Fluid Identification in Low-Contrast Tight Sandstone Reservoirs based on Attention-Enhanced Multi-Layer Perceptron
Bibliographic record
Abstract
Tight sandstone reservoirs, characterized by fine-grained sediments, high clay content, and complex electrical properties, exhibit low-contrast logging responses between gas- and water-bearing zones, posing challenges for accurate fluid identification. This study introduces a novel approach using an Attention-Enhanced Multi-Layer Perceptron (AE-MLP) to improve fluid characterization in such reservoirs. By integrating a multi-head self-attention mechanism, the model dynamically weights key logging parameters (e.g., natural gamma, compensated density, neutron, and sonic) to capture subtle differences among fluid types, including gas, water, gas-water, and dry layers. To address data scarcity and class imbalance, synthetic minority oversampling is applied for data augmentation. Using logging data from Tianjin, the proposed method achieves a 96.36% accuracy, surpassing baseline models like SVM, KNN, RF, and AdaBoost, with a 3.5% improvement over standard MLP. The attention offers a robust and practical solution for fluid identification in complex low-contrast reservoirs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".