SGS Versus Collocated Cokriging Petrophysical Modeling: A Comparative Study in a Heterogeneous Gas Condensate Carbonate Reservoir
Bibliographic record
Abstract
Abstract Building an accurate static model for entire field was the primary objective in the reservoir characterization and simulation. The goal was to develop a model with sufficient detail to represent reservoir discontinuity and petrophysical properties in field scale. There are different geostatistical methods for petrophysical modeling. Sequential Gaussian simulation (SGS) is a kriging based algorithm which simulates nodes after each other sequentially, subsequently using simulated values as a conditioning data. It is necessary to use standard Gaussian values in SGS method. Collocated Cokriging (C.C) is a reduced form of Cokriging, which requires knowledge of only the hard data covariance model, the correlation coefficient between the hard and soft (auxiliary) data, and the variances of the two attributes. In this study, porosity and permeability of a heterogeneous gas condensate carbonate reservoir are modeled first by SGS method. Than seismic attributes with high distribution density but low resolution are used as an auxiliary variable for porosity modeling from well logs data (limited distribution but high resolution).Determining which seismic attributes are meaningful to assist in the modeling and estimation process requires statistical analysis. In second step the modeled porosity by C.C is used as an auxiliary variable for permeability modeling. So in this method, seismic attributes have an indirect role in permeability modeling as well. A common practice usually involves the use of a cross validation scheme, where each well is removed sequentially, and its property is predicted using information from the remaining wells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".