Using copulas for implementation of variable dependencies in petroleum resource assessment: Example from Beaufort-Mackenzie Basin, Canada
Bibliographic record
Abstract
Abstract Petroleum resource potential modeling seeks to characterize undiscovered petroleum resources. This information from the modeling can contribute to a reduction in corporate risk while characterizing the commercial potential of the undiscovered resources. Such models consider different types of variable dependencies arising from geologic risk evaluation, volumetric calculation, and resource aggregation to higher geographic levels. Commonly, the available data are not sufficient to specify such variable correlations or interdependencies, particularly in frontier regions. It is also a challenge to formulate variable correlations in resource calculations because geologic variables have to be fit to a multivariate lognormal distribution or other specific multivariate distributions with an appropriate correlation structure. However, variable correlations are common among the geologic variables, and ignoring the interdependencies may lead to a serious bias in the resource potential estimation and the uncertainty range. Recent methodological developments in statistics indicate that the use of copulas permits more flexibility for the consideration and incorporation of variable interdependency, thus analogs can be introduced to problems where estimating correlation structures are impossible and wider choices of statistical distributions become available. This article proposes the use of copulas for handling variable dependency in petroleum resource assessment. The methods and procedures are illustrated using examples from a hypothetical data set and the crude oil resource appraisal of Tertiary clastic plays in Beaufort-Mackenzie Basin in Arctic Canada. Comparisons of crude oil resource estimates obtained using different correlation scenarios for these plays suggest that when positive correlations are used, the mean value of the oil resource is increased about 1.6 times that estimated, assuming a complete independence among the input variables.
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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 | 0.000 |
| 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) | 0.001 | 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 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".