Analysis of Saturation Pressure Variation of Volatile Reservoir in Gas Injection and Pressure Maintaining Development
Bibliographic record
Abstract
Abstract For the volatile reservoir with gas injection and pressure maintaining development mode, the saturation pressure of volatile fluid in the process of gas injection is the key factor to determine the gas injection effect and the type of injected gas. At present, the prediction of saturation pressure is mainly based on experimental measurement, state equation and empirical formula. However, the indoor experiment is time-consuming and expensive, the calculation of state equation is cumbersome, and there are many parameters, which is not convenient for the actual operation of the mine. Therefore, the empirical formula method has been widely used. However, the existing empirical formulas for predicting saturation pressure do not consider the effects of injected gas, porous media and component gradient, which has a great impact on the prediction results. Therefore, based on the idea of multiple regression, based on the experimental test results, considering the effects of injected gas properties, injection volume, porous media, component gradient and fluid properties, this paper establishes the saturation pressure prediction model of volatile reservoir for gas injection and pressure maintaining development. Through example calculation and analysis, within a certain range of application, the saturation pressure predicted by the newly established model is close to the measured value, and the error is small. It also limits the applicable conditions of the model in this paper. The research results are helpful to the in-depth study of gas injection development mechanism of volatile reservoir and are of great significance.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 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".