Streamline Technology for the Evaluation of Full Field Compositional Processes; Midale, A Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Traditionally, the evaluation of CO2 flooding processes is performed using finite difference compositional simulation models. However, compositional simulation is impractical for modeling large-scale CO2 floods due to computational runtime restrictions. In cases where reservoir heterogeneity and fluid mobility dominate the reservoir recovery mechanism, streamline simulation offers a viable alternative to compositional simulation. The "reduced" physics in streamline simulation allows field scale CO2 flood modeling to be feasible, as long as the streamline PVT model can be calibrated such that the streamline model will produce accurate results for CO2 injection processes. Using streamline simulation allows for the evaluation of multiple full field development scenarios that would otherwise not be possible using compositional simulation. The objective of the study was to provide CO2 flood performance forecasts under various full field development scenarios for the Midale field. This paper focuses on the methodology and results from the 1000 well, >400,000 grid blocks, and 45+ years of history streamline simulation of the Midale field. In particular, it discusses the construction and history match of the full field model, calibration of the streamline model with the compositional model and development of the full field CO2 forecasts.
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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 | 0.000 |
| 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 it