The traditional geological method relies too much on the resolution of seismic reflection and the quality of well data for the determination of geological profiles. In view of the fact that the number of well data that can be used for calibration in the early stage of development is very small, and the traditional geological modeling method generates geological profiles. The efficiency is low and it is difficult to support model establishment and frequent updating. A geological profile generation method based on improved Pix2Pix network was proposed. Firstly, the initial three-dimensional data was sliced. Based on the comprehensive analysis of deep learning network, a Pix2Pix network model based on residual and multi-scale discriminator was constructed. The residual mechanism was introduced in the generator part to improve the learning ability of the network to geological features, and a multi-scale discriminator was set for the model to enhance the discriminant performance of the network. The real seismic reflection data and geological profile data of the oilfield were used to train the model. The experimental results show that the performance of the network model is significantly improved after the introduction of residual mechanism and multi-scale discriminator. The SSIM (structural similarity) score of the generated results and the real geological profile can reach 91.89 %, and the geological features in the generated results are highly fitted with the actual situation.

In order to explore the problem of fracturing fluid filtration in the process of open hole fracturing, based on the classical filtration model, combined with the two-dimensional model of hydraulic fracturing, ABAQUS finite element software was used for simulation calculation. It is found that in the initial stage of fracture propagation, the pore pressure increases rapidly and increased linearly, and the filtration loss of fracturing fluid also increase rapidly, which is the initial stage of fracture propagation. With the continuous injection of fracturing fluid, the increasing trend of pore pressure becomes slower, and the filtration loss of fracturing fluid also increases slowly. As the crack width gradually widens, it is the crack propagation stage. In the later stage of fracture propagation, when the fracture length reaches a certain length, the change of pore pressure is gradually stable, and the filtration loss of fracturing fluid is slightly reduced compared with the pressure holding state and the fracture propagation stage. In the whole process, the calculation of filtration loss takes into account the dynamic expansion of cracks, which is of certain significance for the actual engineering filtration situation.

To address the issue of composite scale in dense sandstone formations, an oil-in-acid microemulsion unblocking agent comprising acid, white oil, composite emulsifiers, and additives was developed. The internal structure and morphology of the microemulsion unblocking agent were characterized using transmission electron microscopy (TEM) and dynamic light scattering (DLS). Its performance was assessed in terms of thermal stability, dissolution efficiency for inorganic and organic scale plugs, and impact on reservoir permeability. The mechanism of unblocking was investigated using X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS). The microemulsion unblocking agent exhibited a spherical dispersion in solution with an average particle size of 6 nm, and maintained stability at 90 ℃ for over 48 h. The dissolution rates for scale formed at different locations were 87.61% and 77.41%. The microemulsion unblocking agent the ability to transition from oil wettingto water wetting, and proved more efficient and faster in resolving complex scale plugging compared to traditional acid solvents. The microemulsion unblocking agent enhanced core permeability recovery rates to over 80%. Results highlight the advantages of the microemulsion unblocking agent, including excellent thermal stability, high scale dissolution capacity, and effective removal of both organic and inorganic composite scales, They can efficiently eliminate obstructions in the wellbore and near-wellbore regions, thereby clearing the wellbore and enhancing the flow paths in the formation and increasing oil and gas production capacity.

Complex equipment such as wind turbine blades faces both performance degradation and random shocks. There are two interdependent relationships between these two failure modes: the interdependence between internal factors in the degradation process and the interdependence between degradation and shock processes. These characteristics pose challenges to reliability analysis. To solve this problem, a new mutually dependent competing failure processes (MDCFPs) model was proposed based on two MDCFPs models. This new model integrated two interdependencies. Taking the wind turbine blade stiffness degradation model based on Gamma process and the extreme shock model based on homogeneous Poisson process as examples, the accuracy and differences of three models were analyzed using the control variate method, and the influence of key parameters was studied. The results show that, under the same conditions, the new model's reliability is closest to the observed empirical reliability, with an absolute error of no more than 0.12. At the same time, the new model's reliability is lower than that of the two base models, with maximum absolute errors of 0.26 and 0.40, respectively. After adjusting the parameters of the new model, the absolute errors in reliability compared to the base models are limited to 0.03 and 0.02. These findings suggest that the new model effectively accounts for interdependencies among factors, prevents overestimation of reliability, and can replace base models, demonstrating broader applicability.

Bearing local fault occurs in the turboprop engine reduction gearbox, which affects engine running safely. Due to the complex structure and high speed of the reducer gearbox, the vibration signal used to monitor the operation state of the mechanical components is complicated. In order to detect the bearing local fault signal in time and extract fault feature accurately from the vibration signal, a method which was based on vibration signal combining FFT, fast kurtogram and envelope spectrum was proposed. Firstly, during the engine operation, the FFT(fast Fourier transform) spectrum was used to detect whether there was bearing fault component in the vibration signal; and then the fast kurtogram was applied to determine the frequency band distribution of the fault component; finally, the bearing fault characteristic frequency was acquired through envelope spectrum analysis. In the course of certain type of turboprop engine ground bench test, this method was used to accurately detect and diagnose the local spalling fault of the inner raceway. Therefore, this method can provide a basis for the engineering application on local fault detection and diagnosis of bearings in aero-engine reduction gearbox.

