Hydraulic fracturing is an essential technology for the development of unconventional resources such as tight gas. The evaluation of the fracture performance and productivity is important for the design of fracturing operations. However, the traditional dimensionless fracture conductivity is too simple to be applied in real fracturing operations. In this work, we proposed a new model of dimensionless fracture conductivity (FCD), which considers the irregular fracture geometry, proppant position and concentration. It was based on the numerical study of the multistage hydraulic fracturing and production in a tight gas horizontal well of the North German Basin. A self-developed full 3D hydraulic fracturing model, FLAC3Dplus, was combined with a sensitive/reliability analysis and robust design optimization tool optiSLang and reservoir simulator TMVOCMP to achieve an automatic history matching as well as simulation of the gas production. With this tool chain, the four fracturing stages were history matched. The simulation results show that all four fractures have different geometry and proppant distribution, which is mainly due to different stress states and injection schedule. The position and concentration of the proppant play important roles for the later production, which is not considered in the traditional dimensionless fracture conductivity FCD. In comparison, the newly proposed formulation of FCD could predict the productivity more accurately and is better for the posttreatment evaluation.
The prediction of active earth pressure was generally implemented under the assumptions of two-dimensional conditions and cohesionless soils. However, in practice, the soils usually display a considerable level of cohesion, and the collapse of retained slopes exhibits a three-dimensional (3D) nature. Considering this fact, this paper intends to predict the 3D active earth pressure in cohesive soils based on the kinematic limit-analysis method and a 3D rotational collapse mechanism. The influence of cracks is considered, including a crack forming before the failure of retained soil masses (open crack) and a crack forming simultaneously with the failure (formation crack). The active earth pressure coefficient is estimated based on the work-energy balance principle. In order to facilitate direct application, several design charts are provided. It is shown that accounting for soil cohesion and 3D effects results in a notable decrease in the active earth pressure, whereas considering the existence of cracks would increase the pressure value. This paper develops the studies on active earth pressure, which considers the presence of cohesion, cracks, and 3D effects together for the first time. The results of this paper can offer references in designs of retaining structures for cohesive slopes.
Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicability and advantages of recurrent neural networks(RNNs)on PWP prediction,three variants of RNNs,i.e.,standard RNN,long short-term memory(LSTM)and gated recurrent unit(GRU)are adopted and compared with a traditional static artificial neural network(ANN),i.e.,multi-layer perceptron(MLP).Measurements of rainfall and PWP of representative piezometers from a fully instrumented natural slope in Hong Kong are used to establish the prediction models.The coefficient of determination(R^2)and root mean square error(RMSE)are used for model evaluations.The influence of input time series length on the model performance is investigated.The results reveal that MLP can provide acceptable performance but is not robust.The uncertainty bounds of RMSE of the MLP model range from 0.24 kPa to 1.12 k Pa for the selected two piezometers.The standard RNN can perform better but the robustness is slightly affected when there are significant time lags between PWP changes and rainfall.The GRU and LSTM models can provide more precise and robust predictions than the standard RNN.The effects of the hidden layer structure and the dropout technique are investigated.The single-layer GRU is accurate enough for PWP prediction,whereas a double-layer GRU brings extra time cost with little accuracy improvement.The dropout technique is essential to overfitting prevention and improvement of accuracy. 相似文献
Natural Hazards - Frequent occurrences of drought stress caused by dry weather create severe destroy in apple yield and quality in North China. Although appropriate drought stress is beneficial to... 相似文献
