Estimation of fluid saturation and pressure distribution throughout a reservoir using machine learning techniques
Why this work is in the frame
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Bibliographic record
Abstract
Water saturation is one of the most critical yet often underappreciated petrophysical parameters in reservoir characterization. A wide range of petrophysical and reservoir engineering computations that lead to crucial field development decisions, including reserve estimation, waterflooding efficiency calculation, and capillary pressure deduction, rely on its accurate determination. This study demonstrates how machine learning techniques can forecast reservoirs’ fluid saturation and pressure distribution. This study describes a deep learning–based proxy modeling technique for accurately predicting reservoir pressure distribution and fluid (oil, water, and gas) saturation during water flooding in single-layer heterogeneous reservoirs. This study used recurrent neural networks (RNNs) and convolutional neural networks (CNNs) to build a proxy model. This work indicates that compared to simulation outcomes, computer-based machine learning algorithms can accurately predict fluid (oil, water, and gas) saturation and pressure distribution. The stated accuracy was evaluated numerically and graphically, and error analysis between various machine learning approaches and simulated results was utilized.
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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 | 0.001 |
| 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