An Improved Probability Conditioning Method for Constraining Multiple-Point Statistical Facies Simulation on Nonlinear Flow Data
Bibliographic record
Abstract
Abstract We evaluate the use of facies probability maps for conditioning discrete multiple-point statistical (MPS) facies simulation on dynamic production data. In MPS simulation, conditional probabilities are estimated from a training image (TI), a conceptual model of geologic connectivity, and used to draw samples of facies distributions that are statistically consistent with those encoded in the TI. Whereas conditioning MPS simulation on both static hard data (e.g., well logs) and soft data (e.g., seismic) is straightforward, calibration of facies against nonlinear flow data is nontrivial. The use of facies probability maps for conditioning MPS simulation on dynamic production data presents a promising approach for calibration of complex facies models. We present an overview of MPS-based conditional simulation with probability maps and discuss some of the important properties and implementation issues of this approach. The paper presents two important contributions: (1) improvement of the original probability conditioning method (PCM) by constructing the facies probability maps based on the first and second order statistical moments of the updated permeabilities at each cell; (2) generalization of the "tau" model to include pixel-based τ values that can assign different confidence levels to the facies probabilities at different grid blocks. Results from numerical examples demonstrate that the proposed approach outperforms the original PCM by incorporating additional information from the observed dynamic data into MPS-based facies simulation.
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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 | 0.001 |
| 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".