Artificial Intelligence & Data Mining Applications in the by Shahab D. Mohaghegh (Ed.), Saud M. Al-Fattah (Ed.), Andrei

By Shahab D. Mohaghegh (Ed.), Saud M. Al-Fattah (Ed.), Andrei S. Popa (Ed.)

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This approach relies on preprocessing test data to obtain a smooth derivative plot. The data are filtered with a preselected smooth parameter incorporated in approximating spline functions. Then, the smoothed derivative plot is transformed into symbols by first approximating the curve with linear and nonlinear segments based on preselected error thresholds and measurements of inflection points. The resulting segments then are used to obtain a detailed symbolic description of the derivative plot.

Such performance is not guaranteed when dealing with very noisy field data. This result can be explained by the systematic (nonrandom) nature of noise in field data. Systematic noise produces false patterns (signatures) that confuse the net during recognition. We elaborate on this subject in the Recommendations for Future Work section. Fig. 5-lnput of a derivative plot Into the artificial neural net. a homogeneous infinite-acting reservoir with wellbore storage and skin. 698), which indicates a response from a well affected by wellbore storage and skin in a homogeneous reservoir with a closed or constantpressure outer boundary.

6. Use the backpropagation procedure described in Appendix A to calculate the amount of weight change required in each link be- 240 cause of the current pattern. Do not apply any changes to the links yet. 7. Repeat Steps 3 through 6 until the net has been presented with all the patterns in the training set. During this process, accumulate the weight changes in each link caused by the different patterns. When the net is given all the examples in the training set, use the cumulative weight change for each link to change the weight of that link.

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