Journal of Energy Engineering

Horizontal well fracturing in the roof of coal seams has been demonstrated to be an effective technique for extracting coalbed methane. However, the mechanism of hydraulic fracturing fracture expansion from roof to coal seam and penetrating the interlayer in multiple stage fracturing is not comprehensive. Mechanical properties of the reservoir, fracturing parameters, and fracturing mode are the mainly factors that influence extraction efficiencies. To explain the relationship between fracturing behaviors and coal, the interlayer and roof along with the fracture propagation and penetration in multiple stages of fracturing; the interaction mechanism of fracture propagation; and penetration with elastic modulus, injection velocity, and fluid viscosity are simulated in single and staged fracturing. Additionally, the effect of the fracturing mode on fracture propagation is considered. The results show that the coal seam strength dominantly influences longitudinal fracture propagation while marginally affecting circumferential stress. Significant modulus differences between coal-rock interlayers and coal seams hinder fracture propagation. In the case of a low injection rate and fluid viscosity, increasing the injection rate and fluid viscosity can help enlarge the fracture area and facilitate penetration. Multistage fracturing and its high injection rates are conducive to maximizing the efficiency of fracturing operations. These results contribute to understanding and optimizing hydraulic fracturing in coal seams for efficient coalbed methane extraction.

Fracture geometries and drainage radius are essential parameters for developing a reasonable development plan for a single fractured well. However, owing to fracture hits, the complex fracture geometries bring challenges for parameter estimations. This paper establishes a well testing based model for a finite-conductivity fractured vertical well in radial composite reservoirs with dynamic supply and fracture networks. Based on the successive steady-state method, the point source function, pressure superposition principle, and boundary element method are used to solve the reservoir model, and the methods of discrete fracture and pressure superposition are used to solve the fracture model. By introducing the rate-normalized pseudopressure and material balance time, the variable fluid flux is equivalent to the constant fluid flux. The drainage radius value and fracture geometries are solved by fitting the log-log curves of pressure response, and case studies are performed. The results show that the drainage radius increases with the increase of production time and finally tends to a specific value, and it has an excellent exponential relationship with time. Also, the fracture geometries of the typical well are multiple-radial fracture networks. Through the study of dynamic drainage radius, the controlled reserves of single wells in unconventional gas reservoirs can be better determined, and it can also provide a theoretical basis for fracture evaluation, productivity prediction, and enhanced recovery study of the same type of tight gas reservoir.

Mining shallow coal seams and deep coal seams is obviously different. In the deep environment of a coal mine, the stress concentration degree is high, and the dynamic load with impact tendency is prominent. In the practice of underground mining engineering in coal mines, the common coal–concrete combination structure has different degrees of damage and failure under the continuous action of dynamic load at different impact rates, which in turn affects the overall stability of the engineering structure. Therefore, it is of great significance to study the mechanical failure characteristics and energy dissipation law of coal–concrete combinations under the influence of impact rate. In this paper, the split Hopkinson pressure bar (SHPB) dynamic load test system is selected, and coal–concrete combination samples are taken as the impact compression test object. Comparative tests under different impact rates are carried out, and the experimental results of the mechanical parameters of the sample change with the impact rate are obtained. Fractal theory and energy dissipation theory are introduced to study the macroscopic failure characteristics of the combination sample after impact dynamic load and the energy dissipation law during the impact process. The results show that the sample has a strong strain rate correlation, and its dynamic compressive strength, dynamic elastic modulus, and impact rate also have a strong positive correlation. The dynamic stress–strain curve conforms to the four typical stages of the dynamic stress–strain curve. The degree of fragmentation of the sample is proportional to the impact rate. The larger the impact rate, the smaller the proportion of large particle size to small particle size; the fractal dimension of particle size also increases with the impact rate. The transmitted energy, reflected energy, dissipated energy, and incident energy increase with the impact rate, but the increase amplitude is different. The overall energy value is reflected energy $>$ transmission energy $>$ dissipated energy. The energy ratio of different forms fluctuates with the impact rate, the reflected energy ratio is positively correlated with a certain impact rate, the transmission energy ratio is basically symmetric and fluctuates between 30% and 40%, and the dissipated energy ratio increases with the impact rate.

