Geovisualization for Association Rule Mining in Oil and Gas Well Data
Why this work is in the frame
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Bibliographic record
Abstract
Association rule mining on oil and gas data has recently been successfully used to help understand reservoirs; therefore, the visualization and understanding of the discovered association rules based on well locations and subsequent predictions based on the applicable areas of the rules are important. In this paper, two visualization methods—point- and surface-based geovisualization—are proposed for association rules from oil and gas well data. The point-based method represents association rules based on well locations, and the surface-based method represents potentially applicable areas through spatial interpolation and visualization. A case study has been carried out on a real cold production oil well dataset in western Alberta, Canada, and, the results illustrate the feasibility of the proposed geovisualization methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.011 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it