Using Surface Deformation To Estimate Reservoir Dilation: Strategies To Improve Accuracy
Bibliographic record
Abstract
Summary Reservoir dilat(at)ions, which are induced by a variety of subsurface injection operations, propagate to the surrounding formations and extend up to the ground surface, resulting in surface deformations. The surface deformations can be measured using various technologies and can be inverted to infer reservoir dilations (volume change distribution). This paper discusses the mathematical aspects of the inverse process in detail and investigates factors affecting the accuracy of the inverse solution through a parametric study. Based on results of the parametric study, the volume change distribution in the lateral direction can be estimated with both high accuracy and high resolution by applying the Tikhonov regularization technique. The volume change distribution in the vertical direction can also be resolved to a certain extent by providing further information regarding the desired solution in terms of an initial estimate. Strategies to improve accuracy of the inverse solution in the lateral as well as in the vertical directions are also discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".