An Efficient and Robust Saturation Pressure Calculation Algorithm for Petroleum Reservoir Fluids Using a Neural Network
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Bibliographic record
Abstract
Abstract Saturation pressure is one of the key parameters in hydrocarbon reservoir engineering computations that can be obtained by either empirical correlations or equations of state. In the latter case, one of the greatest challenges in calculation is the selection of a good initial value to start the iteration. In this work, a feed-forward multilayer neural network model is introduced to predict a good initial value for bubble-point pressure calculation applying iterative methods. The model was developed by using 411 published data samples from fields in the Middle East. This model provides a prediction of the bubble point with a relative average error of 0.532%, an absolute average error of 3.273%, a standard deviation of 3.417%, and a correlation coefficient of 0.999989, which implies great accuracy.
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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 | 0.001 |
| 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 it