A novel competitive learning neural network based acoustic transmission system for oil-well monitoring

The optimal operation of an oil well requires the periodic measurement of temperature and pressure at the downhole. In this paper, acoustic waves are used to transmit data to the surface through the pipeline column of the well, making up a wireless transmission system. Binary data is transmitted in two frequencies, using frequency-shift keying modulation. Such transmission faces problems with noise, attenuation, and, at pipeline joints, multiple reflections and nonlinear distortion. Hence, conventional demodulation techniques do not work well in this case. The neural network presented here classifies signals received by the receiver to estimate transmitted data, using a linear-vector-quantization-based network, with the help of a preprocessing procedure that transforms time-domain incoming signals in three-dimensional images. The results have been successfully verified. The neural network estimation principles presented in this paper can be easily applied to other patterns and time-domain recognition applications.