we can simulate model or source code of IoT systems to test many of these operations. Existing simulation techniques can either perform model-based or code-based simulation one at a time. In this approach, already developed model cannot be utilized for code-based simulation and vice-versa. Also most of the existing simulation techniques work in central isolated environment.In this paper, we propose a ZeroMQ-based [3] framework for simulating distributed components to mitigate the aforementioned problems. The framework enables a component of a system to communicate with other components of the system to simulate through ZeroMQ-based message broker. The proposed structure is able to perform cooperative simulation among model and code components, which makes it possible to incrementally implement the system. This paper is structured as follows. Section II provides related work and Section III provides the structure of framework to cooperatively simulate model and code. For providing feasibility of our approach, in Section IV, case study is presented and Section V provides conclusion. II. RELATED WORK A. Host-Based TestingUnlike general software which is developed in target machine environment, embedded software is developed in host environment and is usually tested in the same environment [4]. Simulation is usually performed in the host environment that is not a target machine but PC or VP (Virtual Prototype) [5]. IoT system [1] possess the same problems faced by embedded distributed environment. Some parts of the system must be tested in target environment whereas other parts can be tested in the host environment because the host environment is clearly different from the target machine environment. In this paper, the proposed framework performs simulation in host environment in which independent simulation of each components represents unit testing whereas simulation of multiple components represents integration testing. B. Model-Based SimulationIt is not difficult to simulate a model which does not interact with other components. But in IoT environment, most systems have not only several abstract levels but also several distributed applications which interact among themselves [6].These distributed models are defined as sub-models and these sub-models may be parts of one model which impose the object-oriented concept. Model components communicate with each other only through specified message. In this   Abstract-Recently, the scale of distributed computing environment is growing larger. One of the reasons is development of IoT environment which directly impacts the human life by providing instant access to vast amount of information and services correspond to healthcare, smart home service, etc. Interaction testing among distributed systems such as IoT systems is non-trivial task, also cooperative simulation of both model and code components is more difficult one. Model-based and code-based simulation techniques are widely used to test embedded systems. Usually code-based simulation is possible when all the modu...

SPE Members Abstract This paper describes a model that was developed to interpret the pressure transient data from producing wells in the Prudhoe Bay waterflood area. The model provides a quantitative description of reservoir permeability as it changes with radius. The match between field data and the model quantifies the trend of the permeability distribution around the producing wells. Some producing wells in the waterflood areas of the Prudhoe Bay field showed decline in oil and total fluid production rates with the onset of water breakthrough. Pressure buildup tests were run in an attempt to identify the cause of the production decline. Qualitative analysis of the signature of buildup tests was used to identify the most likely reservoir description that would produce similar results. That reservoir description was for a system of gradually decreasing permeability towards the wellbore. After developing, the model it was added to a non-linear regression analysis program and the resulting package was used to determine the reservoir parameters. The excellent match between field and model results, obtained only from that model, helped identify the reason for declining production rates in the waterflood area of the field. The solution presented in this paper can be used to analyze transient tests from reservoirs with damage problems or permeability variations with instance. Introduction Pressure transient testing is one of the most useful reservoir description methods. It provides valuable information about the type of behavior of the reservoir (e.g., homogeneous, fractured, faulted, etc.) and quantitative information about several parameters (e.g., permeability, fracture length, average reservoir pressure, etc.). Insitu measurements of dynamic reservoir parameters are obtained from various types of well tests. The in-situ measurements allow for estimating parameters under reservoir conditions and from large samples. The dynamic parameters are probably the most essential information for reservoir management. The literature is full of examples about how properly designed and interpreted transient tests were used to describe simple and complex reservoir conditions. The Prudhoe Bay waterflood area is the subject of this work. The reservoir is a braided fluvial sandstone formation with minor conglomerates. It contains minor proportion of shales that vary in dimension and extent. The net thickness of the formation is upto 180 feet. The producing interval that is the subject of this investigation is contained in the top 150 ft of the reservoir. It was found that in some wells oil and total fluid production rates decline substantially with the onset of water breakthrough. Figure 1 shows a typical well production history. The well averages about 5000 BOPD until water breakthrough (around beginning of 1987). The oil rate then declines rapidly. Well treatments with acid are partially successful at first. However, subsequent treatments usually give less favorable results. Fracture treatments, at least initially, result in increasing the production rate to pre-water breakthrough levels. Attempts to understand the reasons behind the decline in oil rates and correct the problem involved studying several aspects of the reservoir behavior. P. 25^

: Recently, software reliability and safety issues are seriously considered since failures of embedded systems may cause the damages of human lifes. For verifying and testing embedded software, execution environment including sensors and actuators should be prepared in the actual plants or virtual forms on PC. In this paper, we provide the virtual prototype based code simulation techniques and testing framework on PC. Virtual prototypes are generated by combining the Adobe's Flash SWF images corresponding to the state machine of HW or environment components. Code simulation on PC is possible by replacing the device drivers into virtual drivers which connect to virtual prototypes. Also, testing is performed by controlling the states of virtual prototype and simulators. By using these tools, embedded software can be executed in the earlier development phase and the efficiency and SW quality can be enhanced.

This paper describes a model that was developed to interpret the pressure transient data from producing wells in the Prudhoe Bay waterflood area The model provides a quantitative description of reservoirpermeability as it changes with radius. Thematch between field data and the model quantifies the trend of the permeability distribution around the producing wells.Some producing wells in the waterflood areas of the Prudhoe Bay field showed decline in oil and total fluid production rates with the onset of water breakthrough. Pressure buildup tests were run in an attempt to identify the cause of the production decline. Qualitative analysis of the signature of buildup tests was used to identify the most likely reservoir description that would produce similarresults. That reservoir description was for a system of gradually decreasing permeability towwds the wellbore. After developing the model, it was added to a non-linear regression analysis program and the resulting package was used to determine the reservoir parameters. The excellent match between field and model results, obtained only from that model, helped identify the reason for declining production rates in the waterflood area of the field.The solution presented in this paper can be used to analyze transient tests from reservoirs with damage problems or permeability variations with &stance.References and illustrations at end of paper