Analyze Horizontal Well Tests Using Reservoir Simulation Approach–a Case Study
Bibliographic record
Abstract
Analysis based on analytical solutions dominates in conventional well testing analysis. Analytical solutions, however, meet their challenges under some complex test conditions. This paper presents a case study of horizontal well testing analysis using simulation approach. In this case study, we show an example that the horizontal well tests, sometimes, could not be analyzed using conventional well testing methods, but they could be analyzed using simulation approach (a single well model in this case). By history matching the well tests, we calibrated the single well model. Then using the calibrated model, we analyzed the actual well performance. We also used the single well model to design a new drawdown test. The new drawdown test was successfully conducted and analyzed using an analytical model. The analysis results are consistent with those obtained from the earlier tests analyzed using simulation approach. Key words: Reservoir simulation approach; Horizontal well; ECLIPSE; PanSystem
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".