Forward stratigraphic modelling of marine petroleum source rocks: the case of the Carson Basin
Bibliographic record
Abstract
Organic matter rich rocks are the main component of any petroleum system. Marine organic matter deposits form a major part of these rocks and are the source of most oil. Vertical and lateral heterogeneities in marine petroleum source rocks are widely observed and owed to the dynamics of deposition and preservation of marine organic matter. Such source rock heterogeneities add major challenges to hydrocarbon exploration and estimation of resources, therefore, quantification of source rock potential using numerical prediction tools can contribute significantly to reducing exploration risks and enhancing the accuracy of resource assessment. We introduce here our innovative approach to modelling marine petroleum source rocks as part of an established forward stratigraphic modelling workflow. This enhanced workflow only requires minor additional input to a regular forward stratigraphic model in order to simulate marine source rocks deposition and preservation. Modelled source rock properties with this method include TOC, HI, thickness, net to gross, lithology, and other depositional environments properties allowing a sound source rock potential assessment. Additionally, we assess uncertainty, sensitivity, and risk on source rock potential using an innovative response surface modelling approach which provides an efficient and effective way to understand the controls on any output property and quantify the associated risk. To illustrate this new methodology, we will present the case of the Mesozoic Carson Basin offshore Newfoundland and Labrador, Canada.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".