The slewing and luffing coupling motion of tower cranes can easily induce structural vibrations in the crane mast and swing angles of the payload, potentially leading to operational faults. To investigate these vibration patterns, an elastic crane model was developed under combined slewing and luffing dynamic motions, incorporating Lagrange dynamics, air resistance, and beam deflection. The model was analyzed across phases from acceleration to constant speed and then to deceleration. The effectiveness of this nonlinear coupled motion model was validated using a designed experimental platform. The study examined the effects of varying accelerations and initial swing angles. The results indicate that luffing acceleration influences structural vibration, while slewing acceleration has a significant impact on it. Additionally, initial angles greater than or equal to 0.2 rad greatly affect structural vibration. When the slewing acceleration exceeds 0.04 m/s², the frequency of mast vibration increases. Understanding the structural vibration law during coupled motion is crucial for enhancing the design of dynamic systems.

Considering the complex loading environment of the floating offshore wind turbine(FOWT), especially the fatigue damage risks brought by continuous and periodic wave actions, the fatigue performance of FOWT under wave coupling excitation was studied and a long-term fatigue damage assessment method for FOWT was proposed. Taking the Spar FOWT as an example, a nonlinear model under the wave coupling excitation of 8-DOF (dgree of freedom) was established based on the Lagrange equation, and the accuracy of the model was verified. Subsequently, on the basis of the established nonlinear model, the fatigue performance of the example FOWT was discussed according to the proposed method. The results indicate that the fatigue damage of FOWT is closely related to the wave load characteristics, and different damage performance is exhibited under different working conditions. Due to the randomness of sea conditions, only short-term fatigue estimation of the wind turbine is not enough to accurately understand its fatigue performance, and long-term fatigue analysis is needed. Moreover, the peak of short-term fatigue damage at the root of the wind turbine tower occurs near the tower's natural vibration period, while the peak of long-term fatigue damage occurs within the range of high probability sea state periods in the sea. Therefore, efforts should be made to avoid the natural vibration period of the FOWT coinciding with the peak period to prevent high-level damage accumulation and reduce fatigue damage. The analysis also demonstrates the effectiveness of the proposed Monte Carlo based long-term fatigue calculation method for FOWT, which not only has high accuracy but also less time consumption. The proposed improvement method can reduce output fluctuations, enhance stability, and provide more precise results.

Hydrogen-injected into natural gas for pipeline transportation is an effective way to solve the high cost of hydrogen transportation. The rapid and uniform mixing of hydrogen and natural gas is an unavoidable problem to ensure the safety of hydrogen-blended natural gas pipeline transportation. Because the mixed gas flows back to the mixing pipe through the reflux pipe, it can continue to mix with the hydrogen-blended natural gas after entering, and improve the mixing uniformity. Therefore, a reflux mixer was used to mix hydrogen and natural gas. The effects of the inclination angle, diameter ratio and number of reflux pipes on the mixing properties of reflux mixers were analyzed by CFD. The results show that the reflux mixer has low pressure loss, high mixing uniformity and high reflux rate. When the inclination angle of reflux pipe, the diameter ratio of reflux pipe and the number of reflux pipe are 10°, 0.5 and 3, the reflux mixer achieves the best effect, and its pressure loss, mixing uniformity and the reflux rate are 76 Pa, 99.7% and 44.5%, respectively. The research results can provide reference for the parameters design and mixing performance of hydrogen-blended natural gas mixer.

In order to solve the problems of wind power fluctuations and intermittency during grid integration, which affect the stable operation of the power grid, a capacity optimization configuration scheme for a flywheel-lithium battery hybrid energy storage system was proposed. This scheme combined empirical mode decomposition (EMD) and variational mode decomposition (VMD). Firstly, typical daily data was obtained using the K-means algorithm, and EMD was applied to decompose the output power signal of these typical wind power daily data into grid-connected power that meets fluctuation limits and power that needs to be smoothed by the hybrid energy storage system. Then, the sparrow search algorithm was used to optimize the number of decomposition modes K and the quadratic penalty factor α in the VMD algorithm. By decomposing the power that needs smoothing using VMD, a reasonable allocation between lithium batteries and flywheel energy storage was achieved. Finally, considering the constraints of energy storage charging and discharging power and state of charge, an economic model was constructed with energy storage cost as the objective function. The actual power generation data of Qiejidunqu wind farm in Gonghe County, Hainan Prefecture, Qinghai Province were simulated and calculated by MATLAB platform. The results show that the proposed strategy not only effectively mitigates wind power fluctuations but also improves the overall economy of the system.

It is of great significance for accurate forecasting of multi-load to be carried out to improve the consumption of new energy, realize energy saving and emission reduction, and ensure the safe and reliable operation of the power grid. To enhance the accuracy of simultaneous multi-load forecasting,a model which singular spectrum analysis and bi-directional long short-term memory networks SSA-BiLSTM (singular spectrum analysis-bidirectional long short-term memory) was proposed. First, A approach Pearson correlation coefficients for coupled feature extraction was proposed to identify correlations and dependencies within multivariate load data. Then, SSA was employed for feature extraction to capture dynamic characteristics and reduced forecasting complexity. Finally, a multi-ask learning framework was introduced to leverage shared information among multiple forecasting tasks, improving prediction accuracy. Experimental using datasets from multi-area electricity, heat, cold multivariate loads, flexible and wind-solar power generation, the effectiveness of the model. The results show that the proposed model average improves in mean absolute percentage error (MAPE) for the prediction of electrical, heating, and cooling loads in multiple regions is 0.41%, with an average root mean square error (RMSE) increase of 0.02 MW.