A Hybrid of Sequential-Self Calibration and Genetic Algorithm Inversion Technique for Geostatistical Reservoir Modeling
Bibliographic record
Abstract
Geostatistical modeling is a widely used approach to model the heterogeneity of reservoir petrophysical properties. This paper investigates a geostatistical-based inversion technique, a hybrid of sequential-self calibration (SSC) and genetic algorithm (GA), to model reservoir permeability. In this method, a GA is used to search the optimal master point locations, as well as the associated optimal permeability. These permeability values are then propagated to the entire reservoir using Kriging algorithm to match the dynamic production data. We demonstrate that GA is easy to implement and the results are robust. Additionally, we experimented with various numbers of master points, including a linked-list genotype which permits a flexible number of master points. The results show that GA is able to find various numbers of master points and their locations that are suitable for the reservoir field we studied. These numbers are within a small range and are sufficient to capture the heterogeneity of the reservoir permeability to match the production data. The ability of the SSC-GA method to model reservoir permeability by simultaneously optimizing the number of master points, the locations of these master points and the associated permeability in this case study suggests that the technique might be effective with other larger fields.
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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".