Evaluating Interwell Connectivity in Waterflooding Reservoirs with Graph-Based Cooperation-Mission Neural Networks
Bibliographic record
Abstract
Summary Interwell connectivity plays a key role in waterflooding for guiding water injection. The existing works focus on the response relationship between one injection well and one production well. No research has explored the structural information of waterflooding on a well pattern. To address this challenge, this paper proposes cooperation-mission neural networks for interwell connectivity with graph information. Specifically, we propose some assumptions based on the petroleum domain to represent the well pattern with an adjacent matrix of the graph. Then we propose two targets from the view of injection well groups and production well groups. Accordingly, we propose cooperation-mission neural networks from these two aspects to evaluate the interwell connectivity in the well pattern. We test our model from two perspectives: the accuracy of estimation with tracer and the graduality of interwell connectivity. The results demonstrate that our model makes a good performance and achieves the connectivity analysis accuracy rate of 91.4%. Moreover, this study demonstrates that it is practical to evaluate the interwell connectivity with graph.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".