Evaluation of Matrix-Fracture Imbibition Transfer Functions for Different Types of Oil, Rock and Aqueous Phase
Bibliographic record
Abstract
Abstract Several different versions of the transfer functions evolved from classical Mattax and Kyte’s dimensionless group and Aranofsky et al.’s abstract relationship were tested. Another transfer function derived analytically based on power law relationship between recoverable oil and time was also included in the testing process. The exponents in all these transfer functions reflect the strength and type of the transfer. The recovery curves obtained from the spontaneous imbibition of different aqueous phases (brine and surfactant) into cylindrical rock samples saturated with different types of oil were used to fit the transfer functions. The exponents yielding the best fit to experimental data were obtained and correlated to the effective parameters such as the viscosity of oil, matrix permeability, IFT, matrix size, and wettability using multivariable regression analysis. The correlations developed were analyzed for the rock and oil types, and IFT. It was observed that the exponential relationships were more suitable for synthetic and processed oil samples whereas the power law transfer functions were more applicable for crude oil cases. It is hoped that the analysis provided in this paper would facilitate the selection of proper transfer function type for performance estimation of naturally fractured reservoirs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".