Property parameter determination in individual layers for separately fractured wells with commingled production in multi-layered reservoirs
Bibliographic record
Abstract
Abstract At present, without any separate rate test for each layer, there is no way to determinate the properties of individual layers for separately fractured wells with commingled production in multi-layered reservoirs. In order to address this issue, much research work was performed and elucidated in this article. To begin with, we illustrated a basic physical model for a separately fractured well in a multi-layered reservoir. Next, we stated the common determination method that can only be used to gain the average properties of multi-layered reservoirs. Then, according to the physical model, we newly established a mathematical model and plotted standard well-test type curves; additionally, we also discussed why we cannot determinate the properties of individual layers by using the new well-test model. What’s more, we presented a new method to determinate the properties of individual layers. Moreover, we compared the advantages and disadvantages among the three methods. In addition, by using the new determination method, we particularly took two field wells as examples to demonstrate how to determine the properties of individual layers. The proposed new method was validated by use of the common method, the new well-test model and the microseismic monitoring results. At the end, we summarized the research conclusions and indicated that the new method was a good tool to determinate the properties of individual layers in multi-layered reservoirs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".