Rate Transient Analysis via Artificial Intelligence
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Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
This item is printed on demand - Print on Demand Titel. Neuware - The purpose of this research is to develop a mathematical model as a reservoir estimation tool for naturally fractured reservoirs with dual lateral well configurations. The tool proposed in this study includes a forward artificial neural network (ANN) with the ability to predict production data via known reservoir and well design parameters. The proposed tool also includes an inverse ANN component that can be used to predict the permeability and porosity of matrix and fracture, as well as fracture spacing and reservoir thickness. By means of the proposed tool, the user would be able to analyze instantaneously predicted reservoir or production data with less cost and time. The software involved in developing the tool were MATLAB, EXCEL, and a commercial modeling software. The procedures are introduced and discussed in the following chapters including training data generation, selecting training data sets, training forward and inverse ANN models. Moreover, a graphical user interface (GUI) is developed and assembled for each ANN, which allows the user to view results in numerical and graphical formats. 104 pp. Englisch.

Von Händler/Antiquariat, The Book Depository EURO [60485773], London, United Kingdom.
Language: English Brand New Book ***** Print on Demand *****.The purpose of this research is to develop a mathematical model as a reservoir estimation tool for naturally fractured reservoirs with dual lateral well configurations. The tool proposed in this study includes a forward artificial neural network (ANN) with the ability to predict production data via known reservoir and well design parameters. The proposed tool also includes an inverse ANN component that can be used to predict the permeability and porosity of matrix and fracture, as well as fracture spacing and reservoir thickness. By means of the proposed tool, the user would be able to analyze instantaneously predicted reservoir or production data with less cost and time. The software involved in developing the tool were MATLAB, EXCEL, and a commercial modeling software. The procedures are introduced and discussed in the following chapters including training data generation, selecting training data sets, training forward and inverse ANN models. Moreover, a graphical user interface (GUI) is developed and assembled for each ANN, which allows the user to view results in numerical and graphical formats.

Lieferung aus: Deutschland, zzgl. Versandkosten.
The purpose of this research is to develop a mathematical model as a reservoir estimation tool for naturally fractured reservoirs with dual lateral well configurations. The tool proposed in this study includes a forward artificial neural network (ANN) with the ability to predict production data via known reservoir and well design parameters. The proposed tool also includes an inverse ANN component that can be used to predict the permeability and porosity of matrix and fracture, as well as fracture spacing and reservoir thickness. By means of the proposed tool, the user would be able to analyze instantaneously predicted reservoir or production data with less cost and time. The software involved in developing the tool were MATLAB, EXCEL, and a commercial modeling software. The procedures are introduced and discussed in the following chapters including training data generation, selecting training data sets, training forward and inverse ANN models. Moreover, a graphical user interface (GUI) is developed and assembled for each ANN, which allows the user to view results in numerical and graphical formats.

Lieferung aus: Niederlande, Lieferzeit: 5 Tage, zzgl. Versandkosten.
The purpose of this research is to develop a mathematical model as a reservoir estimation tool for naturally fractured reservoirs with dual lateral well configurations. The tool proposed in this study includes a forward artificial neural network (ANN) with the ability to predict production data via known reservoir and well design parameters. The proposed tool also includes an inverse ANN component that can be used to predict the permeability and porosity of matrix and fracture, as well as fracture spacing and reservoir thickness. By means of the proposed tool, the user would be able to analyze instantaneously predicted reservoir or production data with less cost and time. The software involved in developing the tool were MATLAB, EXCEL, and a commercial modeling software. The procedures are introduced and discussed in the following chapters including training data generation, selecting training data sets, training forward and inverse ANN models. Moreover, a graphical user interface (GUI) is developed and assembled for each ANN, which allows the user to view results in numerical and graphical formats.

Lieferung aus: Deutschland, Versandkostenfrei.
Rate Transient Analysis via Artificial Intelligence: The purpose of this research is to develop a mathematical model as a reservoir estimation tool for naturally fractured reservoirs with dual lateral well configurations. The tool proposed in this study includes a forward artificial neural network (ANN) with the ability to predict production data via known reservoir and well design parameters. The proposed tool also includes an inverse ANN component that can be used to predict the permeability and porosity of matrix and fracture, as well as fracture spacing and reservoir thickness. By means of the proposed tool, the user would be able to analyze instantaneously predicted reservoir or production data with less cost and time. The software involved in developing the tool were MATLAB, EXCEL, and a commercial modeling software. The procedures are introduced and discussed in the following chapters including training data generation, selecting training data sets, training forward and inverse ANN models. Moreover, a graphical user interface (GUI) is developed and assembled for each ANN, which allows the user to view results in numerical and graphical formats. Englisch, Taschenbuch.

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Lu Jia, Paperback, English-language edition, Pub by OmniScriptum GmbH & Co. KG.