Parameter Space Reduction and Sensitivity Analysis in Complex Thermal Subsurface Production Processes
Bibliographic record
Abstract
As conventional resources for liquid fuels in the world become scarcer and less secure, there is a need to develop other feasible resources. Oil shale is a massive resource local to the United States for potential liquid fuel production. In situ oil shale processing strategies are attractive for reduced environmental impact (in comparison to surface production operations) and provide access to resources inaccessible to mining. The efficiency of feasible and economical development is greatly enhanced with predictive power that is both efficient and accurate. However, modeling thermal subsurface processes is a complex problem, involving many simultaneously occurring physical phenomena. In this study, an oil reservoir simulator capable of representing thermal processes was used to explore the impact and interplay of various pertinent parameters to an in situ oil shale processing strategy. A statistical methodology was developed using designed factorial experiments (simulations) to expose probable dominating parameters, including synergistic or diminutive interactions between parameters. An empirical regression model or response surface was built from the simulated data. Monte Carlo simulations were used to characterize the response surface and to estimate the uncertainty in predicted oil recovery results because of the explored 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".