NDLI: Total Organic Carbon Prediction in Shale Gas Reservoirs from Well Logs Data Using the Multilayer Perceptron Neural Network with Levenberg Marquardt Training Algorithm: Application to Barnett Shale

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Total Organic Carbon Prediction in Shale Gas Reservoirs from Well Logs Data Using the Multilayer Perceptron Neural Network with Levenberg Marquardt Training Algorithm: Application to Barnett Shale

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Total Organic Carbon Prediction in Shale Gas Reservoirs from Well Logs Data Using the Multilayer Perceptron Neural Network with Levenberg Marquardt Training Algorithm: Application to Barnett Shale

Content Provider	Springer Nature Link
Author	Ouadfeul, Sid Ali Aliouane, Leila
Copyright Year	2015
Abstract	The main goal of this paper was to predict the total organic carbon (TOC) from well logs data using the multilayer perceptron (MLP) neural network machine. This can replace the Schmoker’s method in case of discontinuous measurement of the bulk density log. The MLP machine is composed of three layers, an input layer with four neurons corresponding to the Gamma ray, the neutrons porosity, and the slowness of the P and S waves well logs. The output layer is formed with one neuron, which corresponds to the predicted TOC log, and a hidden layer with ten neurons. The MLP machine is trained using the Levenberg–Marquardt algorithm. Data of two horizontal wells drilled in the lower Barnett formation located in USA are used. Comparison between the predicted and calculated TOC log using the Schmoker’s method clearly shows the use of the neural network method to predict the TOC in shale gas reservoirs.
Starting Page	3345
Ending Page	3349
Page Count	5
File Format	PDF
ISSN	13198025
Journal	Arabian Journal for Science and Engineering
Volume Number	40
Issue Number	11
e-ISSN	21914281
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-05-10
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	TOC Prediction Neural network MLP Levenberg Marquardt Engineering Science
Content Type	Text
Resource Type	Article

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