Grid-free petroleum reservoir characterization with truncated pluri-Gaussian simulation: Hekla case study
Why this work is in the frame
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Bibliographic record
Abstract
A new geostatistical grid-free simulation (GFS) method has been developed recently that represents simulated continuous attributes of the natural phenomena, such as stratigraphic surface boundaries or petrophysical properties, as an analytical function of the coordinates of the simulation locations. Thus, GFS resolves challenges related to model regridding, increasing resolution around already simulated locations and integration of newly available data in a consistent manner. The present paper contains further developments in simulation of categorical variables, such as facies, in a grid-free fashion based on the truncated pluri-Gaussian simulation (TPG) paradigm. The resultant simulation engine allows the entire reservoir system to be represented as an analytical stochastic function: that is, values of any reservoir properties are simulated on demand at requested locations in space. The selection of proper variograms of Gaussian continuous variables for simulation of categorical variables in a TPG framework is proposed through a methodology based on Monte Carlo simulation. The variogram models of the underlying Gaussian continuous variables are obtained by minimizing the difference between numerically computed and target indicator variograms. A local optimization approach is suggested for a fast precise derivation of the variograms. The stable variogram model leads to the closest fit to the experimental variograms of the continuous variables. The automatic establishment of a truncation mask based on multidimensional scaling to convert Gaussian continuous variables to categories is also explained. Finally, the proposed GFS algorithm for petroleum reservoir characterization is demonstrated in its full-scale applicability to the Hekla offshore petroleum reservoir located in the North Sea. The results look promising, and should be beneficial to petroleum reservoir modelling in practice.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it