An integrated approach to seismic stimulation of oil reservoirs: laboratory, field and theoretical results from doe/industry collaborations
Bibliographic record
Abstract
It has been observed repeatedly that low-frequency (10-500 Hz) seismic stress waves can enhance oil production from depleted reservoirs . Until recently, the majority of these observations have been anecdotal or at the proof-of-concept level. The physics coupling stress waves to multiphase fluid flow behavior in porous media is still poorly understood, even though numerous underlying physical mechanisms have been proposed to explain the observations . Basic research on the phenomenon is being conducted through a U .S. Department of Energy funded collaboration between Lawrence Berkeley National Laboratory, the University of California at Berkeley, Los Alamos National Laboratory and the U .S . oil and gas industry . The project has focused on three main areas of research: (1) laboratory core flow experiments, (2) field seismic monitoring of downhole stimulation tests, and (3) theoretical modeling of the coupled stress/flow phenomenon . The major goal is to obtain a comprehensive scientific understanding of the seismic stimulation phenomenon so that field application technologies can be improved. Initial developments and experimental results in all three research focus areas confirm historic observations that the stimulated flow phenomenon is real and that a fundamental scientific understanding can be obtained through continued research . Examples of project results and developments are presented here.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".