Yttria-stabilized zirconia (YSZ) is the most widely used anode material in solid oxide fuel cells. In this study, ${\mathrm{SiO}}_2$ and ${\mathrm{SnO}}_2$ nanofilms were grown by low-temperature chemical vapor deposition. Using the strong interaction between NiO and ${\mathrm{SiO}}_2/{\mathrm{SnO}}_2$, the NiO particles migrated to the surface of YSZ during calcination at 1,400°C to improve the electrochemical performance. By depositing ${\mathrm{SnO}}_2$ first and then ${\mathrm{SiO}}_2$, the polarization resistance can be reduced by the formation of SnNi alloy, and the power density of $\mathrm{YSZ}@{\mathrm{SnO}}_2@{\mathrm{SiO}}_2$ anode is about 27% higher than that of unmodified Ni/YSZ anode at 650°C. The introduction of ${\mathrm{SiO}}_2$ can significantly improve the stability of the anode under reductive hydrothermal conditions. The strong interaction between ${\mathrm{ZrO}}_2$, ${\mathrm{SiO}}_2$, ${\mathrm{SnO}}_2$, and NiO components is the key to the optimal performance of $\mathrm{YSZ}@{\mathrm{SnO}}_2@{\mathrm{SiO}}_2$.

The popularization and application of methanol in internal combustion engines is of great significance to energy and the environment. With its high methanol substitution rate (MSR) and low fuel consumption, the methanol/diesel dual direct injection scheme is receiving increasing attention. In the current research, a three-dimensional simulation model with high fidelity was established to predict the combustion and emission characteristics of the large bore and medium speed methanol/diesel dual direct injection engine. From the view of the problems of long combustion duration and low combustion efficiency of large bore methanol engines applying a diesel pilot, a study on the injection strategy of diesel was proposed under the condition that the methanol injection strategy was fixed. The results show that shortening the injection interval between pilot diesel and methanol can effectively improve the engine power. Moreover, the appropriate extension of the duration of the pilot diesel injection can ensure the overall ignition and uniform combustion of methanol, resulting in higher power and lower emissions. Meanwhile, with the extension of the duration of the pilot diesel injection, the optimal injection interval also shows an increasing trend. This paper reveals how to utilize effectively the low proportion of pilot diesel energy by adjusting the diesel injection strategy to maximize the MSR and provide theoretical references for the engineering optimization of large bore methanol/diesel dual direct injection marine engines.

Gas production and pressure from shale gas wells are highest early in the extraction process and decay rapidly over time. Skid-mounted compression equipment (SCE) in the shale gas gathering and transportation system (SGTS) can adapt to production changes in gas wells and can be deployed flexibly, improving equipment utilization. This paper examines the effects of node pressure and equipment type on the deployment scheme of SCE. Using a specific shale gas field production block as a case study, this paper proposes a method for multicycle operation and pressurizing deployment optimization in the SGTS, establishing a mixed-integer nonlinear programming (MINLP) model. Solving this optimization model yields the optimal deployment scheme of SCE at different times, the number of compression equipment, and the equipment utilization rate. The results show that in shale gas field production, SCE can be deployed flexibly, and most equipment utilization rates are above 0.8, with a maximum of 0.99. This not only achieves efficient use of equipment but also reduces the purchase cost, fully reflecting the importance of SCE in the SGTS. A sensitivity analysis was conducted to consider the impact of different costs and type combinations of SCE on the total production cost, and the results showed that the total cost of eight types was 6.28% lower than that of two types of booster equipment combinations.

Predicting wind speed is a complex undertaking influenced not only by the wind-speed sequence itself but also by various meteorological factors. This paper introduces a novel multivariate deep-learning neural network prediction model that takes into account not only historical wind-speed data but also a series of meteorological features relevant to wind speed. The meteorological features associated with wind speed are initially extracted using the random forest algorithm (RF). Subsequently, Variational Mode Decomposition and Autocorrelation Function analysis are employed for noise reduction in the wind-speed series. Finally, the wind-speed series are predicted using a Gated Recurrent Unit (GRU) deep-learning neural network, and an Improved Sparrow Search Algorithm is proposed to optimize the four parameters of the GRU. To validate the predictive performance of the model, experimental data from three cities in China, Shenyang, Dalian, and Yingkou, are utilized. The experimental results demonstrate that our proposed model outperforms other models, as evidenced by four key performance indicators.