Application of migration modeling to unconventional reservoirs: An example from the Montney Formation northeastern British Columbia
Bibliographic record
Abstract
In unconventional reservoirs, controls on fluid distribution exerted by fluid properties and petrophysical properties are critical to appraising resources and planning developments but are poorly understood. We propose that petrophysical properties combine with fluid properties to determine hydrocarbon migration in low porosity systems with small pores and evaluate these controls through numerical simulations of reservoir filling. During migration, two main forces are present: buoyancy and capillary pressure. The fluid properties affect the capillary pressure and govern the buoyancy force. Rock properties also impact the capillary pressure, a rock with smaller pore throat will have a higher capillary pressure, the rock wettability also affects the capillary pressure. In this case study, we test models for fluid distribution in an unconventional reservoir by simulating the migration of the hydrocarbons in the Montney Formation in western Canada. By injecting different fluid compositions in the model, and applying different capillary pressures to the rocks, we can examine relationships between fluid composition, capillary pressure, and fluid distribution and test reservoir charging scenarios. Because fluid properties vary with pressure and temperature, we also examine reservoir charging at different depths and under different geothermal gradients to understand how this affects migration in an unconventional reservoir like the Montney Formation. The insights gained on this project may be useful not only for the Montney Formation but also for other unconventional reservoirs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".