Simulation of Reservoir Charge to Predict Fluid Compositional Distribution: A New Way to Test the Geologic Model
Bibliographic record
Abstract
Abstract Reservoir fluids often exhibit compositional complexity vertically and laterally in reservoirs. These complexities include viscous oil and tar distributions, and gas-oil ratios and can also include more subtle fluid variations such as varying biomarker ratios and isotopic ratios. Recent advances have led to resolving of many mixing dynamic processes of reservoir charge fluids over geologic time. The objective is to simulate reservoir charge over geologic time to (a) constrain key attributes of the reservoir which comprise the geologic model and (b) to improve the prediction of fluid properties across tectonic features. The analysis of 80 reservoirs within the context of reservoir fluid geodynamics has allowed identification of mass transport and mixing dynamics of different charge fluids over geologic time. Reservoir simulation can be used to predict resulting compositional distributions; these predictions depend on (1) reservoir attributes, both known and uncertain, (2) the properties and locations of charge fluids, such as density and viscosity and (3) the time since charge. The comparison of predicted and measured fluid distributions allows history matching of reservoir charge. Fluid mechanics principles are shown to validate simulation results building confident in their predictions. Forward modeling with reservoir simulation shows that even simple 2D simulations can illuminate key reservoir attributes that impact fluid compositional distributions such as connectivity and baffling especially over different areal sections of the reservoir. A reservoir case study is used to validate the charge and mixing dynamics that are employed in modeling. Reservoir simulation shows that a substantial range of the extent of mixing is found dependent on reservoir and fluid properties, thereby providing a very sensitive test of these reservoir parameters. In addition, the location of charge also impacts the predicted compositional distributions across a reservoir. More comprehensive and complicated simulation models can be developed if preliminary, simple models show significant promise in testing important reservoir uncertainties. The impact of many parameters can be quantified including reservoir architecture, dip angle, aspect ratio, different aquifer configurations, various baffling structures, viscosities and density contrasts of the charge fluids, and the sequence of the fluid charges. Generalized systematics are developed which are very useful to characterize the dynamics of reservoir charge over geologic time. Simulation of reservoir charge for history matching is a very new concept, yet it relies on standard reservoir simulation (over geologic time) for comparison between predicted vs measured fluid compositional distributions of present day to test the reservoir and geologic models. This approach has shown that several presumptions about mixing of charge fluids were not general and inhibited the new workflow. Removing such conceptual limitations has been crucial to developing the novel workflows introduced in this paper to test the reservoir.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".