Direct Geostatistical Simulation With Multiscale Well, Seismic, and Production Data
Bibliographic record
Abstract
Abstract Multiple realizations of facies, porosity, and permeability are used for better representation of reservoir heterogeneity for more accurate performance forecasting and uncertainty assessment. These geostatistical realizations must reproduce all available data to be reliable. The available well, seismic, and production data are at different scales and must be linked to the reservoir modeling scale. The most common geostatistical simulation algorithms call for a Gaussian or normal transformation. It is not possible to merge the data types after this non-linear transformation. This has led to development of "Direct" approaches for simulation and data integration. Considering the variables without transformation ensures reproduction of the different data and the prescribed covariance or variogram model; however, until now, the resulting global histogram of the simulated realizations is not reproduced. The problem is that there is no theory that specifies the shape of the conditional distributions. The mean and variance are determined from the well-known normal or simple kriging equations; however, theory has not existed to specify the shape of the conditional distributions. A number of ad-hoc solutions have been proposed, but they violate the data and artificially reduce the modeled space of uncertainty. This paper develops a theory for determination of the required distribution shapes and reproduction of the global histogram. The results have significant theoretical and practical consequences. Data from multiple sources and scales can be directly reproduced in reservoir models with no need for transformation. There is no need for ad-hoc post-processing transformation or correction schemes. Several applications with synthetic data are shown to illustrate the technique.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".