Effects of Memory on the Complex Rock-Fluid Properties of a Reservoir Stress-Strain Model
Bibliographic record
Abstract
The memory based stress-strain model developed earlier by Hossain et al. (2007) Hossain, M. E., Mousavizadegan, S. H., Ketata, C. and Islam, M. R. 2007. A novel memory based stress-strain model for reservoir characterization. Journal of Nature Science and Sustainable Technology, 1: 653–678. [Google Scholar] has been solved numerically in this study. The derived mathematical model introduces the effects of temperature, surface tension, and pressure variations and the influence of fluid memory on the stress-strain relationship. The variation of shear stress as a function of strain rate is obtained for fluid in a sample oil reservoir to identify the effects of fluid memory. The stress-strain formulation related with the memory is taken into account, and we obtain the variation of it with time and distance for different values of α. The dependency of the stress-strain relation on fluid memory is considered to identify its influence on time. As pressure is also a function of space, the memory effects on stress and strain are shown in space with the pressure gradient change. The computation indicates that the effect of memory causes nonlinearity, leading to chaotic behavior of the stress-strain relationship. This model can be used in reservoir simulation and rheological study, well test analysis, and surfactant and foam selection for enhanced oil recovery.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".