Incident Detection Adapting to the Drilling Depth for Geological Drilling Processes Based on Domain Adversarial Dual Graph Convolutional Network
Bibliographic record
Abstract
In geological drilling processes, it is of great importance to detect drilling incidents, so as to prevent serious consequences and improve the operational safety. However, prompt and accurate detection of drilling incidents is quite challenging, since the difference between data samples under normal and faulty states are usually inappreciable in the early stage of a drilling incident. Meanwhile, the formation commonly changes with the depth, making a model trained based on data from a certain depth hardly adapt to incident detection at different depths. Accordingly, this article proposes a new incident detection method adapting to the drilling depth for geological drilling processes based on the domain adversarial dual graph convolutional network (DADGCN). The contributions are threefold: 1) a temporal-spatial multifeature graph (TSMFG) construction method is designed to excavate the difference of adjacent samples under multiple states; 2) a dual graph convolution network (DGCN)-based incident detection method is proposed to mine the deep features in the graphs; 3) a DADGCN framework is designed to generalize the well-trained incident detection model to different drilling depths. The effectiveness and superiority of the proposed method are demonstrated by case studies involving real data. According to the results, the incident detection accuracy of DGCN reaches 99% under the same drilling depth, and the accuracy of DADGCN exceeds 94% when adapting to different drilling depths, which are much better compared to other state-of-the-art methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".