No abstract

Deep learning has been used in seismic exploration to solve seismic inversion problems, however it requires sufficient and diverse training samples and labels to obtain satisfactory results. Insufficient training labels are a common problem since labels usually come from well‐logging data, which are limited and sparsely distributed. This can result in a trained network with poor generalizability. A novel complete synthetic dataset‐driven method utilizing convolutional neural network is presented for seismic amplitude‐versus‐offset attributes P and G inversion. Gaussian simulation sampling with physical constraints is used to generate a complete elastic parameter dataset by traversing the entire elastic parameter model space. By randomly combining elastic parameters from the full elastic parameters model space, sufficient synthetic pre‐stack amplitude‐versus‐offset gathers and attribute datasets are generated for training the convolutional neural network. Compared with the limited real data‐driven convolutional neural network, the complete synthetic dataset‐driven convolutional neural network has better generalizability. Broadband training labels improve the accuracy and resolution of the complete synthetic dataset‐driven convolutional neural network's inversion results beyond that of the conventional least‐square data‐fitting method. The complete synthetic dataset‐driven convolutional neural network is robust for processing noise‐contaminated seismic data, but if the frequency band of the labels for training the network is too wide and the signal‐to‐noise ratio of pre‐stack amplitude‐versus‐offset gathers is too low, the quality of the inversion results will reduce. The Marmousi II model and field data examples show that the novel complete synthetic dataset‐driven convolutional neural network can extract higher‐resolution amplitude‐versus‐offset attributes P and G.

The elastic properties of argillaceous sandstones are significantly controlled, however, by the perplexing distribution of dispersed, cemented and matrix (including structural and layered) clay. The corresponding rock physics models are established to investigate the influence of different clay distribution on the elastic properties of sandstone. The rock physics modelling and laboratory experimental results exhibit that the higher the content of the matrix clay, the lower the elastic wave velocity. The dispersed and cemented clay increases the sandstone's velocity by reducing the porosity; the increase of dispersed clay only causes a slight increase in the elastic wave velocity in sandstone. In contrast, a small amount of cemented clay can significantly increase the velocity in sandstone. Based on these understandings, we construct a cross-plot of P-wave velocity and clay content as a template to diagnose clay distribution. Based on the rock physics model and empirical knowledge, we divide the template into four zones, namely, matrix, dispersed, cemented and mixed clay distribution zones. Then, we use the published experimental data and numerical modelling data to validate the template. Based on diagnosing the clay distribution, we introduce how to use the diagnostic results of the template to select a suitable rock physics model for argillaceous sandstone in Shengli Oil Field, East China. The selected rock physics model, guided by diagnosing the clay distribution, predicts P-and S-wave velocity very well. The proposed rock physics template for diagnosing clay distribution also has potential application in well-logging data interpretation, rock physics modelling and reservoir characterization.

No abstract

Experimentally understanding the effects of clay type and fraction on the elastic properties of clay-bearing sandstone is crucial for exploring hydrocarbon reservoirs. Therefore, we artificially synthesized a series of pure clay rocks and clay-bearing sandstones with different clay types and fractions, and conducted the ultrasonic measurements to investigate the elastic behaviors. According to experiments on pure clay rocks, illite rocks have the highest porosity, followed by montmorillonite rocks and chlorite rocks. In terms of elastic properties, illite rocks show the highest ultrasonic P- and S-wave velocities, followed by chlorite rocks and montmorillonite rocks. For clay-bearing sandstones, the measured results indicate that clay types and fractions significantly influence both the physical and elastic properties. Specifically, the porosity of chlorite-bearing sandstone is systematically lower than that of illite-bearing and montmorillonite-bearing sandstone. The chlorite-bearing sandstone has the highest P- and S-wave velocities, followed by illite-bearing and montmorillonite-bearing sandstone. Moreover, the clay fraction also affects porosity–velocity trends. The porosity first falls and then increases as the clay fraction rises, while the P- and S-wave velocities first rise and then fall and, in turn the turning point occurs at the ∼30% clay fraction. The potential mechanism of the turning point is analyzed based on the microscopic distribution of clay and sandstone. Additionally, the clay fraction and distribution also affect the electrical conductivity and permeability of the clay-bearing sandstone, implies that the clay types, clay fraction and clay distribution are non-negligible factors in reservoir evaluation.