Application of Artificial Neural Network to Optical Fluid Analyzer.

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  • Kimura Makoto
    Schlumberger K.K., 2–1 Fuchinobe 2–chome, Sagamihara–shi, Kanagawa 229
  • Nishida Katsuhiko
    Schlumberger K.K., 2–1 Fuchinobe 2–chome, Sagamihara–shi, Kanagawa 229

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A three-layer artificial neural network has been applied to the presentation of optical fluid analyzer (OFA) raw data, and the accuracy of oil fraction determination has been significantly improved compared to previous approaches. To apply the artificial neural network approach to solving a problem, the first step is training to determine the appropriate weight set for calculating the target values. This involves using a series of data sets (each comprising a set of input values and an associated set of output values that the artificial neural network is required to determine) to tune artificial neural network weighting parameters so that the output of the neural network to the given set of input values is as close as possible to the required output. The physical model used to generate the series of learning data sets was the effective flow stream model, developed for OFA data presentation. The effectiveness of the training was verified by reprocessing the same input data as were used to determine the weighting parameters and then by comparing the results of the artificial neural network to the expected output values. The standard deviation of the expected and obtained values was approximately 10% (two sigma).

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