Design And Realization Of The Oil Pump Remote Monitoring And Management System Based On The Automatic Diagnosis Of The Dynagraph

Now, our country had treated the energy issues as a more and more important problem. The energy issue has already effected the social stability and the economic development. Especially in recent years, there is a large increment on the dependence on foreign oil. As a result, how to lower production costs, improve labor efficiency and reduce security risks had became to the task we must face to in the following period. Using computer technology to build digital oilfield is an important method to realize the oilfield automatic management and security production. It is also an effective way to improve the production efficiency. The system is the project between KingEagle technology cooperation and a company of DaQing oilfield, and its main objective is to achieve long-range surveillance and unmanned production, and realize the the digital management of oil field.Mechanical oil pumping is the most important method of crude oil production in the world, and sucker rod oil pumping system is the most popular method of mechanical oil pumping. The equipments of sucker rod oil pumping system are vital important in the whole production of crude oil. But as is known to all, the oil pumps word on the field, and it is hard to maintain them and to know their work status, and the losing of the oil pumps' malfunction is very big. Therefore, this paper researches on the remote monitoring and fault diagnosis system of sucker rod oil well, and show the design schema and there realization information. Main achievements of this paper are showed in the following paragraph.The paper first show the design schema of the oil pump remote monitoring and management system. Based on the actual oilfield business model, we have designed the system as a three-tier management platform. They are the control center management platform, sub-control center management platform and control terminal management platform. All the Platforms are both independent management system and an organic body composition. The system can automatic monitor the united-station and the pumping equipment, which can solve the safety and maintenance problems, and greatly improve the production efficiency. In this system, through detecting and analyzing the data we had got, we can get the knowledge of the pumps' state, which give the making of the decision a reliable proof a favorable reference.To make the system more user-friendly, and increase the automation level of the oil field, we also designed rich diagram display method, report forms, curve, alarm methods, GIS and other function modules. In those modules, we had used voice synthesis, geographic information systems and other advanced technologies. During the developing period, we use RUP as our development process, and use many design patterns to improve our design. For the reason of using these advanced object-oriented experience, the oil pump remote monitoring and management system now is powerful, flexible, and user recognized.There is no doubt that in the oil pump remote monitoring and management system, the fault diagnosis technology of the oil pump is one of the most crucial technologies. This paper complete show the fault diagnosis schema of the oil pump, including the diagram's pretreatment, feature extraction and standardized, classifier design and other issues. During the diagram's pretreatment period, we use mathematical morphology's erosion, dilation, thinning and other algorithm. During the diagram's feature extraction period, this paper introduce and the design four method for the learning and training of the classifier. They are the heart shape round distance, the heart shape rectangle distance, the standard distance and the difference distance.During the classifier designing period, this paper use simulated annealing algorithm to optimize the training of the BP neural network. This is because the BP neural network has its own disadvantage. As we all know, during the training or the neural network, the parameter of the learning rate is very important and difficult to select. If learning rate is too large, when the neural network is close to the globe optimal solution, the neural network may surge around this solution. If learning rate is too small, the neural network may converge to the globe optimal solution too slowly, and which is more important that the neural network may stay at the local optimal solutions, and be unable to come over them. All the case above will make the training of the training of neural networks unsuccessful.For the reason above, this paper uses simulated annealing algorithm to optimize the training of BP neural network, dynamic deciding the neural network's learning rate. At the beginning of the training of the BP network, we select a larger learning rate to study, and if the network's error is reducing, the learning rate remains the original value, and if the error changes the worse, the paper use simulated annealing algorithm to reduce the BP neural network's learning rate and use the new learning rate to continue the training of the network. The results show that BP neural network optimized by simulated annealing algorithm can better avoid the oscillation of the network and convergence to the local optimal solutions, and also the training of the network using the mixed algorithm is faster, and the result has a better extensive ability.The practical application of the oil pump remote monitoring and management system based on the automatic fault diagnosis shows a better effect. There are still some following works to do. They are the automatic fault diagnosis to other types of oil pumping equipment, such as screw pump, the electric submersible pump.