A Novel Workflow for Building Multiple Point Statistics Training Images from Virtual Outcrops
Bibliographic record
Abstract
Outcrop analogues of reservoirs are applied because facies-scale reservoir heterogeneities are frequently unresolvable at seismic-scale and well data provides sparse 1D geometrical data. Traditionally, geocellular models rely on manually measuring variograms or object dimensions from outcrops to define the geometry, size and directionality of facies proportions. Therefore, their ability to capture complex shapes and facies relationships in the subsurface is restricted by the quality of available geological data and the limitations of modelling algorithms. Multiple-point statistics (MPS) is a property modelling technique dependent on representative training images (TIs)- conceptual numerical descriptions of the geology expected in the reservoir under study. Lack of suitable TIs has limited the application of the MPS method to date. Recent advances in digital outcrop mapping methods, including lidar and photogrammetry, permit the rapid acquisition of high-resolution 3D virtual outcrop models. These provide a critical and underused source of qualitative and quantitative information for high quality TI generation. We present a novel approach to apply 3D virtual outcrops as TIs; coupled with the streamlining of lidar integration into subsurface models using examples from the Bolea area, Ebro Basin, northern Spain. This approach will significantly improve prediction of 3D facies heterogeneity and its impact on reservoir performance.
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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 | 0.001 |
| 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".