Integrated Reservoir Simulation and Basin Models: Reservoir Charging and Fluid Mixing
Bibliographic record
Abstract
Abstract Perhaps the most important unconstrained aspect of petroleum systems analysis concerns the charging and emplacement of petroleum to a structure or prospect. Petroleum charge rates, leakage, and spill control petroleum residence time in a reservoir, which is fundamental for prediction of biodegration rates, seal integrity (failure), and oil quality, all of which are affected by fluid mixing processes. While forward models can estimate plausible charge rates based on thermal histories, there are no field data proxies for the charge rates that are necessary to constrain migration and charge models. To solve this problem, we are coupling high resolution, full physics reservoir simulation protocols to full 4D basin models such that gradients in petroleum compositions from models and from chemical analysis can be used to constrain charges rates. This new generation of hybrid reservoir simulator/basin models necessitates rapid high resolution fluid mixing solvers and multicomponent fluids. This paper deals with the development of compositional fluid mixing simulators that can be coupled with basin modeling. These simulators will enable the forward simulation of detailed reservoir charging and fluid property evolution, coupling the effects of advection, diffusion, gravity segregation, and biodegration to predict the development of compositional gradients in petroleum columns that can be used to constrain reservoir charging and alteration processes. In this paper the effects of advection, diffusion, and gravity segregation are particularly studied. The traditional simulator for solving the isothermal gravity/chemical equilibrium problem is deduced as a special example of the simulators presented here. Numerical experiments to show these effects are given.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".