Understanding Reservoir Mechanisms Using Phase and Component Streamline Tracing and Visualization
Bibliographic record
Abstract
Abstract Streamline simulation has received considerable attention because of its computational efficiency and also for being visually appealing and physically intuitive. Conventionally streamlines are traced using total fluid fluxes across the grid cell faces. The visualization of total flux streamlines shows the movement of tracer and water flood front, injector-producer relationship, swept volumes for injectors and drainage volumes for producers. However, total fluxes mask many important features of reservoir flow embedded in the individual phase fluxes. These include reservoir dynamics, such as phase distribution, appearance and disappearance of phases, local drive mechanisms and reservoir miscibility conditions for CO2 or solvent flooding. In this paper we demonstrate the benefits of visualizing phase and component streamlines, which are traced using phase and component fluxes respectively. Both three-phase black oil and compositional simulation are used to visualize reservoir flow and to understand the drive mechanisms active in the reservoir. Although the phase and component streamlines are not suited for flow simulation because of their local discontinuities, these streamlines provide unique insight into the reservoir processes and recovery mechanisms. In this study, the phase and component streamline tracing is done using phase/component fluxes from commercial finite-difference black oil and compositional simulators. We demonstrate the power and utility of the phase and component streamlines using synthetic and field examples. The phase streamlines are shown to capture the dominant flowing phases in different parts of the reservoir and to help us optimize locations of infill injectors and producers. Based on the appearance and disappearance of phase streamlines, we can identify the regions of the field where different drive mechanisms such as waterflood and solution gas drives are active. The field applications involve waterflooding in a structurally complex reservoir in South America and also, a CO2 injection project in a large carbonate reservoir in Canada. We use component streamlines to track the movement of injected CO2 in the reservoir. By tracking the phase and component streamlines, we can clearly distinguish the CO2 in the gas phase and the dissolved CO2. This not only helps optimize CO2 injection but also has important implications in the effectiveness of the CO2 sequestration.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".