Neural network prospect in reservoir characterization
محل انتشار: هفتمین کنگره ملی مهندسی شیمی
سال انتشار: 1390
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 546
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شناسه ملی سند علمی:
ICHEC07_648
تاریخ نمایه سازی: 25 فروردین 1394
چکیده مقاله:
The precision of an artificial neural network (ANN) algorithm is a key issue in the estimation of an oil reservoir properties from the log and seismic data. This paper demonstrates the use of the k-fold cross validation technique to obtain confidence bounds on an ANN's accuracy statistic from a finite sample set. In addition, we also show that an ANN's classification accuracy is dramatically improved by transforming the ANN's input feature space to a dimensionally smaller, new input space. The new input space represents a feature space that maximizes the linear separation between classes. Thus, the ANN's convergence time and accuracy are improved because the ANN must merely find nonlinear perturbations to the starting linear decision boundaries.These techniques for estimating ANN precision bounds and feature space transformations are demonstrated on the problem of estimating the sand thickness in an oil field reservoir based only on remotely sensed seismic data.
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نویسندگان
Mohammad Ali Mohammadi
Corresponding Author’s Address: Department of petroleum engineering ,Omidiyeh Branch ,Islamic Azad University , Omidiyeh ,Iran