Estimation of Permeability From Wireline Logs in a Middle Eastern Carbonate Reservoir Using Fuzzy Logic
Bibliographic record
Abstract
Abstract Permeability is one of the most difficult properties to predict, especially in carbonate reservoirs. The most reliable data of permeability, obtained from laboratory measurements on cores, do not provide a continuous profile along the depth of the formation. This paper presents the use of fuzzy logic modeling to estimate permeability from wireline log data in a Middle Eastern carbonate reservoir. In this study, correlation coefficients are used as criteria for checking whether a given wireline log is suitable as an input for fuzzy logic modeling. The coefficients are enhanced if they are evaluated with respect to the logarithm of core-based permeability values of the given well. After training the fuzzy model on a layer in a given well, permeability predictions were made for other layers in the same well. These predictions were in excellent agreement with permeability values obtained from cores. It was also observed that Subtractive Clustering technique gives better predictions of permeability when compared with Grid Partitioning technique. A parametric study was also conducted to see the effect of type and number of membership functions, combination of log input parameters, and data size on predictions of permeability. The possibility of training the fuzzy program on one well and testing it for other wells in the same formation is also explored.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".