Models of hydrocarbon reservoirs are often used to support management decisions about field development and redevelopment. Typical modelling workflows result in a reservoir and simulation model with a property distribution generally comparable to the well data, and this is often considered sufficient. The current study tests this assumption on the fluvial reservoir in the Upper Lunde Member of the Snorre Field where sedimentary heterogeneities at multiple scales influence reservoir properties such as porosity and permeability. This work shows that, by describing and modelling the sedimentary heterogeneities at several length scales in the reservoir, and by using a flow-based local upscaling method, the resulting porosity and permeability distribution at the scale of the reservoir and simulation model are significantly different from porosity and permeability distribution at the well data scale; the variance tends to reduce and, for permeability, the distribution type is changed from log-normal to normal. Reservoir property distributions based on multiscale modelling, sensitive to the representative elementary volumes for permeability, and upscaled in a realistic sedimentological framework, give a better representation of the effective permeability architecture.

Fracturing and refreezing of sea ice in the Kara sea are investigated using complex network analysis. By going to the dual network, where the fractures are nodes and their intersections links, we gain access to topological features which are easy to measure and hence compare with modeled networks. Resulting network reveal statistical properties of the fracturing process. The dual networks have a broad degree distribution, with a scale-free tail, high clustering and efficiency. The degree-degree correlation profile shows disassortative behavior, indicating preferential growth. This implies that long, dominating fractures appear earlier than shorter fractures, and that the short fractures which are created later tend to connect to the long fractures. The knowledge of the fracturing process is used to construct growing fracture network (GFN) model which provides insight into the generation of fracture networks. The GFN model is primarily based on the observation that fractures in sea ice are likely to end when hitting existing fractures. Based on an investigation of which fractures survive over time, a simple model for refreezing is also added to the GFN model, and the model is analyzed and compared to the real networks.

No abstract

No abstract