Fractured porous media exist widely in reservoir rocks and fractures play a critical role in fluid flow processes. High resolution direct numerical simulations of fluid flow can provide important insight into pore‐scale processes and flow mechanisms in fractured permeable media, however, the tremendous computational costs prevent these methods from being applied into larger scale models. Pore network modeling is an alternative solution to this problem, but currently it can only be used in porous media without fractures. This work is concerned with incorporating discrete fractures into pore network modeling to represent fractured porous media. A new fracture matrix pore network model (FM‐PNM) is developed to efficiently simulate the fluid flow properties in fractures associated with the pore matrix. Discrete fractures are transformed into a fracture pipe network and integrated with the pore matrix that is represented by a network of pore bodies and pore throats. These two networks are coupled together to create a single nested network which is topologically equivalent to the fractured porous medium. This method extends the scope of applications of pore network modeling to fractured porous media. The permeability of the coupled network (FM‐PNM) is benchmarked by Lattice Boltzmann simulation for various structures of pore matrix and discrete fracture networks.

Fractures widely present in the subsurface and play a critical role in the fluid flow processes in porous media. The Fracture Pipe Network Model (FPNM) is an efficient method to represent and calculate fluid flow properties as a particular part of Discrete Fracture Networks (DFNs) method compared to direct numerical simulations. However, the current FPNM formulation can result in large deviations in computed fluid flow properties when applied to complex interconnected DFNs, although it can produce good results for simple DFNs. To enhance the performance and versatility of current FPNMs, four modifications to the FPNM formulation are introduced from different perspectives to improve the accuracy of pipe conductance assignment and ensure the correct topology of the fracture network. Two benchmarking examples with complex interconnected fractures and two real fractured samples are presented and the results show the modifications significantly improve the accuracy of computed fluid flow properties in complex DFNs.

Dynamic reserves evaluation is crucial for the development and assessment of gas reservoirs. However, ultra-deep fractured tight sandstone gas reservoirs pose unique challenges due to their abnormally high pressure, tight matrix, and complex fracture distribution. This paper proposes a simplified forward calculation method that incorporates the fracture length for the rapid estimation of dynamic reserves in fractured tight sandstone gas reservoirs. This method was based on the pressure change rate equation and considered the unique characteristics of fractured gas reservoirs. Numerical simulations were conducted to analyze the sensitivity of the proposed method. The proposed method was applied to estimate the dynamic reserves of a fractured gas reservoir, and the results closely approximate the well group method, indicating its accuracy. The main advantage of this method lies in its simplicity, allowing field engineers to perform rapid dynamic reserve evaluations.

The development of heavy oil reservoirs in China is of great significance to safeguard national energy security, but great challenges are faced due to the complex and heterogeneous reservoir properties. Inter-well connectivity analysis is critical to enhancing the development performance, as it is a good way to interpret fluid flow and provides a theoretical basis for injection-production optimization. Data-driven deep learning methods have been widely used in reservoir development and can be employed to develop surrogate models of injection and production and to infer inter-well connectivity. In this study, the model performance of a recurrent neural network (RNN) and its four variants were evaluated and compared in a temporal production prediction. The comparison results showed that bidirectional gated recurrent unit (Bi-GRU) is the optimal algorithm with the highest accuracy of 0.94. A surrogate model was established to simulate the inter-well connectivity of steam-assisted gravity drainage (SAGD) in the research area by utilizing the Bi-GRU algorithm. A global sensitivity analysis method, Fourier amplitude sensitivity testing (FAST), was introduced and combined with the surrogate model to explain the influence of the input variables on the output variables by quantitatively calculating the sensitivity of each variable. Quantitative results for the inter-well connectivity of SAGD were derived from the sensitivity analysis of the proposed method, which was effectively applied to typical linear patterns and five-spot patterns. Inter-well connectivity varied from 0.1 to 0.58 in test applications, and mutual corroboration with previous geological knowledge can further determine the distribution of the interlayer in the reservoir. The workflow proposed in this study provides a new direction for analyzing and inferring the inter-well connectivity of SAGD in Northeast China heavy oil reservoirs.