摘要：This study involves the applications of different numerical techniques in a more general way to the design of a simulator for an enhanced oil recovery process with surfactant assisted water flooding. The data from a hypothetical oil well and a Nigerian oil well were used for the validation of the simulator. The process is represented by a system of nonlinear partial differential equations: the continuity equation for the transport of the components and Darcy’s equation for the phase flow. The orthogonal collocation, finite difference and coherence theory techniques were used in solving the equations that characterized the multidimensional, multiphase and multicomponent flow problem. Matlab computer programs were used for the numerical solution of the model equ- ations. The predicted simulator, obtained from the resulting numerical exercise confers uncondi- tional stability and more insight into the physical reservoir description. The results of the ortho- gonal collocation solution were compared with those of finite difference and coherence solutions. The results indicate that the concentration of surfactants for orthogonal collocation show more features than the predictions of the coherence solution and the finite difference, offering more opportunities for further understanding of the physical nature of the complex problem. We have found out that the partition of the three components between the two-phases determines other physical property data and hence the oil recovery. The oil recovery for the Nigerian oil reservoir is higher than the recovery predicted for the hypothetical crude. The displacement mechanism for the multicomponent and multiphase system is stable in the Nigerian oil reservoir due to the mod- erate value of the oil/water viscosity instead of the hypothetical reservoir with high value of oil/water ratio.

摘要：Kazakh Research Institute of Water as a leading research organization on water problems works on scientific reasoning of water problems solution in the republic. The institute activity is a multifaceted and an integral part of its activity is, particularly, a system research, analysis and evaluation of developed projects of hydro economic rebuilding in the river basins. One of such significant documents of the last time is the project of regulation of the Syr-Darya River and the north-end of the Aral Sea. The experts of the institute took part in the state expertise of this project and estimated its significance for ecological revival of the region.

摘要：Neural networks based on high-dimensional random feature generation have become popular under the notions extreme learning machine (ELM) and reservoir computing (RC). We provide an in-depth analysis of such networks with respect to feature selection, model complexity, and regularization. Starting from an ELM, we show how recurrent connections increase the effective complexity leading to reservoir networks. On the contrary, intrinsic plasticity (IP), a biologically inspired, unsupervised learning rule, acts as a task-specific feature regularizer, which tunes the effective model complexity. Combing both mechanisms in the framework of static reservoir computing, we achieve an excellent balance of feature complexity and regularization, which provides an impressive robustness to other model selection parameters like network size, initialization ranges, or the regularization parameter of the output learning. We demonstrate the advantages on several synthetic data as well as on benchmark tasks from the UCI repository providing practical insights how to use high-dimensional random networks for data processing.

摘要：在径流统计分析的基础上,确定三峡水库汛期分期,并分别研究了两种防洪补偿调度方式下的各分期汛限水位。初步分析结果认为,在满足原设计防洪安全要求的前提下,对荆江河段防洪补偿的调度方式,则三峡水库汛期汛前期汛限水位155 m,主汛期汛限水位维持145 m不变,汛末期水库运行水位为155 m;对兼顾城陵矶地区防洪补偿的调度方式,则汛前期汛限水位151 m,主汛期汛限水位维持145 m不变,汛末期汛限水位为147 m。这样的分期控制汛限水位不仅可以满足防洪要求,而且可以提高洪水资源的利用率。