Using Artificial Neural Networks in Reservoir Characterization: Characterization of Dual Porosity Gas Reservoirs with Faults Using Artificial Neural Networks

 
9783639512199: Using Artificial Neural Networks in Reservoir Characterization: Characterization of Dual Porosity Gas Reservoirs with Faults Using Artificial Neural Networks

The framework of this study is to convert observed measurements of reservoir data into characteristic information of the reservoir. Artificial neural network (ANN) technology is utilized in mapping/interpolating the non-linear complex relationship between observed measurements and reservoir characteristics. The proposed ANN methodology is applied towards analysing the pressure transient measurements collected from isotropic and anisotropic faulted dual-porosity gas reservoirs, as an inverse solution to formation characteristics, such as the permeability and porosity of the fracture and matrix systems, distance to the fault, orientation of the fault with respect to the principal flow directions, and sealing capacity of the fault are predicted using the reservoir fluid, rock, and bottom-hole pressure as the principal inputs. The main focus of this study is to develop a suitable network to obtain accurate prediction about desired reservoir characteristics of dual-porosity tight gas systems with a fault, and demonstrate the efficient processing power of ANN on this class of reservoir problems.

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Obtaining his BS in PE from Kazakh-British Technical U. Zhazar has started his career as Junior Field Eng. in Baker Atlas Kazakhstan. After joining BG Group as a Well engineer he continued his education with MS in PE at Penn. State U. where he conducted research on Characterization of Naturally Fractured Reservoir Using Artificial Neural Networks.

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Toktabolat, Zhazar
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Book Description Book Condition: New. Publisher/Verlag: Scholar's Press | Characterization of Dual Porosity Gas Reservoirs with Faults Using Artificial Neural Networks | The framework of this study is to convert observed measurements of reservoir data into characteristic information of the reservoir. Artificial neural network (ANN) technology is utilized in mapping/interpolating the non-linear complex relationship between observed measurements and reservoir characteristics. The proposed ANN methodology is applied towards analysing the pressure transient measurements collected from isotropic and anisotropic faulted dual-porosity gas reservoirs, as an inverse solution to formation characteristics, such as the permeability and porosity of the fracture and matrix systems, distance to the fault, orientation of the fault with respect to the principal flow directions, and sealing capacity of the fault are predicted using the reservoir fluid, rock, and bottom-hole pressure as the principal inputs. The main focus of this study is to develop a suitable network to obtain accurate prediction about desired reservoir characteristics of dual-porosity tight gas systems with a fault, and demonstrate the efficient processing power of ANN on this class of reservoir problems. | Format: Paperback | Language/Sprache: english | 144 pp. Bookseller Inventory # K9783639512199

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Zhazar Toktabolat
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Book Description SPS Apr 2013, 2013. Taschenbuch. Book Condition: Neu. Neuware - The framework of this study is to convert observed measurements of reservoir data into characteristic information of the reservoir. Artificial neural network (ANN) technology is utilized in mapping/interpolating the non-linear complex relationship between observed measurements and reservoir characteristics. The proposed ANN methodology is applied towards analysing the pressure transient measurements collected from isotropic and anisotropic faulted dual-porosity gas reservoirs, as an inverse solution to formation characteristics, such as the permeability and porosity of the fracture and matrix systems, distance to the fault, orientation of the fault with respect to the principal flow directions, and sealing capacity of the fault are predicted using the reservoir fluid, rock, and bottom-hole pressure as the principal inputs. The main focus of this study is to develop a suitable network to obtain accurate prediction about desired reservoir characteristics of dual-porosity tight gas systems with a fault, and demonstrate the efficient processing power of ANN on this class of reservoir problems. 144 pp. Englisch. Bookseller Inventory # 9783639512199

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Book Description SPS Apr 2013, 2013. Taschenbuch. Book Condition: Neu. Neuware - The framework of this study is to convert observed measurements of reservoir data into characteristic information of the reservoir. Artificial neural network (ANN) technology is utilized in mapping/interpolating the non-linear complex relationship between observed measurements and reservoir characteristics. The proposed ANN methodology is applied towards analysing the pressure transient measurements collected from isotropic and anisotropic faulted dual-porosity gas reservoirs, as an inverse solution to formation characteristics, such as the permeability and porosity of the fracture and matrix systems, distance to the fault, orientation of the fault with respect to the principal flow directions, and sealing capacity of the fault are predicted using the reservoir fluid, rock, and bottom-hole pressure as the principal inputs. The main focus of this study is to develop a suitable network to obtain accurate prediction about desired reservoir characteristics of dual-porosity tight gas systems with a fault, and demonstrate the efficient processing power of ANN on this class of reservoir problems. 144 pp. Englisch. Bookseller Inventory # 9783639512199

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Book Description SPS Apr 2013, 2013. Taschenbuch. Book Condition: Neu. This item is printed on demand - Print on Demand Neuware - The framework of this study is to convert observed measurements of reservoir data into characteristic information of the reservoir. Artificial neural network (ANN) technology is utilized in mapping/interpolating the non-linear complex relationship between observed measurements and reservoir characteristics. The proposed ANN methodology is applied towards analysing the pressure transient measurements collected from isotropic and anisotropic faulted dual-porosity gas reservoirs, as an inverse solution to formation characteristics, such as the permeability and porosity of the fracture and matrix systems, distance to the fault, orientation of the fault with respect to the principal flow directions, and sealing capacity of the fault are predicted using the reservoir fluid, rock, and bottom-hole pressure as the principal inputs. The main focus of this study is to develop a suitable network to obtain accurate prediction about desired reservoir characteristics of dual-porosity tight gas systems with a fault, and demonstrate the efficient processing power of ANN on this class of reservoir problems. 144 pp. Englisch. Bookseller Inventory # 9783639512199

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Book Description SPS, 2013. Paperback. Book Condition: New. Language: English . Brand New Book. The framework of this study is to convert observed measurements of reservoir data into characteristic information of the reservoir. Artificial neural network (ANN) technology is utilized in mapping/interpolating the non-linear complex relationship between observed measurements and reservoir characteristics. The proposed ANN methodology is applied towards analysing the pressure transient measurements collected from isotropic and anisotropic faulted dual-porosity gas reservoirs, as an inverse solution to formation characteristics, such as the permeability and porosity of the fracture and matrix systems, distance to the fault, orientation of the fault with respect to the principal flow directions, and sealing capacity of the fault are predicted using the reservoir fluid, rock, and bottom-hole pressure as the principal inputs. The main focus of this study is to develop a suitable network to obtain accurate prediction about desired reservoir characteristics of dual-porosity tight gas systems with a fault, and demonstrate the efficient processing power of ANN on this class of reservoir problems. Bookseller Inventory # KNV9783639512199

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