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- Author: Zhazar Toktabolat
- Publisher: Scholars' Press
- ISBN-10: 3639512197
- ISBN-13: 9783639512199
- Format: 15.2 x 22.9 x 0.9 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Using Artificial Neural Networks in Reservoir Characterization (e-book) (used book) | bookbook.eu
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Description
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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- Author: Zhazar Toktabolat
- Publisher: Scholars' Press
- ISBN-10: 3639512197
- ISBN-13: 9783639512199
- Format: 15.2 x 22.9 x 0.9 cm, softcover
- Language: English English
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.
Reviews