Research on inter-well connectivity of water-flooding reservoir: Temporal neural network based on graph structure
Bibliographic record
Abstract
Inter-well connectivity plays a crucial role in the development and production of water-flooding reservoirs, involving fluid transfer and mutual influence between different wells, and playing an important role in optimizing oilfield development and production. The traditional study of inter-well connectivity is mainly based on static information, while ignoring the dynamic changes over time and the graph structure of the well network. To address this issue, this paper proposes a temporal neural network based on graph structure (GSTNN). It combines the advantages of traditional neural networks and graph structures, fully considering the temporal information of the well network. A series of well network temporal feature information is used to predict inter-well connectivity within the framework of traditional neural networks, and optimize the model based on the graph structure of the well network. Experiments with GSTNN in different basic models and different graph sizes show that the method proposed in this paper is consistent with the actual geological characteristics of oil reservoirs. Compared with traditional neural network methods, GSTNN achieves more accurate results and shows good performance in less time in production prediction and inter-well connectivity reflection.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".