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02h0026030 20111123111008.0 cr un||||||||| 111121s2002 xx ||||f|||d||||||||eng | AAI1409747 0493716262(ebk.) : CNY371.35 NGL NGL NGL a355 ; a714 ; a626.2 Oyerokun, Ademola Akinwumi. A new approach for training and testing artificial neural networks for permeability prediction [electronic resource] / Ademola Akinwumi Oyerokun. 2002. 94 p. : digital, PDF file. Source: Masters Abstracts International, Volume: 41-01, page: 0309. ; Chair: Khashayar Aminian. Thesis (M.S.PNGE.)-- West Virginia University, 2002. Includes bibliographical references. Although many attempts have been made in the recent years for permeability prediction using Artificial Neural Network (ANN), none of the approaches has employed pre-specified test set instead of a randomly generated test set.;The methodology for selecting proper pre-specified test set was presented in chapter four of this report. The pre-specified test sets were chosen from a plot of log of permeability versus density. This approach was explicitly discussed later in the report.;In this study, a pre-specified test set approach for training the network for field applicability has been developed using inputs from electric logs and flow unit obtained from geological interpretation of the pay zone. The developed ANN model was successfully applied to the Stringtown Oilfield in West Virginia.;The results of this research demonstrated that the embedded powerful abilities of the ANN could be utilized to predict permeability among other important petrophysical parameters provided it was properly trained with the right pre-specified test set for field applicability. Petroleum engineering. ; Rocks ; Neural networks (Computer science) Permeability. Electronic books. aeBook. aCN bNGL http://proquest.calis.edu.cn/umi/detail_usmark.jsp?searchword=pub_number%3DAAI1409747&singlesearch=no&channelid=%CF%B8%C0%C0&record=1 NGL Bs1066 rCNY371.35 ; h1 bs1108
