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- W4386074967 abstract "In this study, the 3D seismic cube recorded in the Fort Worth Basin in the United States is inverted using an artificial neural network in order to estimate the pore pressure prior to drilling utilizing seismic genetic inversion. The stacked 3D seismic amplitudes near three wells are used as an input for the multilayer perceptron neural network, which is trained in a supervised mode. The output is the computed acoustic impedances obtained from the density and sonic logs recorded in these wells. The weights of connections between neurons are optimized during training after which the entire seismic cube is transmitted through the neural network. The acoustic impedance's cube is the machine's output. Using the sonic well-log data of a vertical well, a linear relationship between depth and velocity is produced; the Eaton model will use this relationship as a vertical trend. The pore pressure is derived from the Eaton model using acoustic impedances. The suggested method is utilized to determine the pore pressure in the lower Barnett Shale; the results can be used for hydraulic fracture planning and simulation as well as wellbore stability." @default.
- W4386074967 created "2023-08-23" @default.
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- W4386074967 date "2023-07-31" @default.
- W4386074967 modified "2023-09-25" @default.
- W4386074967 title "Predrill Pore Pressure Estimation in Shale Gas Reservoirs Using Seismic Genetic Inversion with an Example from the Barnett Shale" @default.
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- W4386074967 doi "https://doi.org/10.1002/9781119389385.ch1" @default.
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