intelligent algorithm

Permeability is one of the most important petrophysical properties of hydrocarbon reservoirs. Estimating permeability is a challenge that petroleum engineers face, particularly in carbonate reservoirs, especially karst reservoirs, where data on core samples may be lacking. In this study, empirical relationships, regression analysis, artificial neural networks, and the nearest-neighbor algorithm, were employed to estimate permeability in depth intervals where core data were unavailable. The results obtained from these methods were compared with each other and with core measurements. Electrical facies provide intelligent models with the capability to estimate permeability with more details using conventional petrophysical logs. Furthermore, considering that electrical facies analysis is conducted for all wells in the field, the use of optimized intelligent models allows for the estimation of permeability in all wells, leading to more accurate results. Based on the results, artificial neural networks and the nearest-neighbor algorithm performed better compared to the other methods, with correlation coefficients of 2% and 5% higher, respectively, than the other approaches. To optimize the obtained results, permeability estimation using these two methods was incorporated into the framework of electrical facies modeling. Subsequently, the results of facies analysis were compared with the results of layered modeling. Ultimatly, among the two methods used, the nearest-neighbor algorithm, on average, provides a more suitable permeability estimation for the Fahliyan formation with a correlation coefficient of 66% compared to the artificial neural network method with a correlation coefficient of 57%. The proposed method in this study can be applied in heterogeneous carbonate reservoirs with well-defined heterogeneity in porosity distribution.

Multi-carrier energy systems have systematic flexibility for energy and load management. The integration of different types of energy under the concept of energy hub in multiple energy infrastructures such as electricity, natural gas, and heat in an integrated way results in the supply of load demand at a lower cost. The energy hub creates a great opportunity for energy system operators to achieve a system with higher efficiency and better performance. The profitability of energy hubs in the presence of various uncertainties, which are intensified by the presence of different types of energy carriers, is one of the issues in this field. The optimal performance of the power system depends on the optimal performance of each energy hub component. One of the most important components is combined heat and power (CHP) generation. This paper deals with the optimal operation of an energy hub in an industrial complex based on collected field data, including electrical loads and heating and cooling demands, considering electricity market prices and renewable energies such as wind and photovoltaic, as well as intelligent algorithms for responsive loads. According to the simulation results, comparing the total cost of energy in the presence and absence of responsive loads, the presence of a responsive load next to the energy hub significantly reduces the annual economic cost.

The output power of solar arrays is dependent on temperature and light intensity. Solar arrays have low efficiency. To increase the efficiency of photovoltaic systems, the solar array maximum power point tracking is required. The exotic phenomena like clouds, on the solar arrays will provide partial shade in these conditions and voltage curve can be several local maximum points. Conventional methods because they tend to track the maximum power point of the local convergence are not appropriate in partial shade condition. So in this paper, a new method of the maximum power point tracking with the ability to track the global maximum power point is provided. The proposed method is implemented by the microcontroller STM32F407VGT6 and DC/DC buck converter. The circuits are presented to measure temperature and irradiation. Also a comparison between the proposed method and the method of P&O to verify the performance of the proposed method is done. The experimental and simulation results confirm the accuracy of the proposed method.