Weiwei Zhu

and 5 more

The stochastic discrete fracture network (SDFN) model is a practical approach to model complex fracture systems in the subsurface. However, it is impossible to validate the correctness and quality of an SDFN model because the comprehensive subsurface structure is never known. We utilize a pixel-based fracture detection algorithm to digitize 80 published outcrop maps of different scales at different locations. The key fracture properties, including fracture lengths, orientations, intensities, topological structures, clusters and flow are then analyzed. Our findings provide significant justifications for statistical distributions used in SDFN modellings. In addition, the shortcomings of current SDFN models are discussed. We find that fracture lengths follow multiple (instead of single) power-law distributions with varying exponents. Large fractures tend to have large exponents, possibly because of a small coalescence probability. Most small-scale natural fracture networks have scattered orientations, corresponding to a small κ value (κ<3) in a von Mises–Fisher distribution. Large fracture systems collected in this research usually have more concentrated orientations with large κ values. Fracture intensities are spatially clustered at all scales. A fractal spatial density distribution, which introduces clustered fracture positions, can better capture the spatial clustering than a uniform distribution. Natural fracture networks usually have a significant proportion of T-type nodes, which is unavailable in conventional SDFN models. Thus a rule-based algorithm to mimic the fracture growth and form T-type nodes is necessary. Most outcrop maps show good topological connectivity. However, sealing patterns and stress impact must be considered to evaluate the hydraulic connectivity of fracture networks.

Jiangtao Zheng

and 3 more

Spontaneous imbibition of the injected fluid into the pore space of a tight oil reservoir and replacing the crude oil therein has been considered as one of the possible mechanisms in increasing oil recovery. Such deeply buried reservoir rocks is usually under high-pressure and high-temperature conditions. Besides, their interior complex porous structures are usually characterized as pore bodies and slit-shaped pore throats. As a result, an accurate description of the spontaneous imbibition behavior driven by capillary force in the real pore space under reservoir conditions is crucial to understand the process and uncover the controlling mechanisms. An improved multi-component pseudo-potential lattice Boltzmann method was developed to simulate the spontaneous imbibition behavior in a representative 3D pore space extracted from a tight sandstone reservoir rock. Comparison of the spontaneous imbibition behavior under ambient condition and reservoir condition showed that the latter case exhibited two times faster of the imbibition. Moreover, a snap-off of the oil droplet phenomenon was observed in the pore bodies surrounded by slit-shaped pore throats. The snap-off oil droplets stuck in the pore bodies and accounted for 9.47 % of the pore volume. These results indicated the importance of investigating the spontaneous imbibition in a real porous structure and under actual reservoir condition. The proposed pore-scale simulation method provides a useful tool in understanding the complex spontaneous imbibition pattern and the resulted enhanced oil recovery.

Yang Liu

and 4 more

The pore network is an approximate representation of the void space of porous materials such as rocks and soil via pores (corresponding to large cavities) and throats (narrow constrictions). During extraction of networks from real void space, ambiguous definitions of pores and throats may cause significant errors in predictions of single/multi-phase transport properties. Meanwhile, the pore-throat segmentation needs to exclude non-physical parameters as much as possible. In this work, we propose a pore-throat segmentation method based on local hydraulic resistance equivalence between the real space and the pore-throat geometry. The pore-throat boundary is locally determined at the position where the pore network preserves the hydraulic resistance of the real space most. This local segmentation method ensures better equivalency between extracted pore-network and real pore space without any impirical and non-physical parameters. After validations of accuracy and reliability by standard benchmarks, this method is appled to real porous materials including spherical pack, sandpack, sandstone, and carbonate. The absolute permeability and relative permeability predicted by the new pore-network model (PNM) agree well with the experimental data and the direct simulation results. The proposed method improves the accuracy of PNM predictions significantly with only slight increases of computational costs. This local pore-throat segementation method may enhance capability of PNM and extend PNM to more complicated cases.