An Improved Fractal Model for Characterizing Spatial Distribution of Undiscovered Petroleum Accumulations
Bibliographic record
Abstract
Study of a mature petroleum play in the Western Canada Sedimentary Basin (WCSB) indicates that the spatial distribution of petroleum accumulations exhibits a self-affinity characteristic. This characteristic motivated the examination of a fractal model for a quantitative description of petroleum resource spatial distribution. The proposed approach transforms the spatial information with respect to discovered petroleum accumulations into a frequency domain, represented by an amplitude map and a phase spectrum. The amplitude map is then calibrated using a fractal model, inferred from exploration data, to account for the sampling bias in exploration procedure. The information in the obtained phase spectrum provides no clue with respect to the locations of undiscovered accumulations, and cannot be enhanced by the established fractal model either. If the calibrated amplitude map and a random phase map are transformed back to the spatial domain and conditioned on the discovered petroleum accumulations, the resulting map is equivalent to one of equal-probable realisations from a conditional simulation. Improvement can be made by extracting information with respect to locations of undiscovered petroleum deposits from geological factors controlling the formation of petroleum accumulations in a petroleum system. Using additional quantitative models, such as a geological favorability map or a map of probability of petroleum occurrence, allows an improved characterisation of spatial distribution of petroleum accumulations by the fractal model. An example from the Western Canada Sedimentary Basin illustrates the application of the method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".