Parameter Space Reduction and Sensitivity Analysis in Complex Thermal Subsurface Production Processes
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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