Case Study: Dynamic Visualization of Miscibility for EOR Design and Implications for Field Planning
Bibliographic record
Abstract
Abstract CO2 flooding is a well-known enhanced oil recovery method which is made attractive both by the opportunity to recover significant additional oil and the opportunity to sequester CO2 at abandonment. However, as the acquisition and compression of CO2 is costly, the flood must be carefully designed to maximize recovery from the injected volumes. Typically, design is done with a plan to maintain reservoir pressure above the minimum miscibility pressure (MMP), but this study investigates if a far more detailed analysis of miscibility is warranted. A pilot CO2 flood in Western Canada and its existing development plan are investigated. Instead of a relying on the single MMP from slim tube tests, miscibility is calculated on cell-by-cell basis with a tuned equation of state (EOS) and history matched reservoir model. A ‘distance’ to miscibility is calculated, in pressure terms, by comparing the saturation pressure to the cell pressure throughout the flood. The result is then visualized to illuminate possible improvements. Because it takes in account changes in pressure, composition and temperature in both space and time, this method may model important reservoir phenomena that are not considered with the slim tube method - including channeling, buoyancy, heterogeneity, multiple gradients with depth, and cross-flow diffusion. The calculation was applied to the field development plan to evaluate the effectiveness of the current strategy. Even though the MMP conditions were nominally met, the new parameter highlights areas where miscibility was not occurring and oil was being by-passed. The measure also shows areas where the CO2 concentrations were excessive, and so the injectant was underutilized in sweeping oil towards producers. Based on these results, changes to the field development plan were proposed. Well plans and operating constraints were altered to improve downhole mixing and miscibility, leading to improvement in predicted oil recovery and economic measures such as capital expenditure, operating expenditure and net present value. These results demonstrate that MMP can be overly simplistic in the design of miscible flooding strategy. The case-study presented adds to the database of past experience when designing EOR floods, while providing a simple visualization parameter to aid engineers in understanding and optimizing miscible recovery design. It is particularly useful for multiple contact floods, for fields with limited CO2 availability, and where in-situ heterogeneity, gradients with depth, and diffusion phenomena complicate recovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".