow and computer technology, a permeation fluid mechanics model of three phase chemical compound combination flooding is presented firstly, then a numerical algorithm consisting of a full implicit program, an implicit computation for the pressure and an implicit/explicit program respective for the pressure and the concentration is given by structuring an upstream sequence and an iterative algorithm of implicit fined upwind fractional step finite difference to solve the pressure equation, the saturation equation and the concentration of chemical substance components and the petroleum acid concentration equation. A type of high accuracy software applicable in major industries is made on ten-meter steps, one hundred and fifty thousand nodes and tens of years and has been carried out successfully in analysis and simulation of national major oil-fields extraction such as Daqing Oilfield, which gives rise to outstanding economic and social benefits. A precise analysis is given for a simplified model and an idea is presented to solve this international famous problem.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".