Prediction of Reservoir Fluid Composition Using Basin and Petroleum System Modeling
Bibliographic record
Abstract
Abstract Apetroleum system modeling (PSM) study was performed on the Jeanne d’Arc Basin, offshore eastern Canada, to study the constraints and reliability of the reconstruction of petroleum reservoir filling histories. Petroleum generation and phase behavior were analyzed using phase-predictive compositional kinetic models (PhaseKinetics) determined by pyrolysis of Egret Member source rock samples. Various additional calibration data (well, rock, and fluid data), such as porosity, permeability, temperature (bottom-hole temperature, apatite fission tracks, fluid inclusions), maturity (vitrinite reflectance), and petroleum properties, such as API, gas-oil ratio, formation volume factor, and saturation pressure were integrated into this model. Different charge scenarios were tested for the effects of open and closed faults in the carrier system to reconstruct the most likely migration pathways for the petroleum that is trapped in the Terra Nova (TN) oil field. The most probable filling history includes charge to the reservoir from a local kitchen and a second kitchen located between Hibernia and TN that was responsible for the long-range migration. In the model, the hydrocarbons migrate from this kitchen in the northwest part of the study area along pathways defined by closed transbasin faults from the north into the field. This new migration concept differs from the traditional explanation based on geochemical measurements only von der Dick et al., 1989), which infers that local generation was solely responsible for filling the TN field. The latter can be disproved based on a simple mass balance calculation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".