Abstract

Nowadays, more than 5,000 rod pumping wells are running in the Huabei Oilfield and take a great account of the production wells. To increase the management efficiency of these stripper wells as well as lowering the management's cost, we have installed the wisdom-oilfield components that uses innovative technologies such as wireless networks, well sensors to collect and send the operating data to the central point to handle. But there are still problems to the current management modes of our rod pumping wells in two aspects. One is for pumping wells installed with the wisdom-oilfield components, in the daily operation and maintenance at the well-site, people can't get access to the central server and query the status or make the data analysis, leading to the pumping wells' maintenance difficult and the efficiency low. The other is for non-network coverage pumping wells in remote area that we can't make the digital management and control. Where the rod pumping system's running can only rely on experienced artificially adjustments, which lacks reasonable and theoretical basis. This paper aims at presenting our recent attempts adhere to these two challenges. On the basis of Android platform, we developed a dynamometer card measurement and analysis system comprising the mobile query and analysis system as well as a well-site data acquisition and analysis system. They unveiled the design of the intelligent security systems capable of solving problems in the well-site. At the moment, with the two systems, we not only made the mobile phone a terminal that can connect the server for processing results of the central working site in security, but also made the mobile phone a data collecting and analysis terminal at the well-site. The two systems had took a wide range of well-site application tests in the first exploit factory of the Huabei oilfield company, which dramatically lowered the labors' intensity, improved their working efficiency and reduced the rod pumping wells' management costs by up to 30%. The calculated production results showed the average relative error is only 5.1%, less than 10% of industry's standard, which meets the requirements of industrial applications.

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