A Visual Framework for Reservoir Connectivity Analysis
Bibliographic record
Abstract
Summary Static connectivity measures have been proposed for quick evaluation of reservoir performance to provide a potentially important link between the reservoir characterization and the simulation studies. These measures are easy in concept and inexpensive in execution, and create an important, intermediate level in the assessment of reservoir productivity. This paper proposes a framework for static connectivity analysis in reservoirs that use water flooding technique and pressure propagation fronts as it used in well testing. The software uses a fast marching method and a shortest path algorithm that both are sensitive to geological heterogeneities which can give some insights into finding the connective geobodies. An illustrative example is shown to describe the software interface and to present a simple but systematic connectivity analysis scenario. The distinct tasks contained in a typical reservoir development workflow may be benefited from the addition of connectivity analysis, such as the assessment of optimum well placements for injection-production wells and the evaluation of features of stratigraphic architectures that affect the recovery. The proposed tool is towards providing a geoengineering approach to use the geological knowledge for proposing better production scenarios.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".