Prediction of Electrofacies Based on Flow Units Using NMR Data and SVM Method: a Case Study in Cheshmeh Khush Field, Southern Iran
Bibliographic record
Abstract
The classification of well-log responses into separate flow units for generating local permeability models is often used to predict the spatial distribution of permeability in heterogeneous reservoirs. The present research can be divided into two parts; first, the nuclear magnetic resonance (NMR) log parameters are employed for developing a relationship between relaxation time and reservoir porosity as well as introducing the concept of relaxation group. This concept is then used for the definition of electrofacies in the studied reservoir. A graph-based clustering method, known as multi resolution graph-based clustering (MRGC), was employed to classify and obtain the optimum number of electrofacies. The results show that the samples with similar NMR relaxation characteristics were classified as similar groups. In the second part of the study, the capabilities of nonlinear support vector machine as an intelligent model is employed to predict the electrofacies and permeability distribution in the entire interval of the reservoir, where the NMR log parameters are unavailable. SVM prediction results were compared with laboratory core measurements, and permeability was calculated from stoneley wave analysis to verify the performance of the model. The predicted results are in good agreement with the measured parameters, which proves that SVM is a reliable tool for the identification of electrofacies through the conventional well log data.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".