Multi-stage fracturing of horizontal wells is an indispensable technology to create complex fracture networks, which can unlock production potential and enable commercial productivity for shale gas with low porosity and permeability. Real-time monitoring of fracture networks is essential for adjusting key parameters, mitigating fracturing risks, and achieving optimal fracturing effects. Micro-seismic monitoring technology accurately captures and describes the development of fracture networks by detecting micro-seismic waves generated through rock ruptures, providing valuable insights into the evaluation of post-fracturing. In this study, we first introduced the basic parameters of well X that were obtained by laboratory experiments and logging interpretation, including porosity, gas-bearing properties, mineral composition, rock mechanics, and crustal stress. Then, the hydraulic fracturing scheme was designed on the basis of the geological engineering characteristics of well X. Finally, we conducted a comprehensive analysis of various factors that can affect hydraulic fracturing. This included an examination of the impact of pre-fluid temporary plugging and fracture complexity on the overall effectiveness of the operation. Based on the laboratory experiments and theoretical analysis, the following conclusions can be drawn: (1) fracture size is essentially formed when the fluid strength exceeds 35 m³/m; (2) both preflush with high viscosity and the amount of power sand exceeding 20 cubic meters are conducive to the propagation of fracture height; (3) temporary plugging balls facilitate the balanced propagation of multiple fracture clusters within a stage, whereas temporary plugging particles promote the formation of complex fractures; and (4) geological conditions are a prerequisite for creating a complex network of fractures, and only engineering techniques can facilitate the appropriate enhancement of fracture complexity. This study provides an essential method for the fracturing design of shale gas.

In tight reservoirs, the rock formations are typically less porous and permeable, which makes it more difficult for hydrocarbons to flow through them. In addition to length and conductivity, the height of a fracture is another critical parameter of the hydraulic fracturing treatments in unconventional tight/shale formations, which determines the stimulated reservoir volume. If the fracture height is too shallow, the volume of rock exposed to the fluid and proppant may not be sufficient to improve the reservoir’s production significantly. Conversely, if the fracture height is too deep, the injected fluid may not be able to propagate high enough to reach the desired formation. However, after years of research, fracture height has often been simplified in traditional or recent studies of fracture simulation and estimation. The objective of this work is to propose an innovative way to simulate the hydraulic fracturing process in both horizontal and vertical directions in tight formations with a well-built finite element numerical model. Fracture toughness $K_{IC}$ is calculated based on the Brazilian test. Vertical fracturing fluid was also considered, and the model was validated by fracture height monitoring data from a stimulated well in the Montney formation. The influence of rock and fluid properties on the fracture height propagation was studied thoroughly with sensitivity analysis. The results indicated the fracture height prediction model was in good accordance with the monitoring data collected from the field, with an error margin of 7.2%. Sensitivity analysis results showed that a high Young’s modulus led to a larger stress intensity factor at the fracture tip, thus further advancing the fracture. Minimum horizontal stress also tends to facilitate the fracture to propagate. The influence of Poisson’s ratio and fluid viscosity on fracture height propagation was also investigated.

The development of tight gas reservoirs presents a significant challenge for sustainable development, as it requires specialized techniques that can have adverse environmental and social impacts. To address these challenges, efficient development technologies, such as multistage hydraulic fracturing, have been adopted to enable access to previously inaccessible natural gas resources, increase energy efficiency and security, and minimizing environmental impacts. This paper proposes a novel evaluation method to analyze the post fracturing productivity controlled by complex fault fracture zones in tight reservoirs. In this article, a systematic method to evaluate stimulated reservoir volume (SRV) and fault-fracture zone complexity after stimulation was established, along with the analysis and prediction of productivity through coupled fall-off and well-test analyses. Focusing on the Xujiahe formation in the Tongnanba anticline of northeastern Sichuan Basin, a 3D geological model was developed to analyze planar heterogeneity. The fall-off analytical model, coupled with rock mechanical parameters and fracturing parameters such as injection rates, fracturing fluid viscosity, and the number of clusters within a single stage, was established to investigate the fracture geometric parameters and complexities of each stage. The trilinear flow model was used to solve the well-test analysis model of multi-stage fractured horizontal wells in tight sandstone gas reservoirs, and well-test curves of the heterogeneous tight sandstone gas fracture network model were obtained. The results show that hydraulic fractures connect the natural fractures in fault-fracture zones. An analysis of the relationship between the fracture geometric outcomes of each segment and the net pressure reveals that as the net pressure in the fracture increases, the area ratio of natural fractures to main fractures increases notably, whereas the half length of the main fracture exhibits a decreasing trend. The overall area of natural fractures following stimulation is 7.64 times greater than that of the main fractures and is mainly a result of the extensive development of natural fractures in the target interval. As the opening ratio of natural fractures increases, the length of the main fractures decreases accordingly. Therefore, increasing net pressure within fractures will significantly enhance the complexity of fracturing fractures in shale gas reservoirs. Furthermore, the initial production of Well X1–10, which is largely controlled by fault-fracture zones, and the cumulative gas production after one year, are estimated. The systematic evaluation method in this study proposed a new way to accurately measure fracturing in tight reservoirs, which is a critical and helpful component of sustainable development in the natural gas industry.

Machine learning methods have gained significant attention in forecasting waterflooding performance in recent years, but their accuracy often remains insufficient for practical field applications. This study proposes a hybrid framework that integrates a linear dynamical system (LDS) with a neural network (NN). The framework improves oil-rate prediction by decomposing the injection–production relationship into linear and nonlinear components. Specifically, the aggregate injection rate is approximately linearly related to total liquid production, which is effectively captured by the LDS model, based on reservoir material balance principles. In contrast, the oil fraction of the produced liquid, defined as the ratio of oil rate to liquid rate, is bounded between 0 and 1 and typically decreases over time. This nonlinear trend is accurately modeled using a neural network (NN). The parameters of the LDS–NN framework are learned from historical injection and production data via a supervised training process. Furthermore, key hyperparameters within the model can be adjusted to optimize the performance for different reservoir characteristics. The proposed hybrid method is evaluated using both simulated reservoir cases and real field data, and compared against the performance of LDS-only and NN-only models. The results demonstrate that the LDS–NN framework consistently provides more accurate oil-rate predictions than either standalone LDS or NN approaches, across both synthetic and real-world waterflooding scenarios.