Reservoir Sensitivity Forecasting Method Based on Hybrid Improved CNN and BiGRU Unit
Bibliographic record
Abstract
Reservoir sensitivity evaluation is used to evaluate the degree of damage to various operating fluids and production parameters of the reservoir in the production process of oil and gas wells. The neural network is widely used in reservoir sensitivity forecasting because of its nonlinear solid fitting and generalisation ability. Although many neural network models have been applied to reservoir sensitivity forecasting, there is still room for improvement in the accuracy of the models. Therefore, to improve the prediction accuracy of the forecasting model, this study will introduce a novel convolutional neural network model (WOA-CNN-BiGRU) integrated with a whale optimisation algorithm and bidirectional gated recurrent unit to forecast the sensitivity of low permeability reservoir. The experiment used relevant datasets to test the model strictly, and the previous BPNN, Elman, and RBF models were compared. The result shows that the percentage error of the WOA-CNN-BiGRU model was as low as 2.6%, which was lower than other forecasting models. The results show that the accuracy of the WOA-CNN-BiGRU model is not only higher than that of engineering measurement methods but also higher than that of other existing models, which has a good potential for application in the industry of reservoir sensitivity forecasting.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".