Seismic Inversion Based on Fusion Neural Network for the Joint Estimation of Acoustic Impedance and Porosity

Seismic inversion and petrophysical inversion are the most common methods used in exploration geophysics to obtain elastic and petrophysical parameters, which are essential for reservoir characterization. However, they are commonly ill-posed problems and both are usually performed independently. Recently, deep learning has been successfully applied to the solution of inverse problems (e.g., seismic inversion and petrophysical inversion) by using large amounts of labeled training data to establish a mapping relationship between the input and the target. On the one hand, the performance of deep learning-based inversion depends heavily on diversity of the training dataset. However, the number of wells in actual production is limited, which greatly limits the application of deep learning-based inversion methods. On the other hand, deep learning-based inversion methods usually calculate elastic and petrophysical parameters independently, which lacks clear physical meaning and leads to large computational errors. To overcome these problems, by considering the spatial variability of elastic and petrophysical parameters from well-log data, a large amount of realistic pseudo-well-log and post-stack seismic data are first generated based on geostatistics to obtain the diversity of data required for network training. Meanwhile, we propose a fusion neural network architecture to build a physically meaningful network to simultaneously estimate acoustic impedance and porosity within the reservoir conditioned to post-stack seismic and limited well data. The fusion neural network consists of two subnetworks based on the spatio-temporal neural network. The method is validated by the application of real data and compared to traditional inversion approaches.