Bibliographic record
Abstract
The enhanced-oil-recovery (EOR) literature produced in the past several months was dominated by reservoir modeling and characterization; flood enhancements; machine learning; and, more notably, relative permeability estimation. This last one needs to be under-stood further. Relative permeability characterizes flow in porous media, the understanding and manipulating of which is key to the success of EOR. Our fascination with the topic during the last 75 years, therefore, is understandable. Starting with a seminal paper from W.R. Purcell in 1949, we have more than 12,000 articles in the SPE collection alone on relative permeability estimation, of which more than 400 were published in the last year. Mechanics of motion is also important to mankind, but we do not have literature piling up on Newton’s laws of motion. Is it that we haven’t understood flow in porous media yet, or is it because the subject matter is so complex? The truth, perhaps, lies somewhere in between. Flow in pores that are randomly sized, randomly connected, and have variable chemical makeup is, by nature, complex and mathematically unmanageable. Relative permeability has been our attempt to lend its aggregate behavior some sense of manageability. This has always been done in a fit-for-purpose manner. What is fit for one context, however, may not be so for the others, and this uncertainty continues to churn out theses, antitheses, and syntheses. Expectedly, even more such activity arises with every improvement in computing capabilities, as is once again evident with advances in machine learning—new tools to handle old problems. That is where my first pick is for you, with some additional suggested readings in references that follow. Data analytics does much more than estimate relative permeability, and the second paper abridged here uses it to predict a flood performance. To allow a break from data science, the third paper chosen deals with the important topic of electromagnetics as applied to reservoir characterization and heating. I hope you find these to be useful and interesting reads.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.303 | 0.224 |
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".