Neural Networks are a powerful tool -For computation when the data available is less than adequate.Can solve fundamental problems such as formation permeability prediction from the well log response with high accuracy.Have great potential for computing results from historical data which would otherwise be irrelevant for analysis. Neural Networks find various applications in the Petroleum industry - Optimize the hydraulic fracture design, Permeability Predictions, Facies classification etc. This study deals with the development of a neural network for predicting well log response. This network is trained by input-output pairs of known well log data, using which an estimation model is created. This estimation model can then compute permeability output for input well log data of different offset wells. The neural network created uses a feed forward model with a Levenberg-Marquardt learning algorithm. Error is calculated using Mean Squared Error (MSE) technique. An optimum number of neurons in input, output and hidden layers were set. The network was trained with given pair of input response of well log dataset and its output permeability response was predicted. The study also considers two different cases to mark the importance of optimized training of a neural network before it can be used for predicting permeability values in offset wells. This technique is completely data driven and does not require priori assumptions regarding functional forms for correlating permeability and well logs (Sharma et al. 2011). The network developed in this study gives highly accurate results. Also it is very flexible as the training algorithm, number of layers and other parameters can be changed according to the data set available. It proves to be cost effective and saves time since it does not require core analysis. Neural Networks prove to be a useful emerging alternate tool to conventional methods. Introduction Reservoir characterization is a very important domain of petroleum engineering. It includes description of the spatial distribution of rock properties such a porosity, permeability and saturation, in high detail. This can be achieved from a variety of sources: well logs, cores, seismic, production tests, drill stem tests. Porosity is defined as the fluid storage capacity of porous media. It mainly depends on two factors: grain size distribution and sorting index. Porosity is measured using three techniques namely, Well logs (Sonic, Neutron-Density and NMR), Seismic and Core analysis. Permeability is the measure of the ease with which the rock will permit the passage of fluids. Porosity alone is not sufficient to make a reservoir producible. For the flow of hydrocarbons, the reservoir must have well interconnected pores. Knowledge of permeability is significant for developing an effective reservoir description and quality. Permeability determination is an active research area in petroleum industry as there is no direct formula for calculation of permeability from logs. The conventional methods for permeability calculation are core analysis and drill stem tests. However, these methods have some limitations as they are very expensive and all wells in a field may not have core data (Abdideh 2012). In heterogeneous reservoirs classical methods face problems in determining accurately the relevant petrophysical parameters. Applications of artificial intelligence have recently made this challenge a possible practice (Hamada et al. 2010). Since Permeability and Porosity are typically distributed in a spatially non uniform and non-linear manner (Verma et al. 2012), we propose the use of Artificial Neural Networks for improved porosity and permeability estimation.

Plants play a vital role in maintaining the ecological cycle, and thus, to maintain the plant's proper growth and health, adequate monitoring is required. Hence, the aim of the chapter is to create a smart plant monitoring system using automation and internet of things (IOT) technology. This topic highlights various features such as smart decision making based on soil moisture real-time data. For this purpose, sensors like soil moisture sensor, DHT11 sensor, level sensor, etc. are used. The soil moisture sensor measures the level of moisture (i.e., water content of different plants). The signal will be sent to Arduino board when the moisture level drops below the marginal value, which triggers the pumping of water into the plant by the pump. When the moisture level reaches absolute value, the pump is halted. The other condition for this process is level sensor. Level sensor senses the water level in the tank and sends the information of water level value to Arduino board and Arduino board to cloud. The whole data about the plant monitoring will be sent to the cloud server.

Internet has revolutionized the technological era, which has a significant impact on us by making communication much better not only with the living beings but also with non-living things through the medium of internet of things (IoT). Thus, this topic highlights how internet of things can minimize user intervention in controlling home appliances and monitoring its setting. Integrating IoT with cloud computing and web service helps us in providing feasibility in accessing home appliances (i.e., monitoring appliances and measuring home condition). The whole process of integration aims to create an intelligent system. Thus, smart home is one of the application of IoT aimed at improving comfort, safety, and wellbeing within our homes.

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