Analysis of Injection and Production Data for Open and Large Reservoirs
Bibliographic record
Abstract
Numerous studies have concluded that connectivity is one of the most important factors controlling the success of improved oil recovery processes. Interwell connectivity evaluation can help identify flow barriers and conduits and provide tools for reservoir management and production optimization. The multiwell productivity index (MPI)-based method provides the connectivity indices between well pairs based on injection/production data. By decoupling the effects of well locations, skin factors, injection rates, and the producers’ bottomhole pressures from the calculated connectivity, the heterogeneity matrix obtained by this method solely represents the heterogeneity and possible anisotropy of the formation. Previously, the MPI method was developed for bounded reservoirs with limited numbers of wells. In this paper, we extend the MPI method to deal with cases of large numbers of wells and open reservoirs. To handle open reservoirs, we applied some modifications to the MPI method by adding a virtual well to the system. In cases with large numbers of wells, we applied a model reduction strategy based on the location of the wells, called windowing. Integration of these approaches with the MPI method can quickly and efficiently model field data to optimize well patterns and flood parameters.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".