Accurate determination of the CO<sub>2</sub>‐crude oil minimum miscibility pressure of pure and impure CO<sub>2</sub> streams: A robust modelling approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Gas flooding processes have emerged as attractive enhanced oil recovery (EOR) methods over the last few decades. Among different gas flooding processes, CO 2 flooding is recognized as being most efficient for displacing oil through miscible displacement. Minimum miscibility pressure (MMP) is a crucial parameter for successfully designing CO 2 flooding, which is traditionally measured through time‐consuming, expensive, and cumbersome experiments. In the present study, a new reliable model based on feed‐forward artificial neural networks was presented to predict both pure and impure CO 2 ‐crude oil MMP. Among various properties and parameters, reservoir temperature, reservoir oil composition, and injected gas composition were selected as the input parameters of the proposed model. To evaluate and compare the results of the developed model with existing models, both statistical and graphical error analyses were simultaneously employed. The results showed that the proposed model is more reliable and accurate compared to existing models in a wide range of thermodynamic and process conditions. Furthermore, by employing the relevancy factor, it was found that the reservoir temperature has the most significant impact on the MMP. Finally, in order to identify probable outliers and the applicability domain of the proposed model, the leverage approach was performed. The results illustrated that only two experimental MMP data points were located outside of the applicability domain of the proposed model. As a result, the developed model is statistically reliable for predicting crude oil MMP.
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.
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 | 0.000 |
| 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 | 0.001 |
| 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 it