Chapter 39 Using discovery process and accumulation volumetric models to improve petroleum resource assessment in Sverdrup Basin, Canadian Arctic Archipelago
Bibliographic record
Abstract
Abstract Sverdrup Basin hosts a structural petroleum play in Mesozoic clastic reservoirs. Twenty-one discovered fields (eight crude oil and 25 natural gas pools) have 294.1×106m3crude oil, and 500.3×109m3natural gas, original in-place contingent resources. We discuss and compare discovery process and volumetric assessment methods that, respectively, predict a 673.1×106m3or 698.7×106m3median crude oil resource and a 1187.4×109m3or 1202.8×109m3median natural gas resource. Both methods predict that the largest crude oil and third-largest natural gas pools are undiscovered, a result inferred to be consistent with available data and the exploration history. Volumetric assessments can precede any discoveries and they use common geoscience data inputs; however, they can be affected by data interdependencies and biases from exploratory sampling and subjective parameter estimates, particularly those affecting the number of accumulations. Discovery process methods solve for the accumulation numbers and size distribution simultaneously, accounting for sampling bias and free of data interdependencies, but only once sufficient discoveries exist. The Sverdrup Basin dataset and exploration history permit us to cross-validate volumetric and discovery process assessments and validate their predictions, for example undiscovered pool sizes, against a regional geoscience dataset. The discovery process results agree well with geoscience constraints, but the initial volumetric assessment must be restricted to predict undiscovered pool sizes consistent with the geoscience dataset. Our analysis illustrates advantages and potential pitfalls for volumetric and discovery process assessments and shows that cross-validation between methods and against available data constrains resource potentials and improves confidence in result.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".