Prospective Resources Evaluation Offshore Newfoundland and Labrador -Uncertainty and Risk Assessment Using Machine Learning Processes
Bibliographic record
Abstract
Summary Physical and geochemical models allow the simulation and quantification of the various geological processes leading to the presence of oil and gas accumulation. The models have to be calibrated to available data, within an acceptable precision, including wells (lithology, petrophysics, geochemistry), existing fields, and seismic attributes such as impedance, AVO, and other attributes. These models require High Performance Computing capabilities. The machine learning process consists of building a function that reproduces some results of a few tenths of complex models (the volumes of the in place prospective resources for instance). This response function is able to generate thousands of results rapidly. This function is used to define the probabilistic distribution of the outcomes (HC prospective resources). In the case of offshore Newfoundland and Labrador, in areas where well control is limited (i.e. Flemish Pass Basin, Orphan Basin, Carson Basin, Chidley Basin), this model based approach has been used to estimate unrisked oil and gas volumes in place distribution (P90, P50 and P10) - ( 2015–2020 CNLOPB Resource Assessment). The pitfalls of this new approach will be discussed, in light of the uncertainties of the geological models and related impact on the volume distribution.
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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 | 0.000 |
| 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) | 0.000 | 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".