Rock brittleness index inversion method with constraints of seismic and well logs via a CNN-GRU fusion network based on the spatiotemporal attention mechanism

Rock brittleness is an important parameter for evaluating the fracability of the unconventional oil and gas reservoirs such as tight sandstone and shale oil. Compared with the laboratory measurement, rock brittleness in 3D stratum using the joint inversion of seismic and well logs is more significant for oil exploration and development, but the resolution of the inversion results is low due to the narrowly effective bandwidth of seismic data. Deep neural network (DNN) can establish the complex nonlinear mapping relationship between input and output data to improve the resolution of inversion results, but it has the problem of unstable prediction results in the case of insufficient training samples in the geophysical field. Aiming at the problem, firstly, the ratio of Young ‘s modulus to Poisson ‘s ratio is defined as the rock brittleness index, and an AVO approximation equation with brittleness index is derived, thus establishing the convolution model between the rock brittleness index and seismic data. Secondly, an objective function containing the loss of logging data, seismic data and low-frequency components obtained by the inverse distance weighted interpolation of seismic and well logs is proposed to improve the spatial constraints of seismic and well logs. Then, the spatiotemporal attention mechanism is fused with the convolutional neural network (CNN) and gate recurrent unit (GRU) network to improve the sensitivity of the network proposed to important spatiotemporal characteristics. Finally, a DNN based on the double constraints of convolution model and data is established to solve the inversion stability in the case of small samples. The inversion results of the geological model with sand body superposition and pinchout and the tight sandstone reservoir in Junggar Basin verify that the generalization and prediction accuracy of the network proposed.