High-Resolution Numerical Simulations of Capillary Trapping for Carbon Dioxide in Fractured Formations
Bibliographic record
Abstract
Abstract CO2 injection is one of the most common forms of enhanced oil recovery. It also allows us to sequester greenhouse gases in the subsurface. CO2 injection is currently considered for many clastic and carbonate reservoirs. The latter reservoirs often contain fractures, therefore quantification of sweep efficiency and CO2 trapping in fractured formations is of critical importance to design appropriate CO2 injection schemes for carbonate reservoirs. Discrete fracture and matrix (DFM) simulations have emerged as a powerful technology to analyse the fundamental flow and transport properties in naturally fractured reservoirs and bridge gaps between geosciences and reservoir engineering: they help to validate upscaling workflows, improve the analysis of pressure transients from well-tests, and allow to explore how uncertainties in fracture network properties impact hydrocarbon recovery. The key difference between DFM simulations and traditional dual-porosity approaches is that the structurally complex fracture geometries are explicitly discretised as 2D surfaces in a 3D reservoir model, which hence comprises a geologically realistic representation of the fracture patterns and rock matrix. Here we extend the DFM simulation workflow to account for capillary trapping of CO2 in fractured media. Fluid flow is described by a fully compositional model including an equation of state for CO2-H2O-NaCl fluids. The governing equations are discretised in space using unstructured and mixed-dimensional finite element - finite volume techniques. We demonstrate how our new DFM approach can be applied to simulate CO2 injection and trapping of CO2 in fractured geological formations with geologically realistic fracture networks. We show how matrix diffusion and capillary forces influence the rate at which CO2 is trapped in the rock matrix and discuss how this trapping could be modelled more accurately in conventional reservoir simulators.
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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) | 0.003 | 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".