A Visual Steering Software for Geological Well Testing
Bibliographic record
Abstract
Summary Conventional well test interpretation is based on employing simplified analytical models for parameter estimation. However, an enhanced use of the well test data for the reservoir characterization is only attained using geological well testing. Geological well testing is used to validate the dynamic response of the model using available pressure transient tests. This paper presents a novel visual steering framework for dynamic validation of the reservoir models using the well test data. The software has the ability of correlating the 3D models with the well test diagnostic curves for detecting the influence of the reservoir and fluid heterogeneities on the transient behavior of the model. The engineer is able to update the geological model using various facilities embedded in the software using manual and/or assisted history matching approaches. We show a challenging well test response in a faulted channelized environment in a gas condensate reservoir and present a sensitivity study to understand and interpret such a complicated well test response using our developed framework. This work is aimed at designing a multi-source, multi-domain framework for efficient data integration within an engineering workflow.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".