Research And Implementation Of Virtual Training System Of Oil Drilling Operation Based On Shannon-Schramm Model

Oilfield production accidents would not only cause severe economic losses to the country and the enterprise, but also endanger oil workers. The standardized training towards drilling in oilfield is a necessary means to reduce accidents. The oilfield enterprises mainly adopt traditional methods to train oil workers at present. The training methods include teaching themselves, taking intensive classes and on-site observation or guidance. But there are varying disadvantages such as boring, affecting production order and great risky. They cannot guarantee effect of training, and even set up security danger to work in practice.The Shannon-Schramm model and the actual propagation process are analyzed in this paper, which points out the defect of Shannon-Schramm model and complement it. Out of improved Shannon-Schramm model comes the trend of wave type advancing and spiraling up on the extension in destination experience field.Based on improved Shannon-Schramm model and related educational studies, this paper devises a distance training model for drilling workers in oilfield. In this training model, the learning module includes four sub modules which are document reviewing, operation demonstration standardizing, interactive learning and simulation training. These four sub modules together constitute a training process that leaps abstract experience to that of observation and to that of operation, and which is a progressive learning course. At the same time, the four sub modules provide four entries to choose to learn, so that learners at the individual level can choose different entries to start training course. The examination testing module is used to measure training effects and the testing results are fed back to learners in time, so next training plan can be made.At last, this paper achieves a drilling workers-oriented oilfield drilling training system, by multimedia technology and virtual reality technology. The system has well training results when it enters service.