Acoustic impedance inversion in coal strata using the priori constraint-based TCN-BiGRU method

Using 3D seismic exploration to detect ground fissure

The elastic anisotropy of coal formations is important for the prediction of unconventional coalbed methane reservoir performance and hydraulic fracturing. Although essential for several geophysical applications, the elastic anisotropy of coals is still poorly understood. Therefore, three sets of cylindrical coal samples are drilled parallel and perpendicular to the bedding plane, and the ultrasonic velocities under different confining pressures are measured in the laboratory. We combine experimental observations and modeling analysis to evaluate the elastic anisotropy of the coals. The results suggest that the velocity of coals parallel to the bedding plane is less sensitive to confining pressure than those perpendicular to the bedding plane. The fracture densities of coals parallel to the bedding plane are much lower than those perpendicular to the bedding plane, and most of the fractures are closed when the pressure is higher than 15 MPa. The organic matter (OM), clay content, and their preferred orientation play dominant roles in velocity anisotropy, and the fracture effect can significantly enhance it. The velocity anisotropy parameters of coals are positively correlated with the OM and clay content, and the preferred orientation of OM and clay plays a significant role in coal anisotropy. The results in this study are helpful in the characterization of the fractures of coals and can provide an important rock-physics basis for microseismic fracture monitoring and reservoir prediction.

Owing to the increasing challenges associated with coal mine exploration and development, extremely precise surveys with high-resolution images are required to support production. Conventional inversion methods cannot provide sufficiently precise images of the complex lithologies in coal measure strata. Accordingly, this study performed research in Qiyuan mining area, Shanxi Province, China, and predicted the complex lithology on the basis of facies control using waveform indication inversion and waveform indication simulation. Horizontal changes in seismic waveforms were used to reflect lithologic assemblage characteristics for facies-controlled constraints, and the vertical mapping the connection between seismic waveform and logging curves was shown. Moreover, high-resolution inversions of wave impedance and natural gamma parameters were conducted. Combined with lithologic shielding and accurate time–depth conversion, the inversions enabled the precise characterization of the lithological assemblage distribution in the study area. Our results showed that waveform indication inversion could distinguish between coal seams and limestone, whereas waveform indication simulation based on natural gamma could effectively distinguish between sandstone and mudstone. Furthermore, the horizontal resolution was improved and the vertical resolution extended to a thickness of 2–3 m. In addition, the inversion results were highly consistent with drilling results, with an error <0.1 m. Therefore, waveform indication inversion and simulation could be applied to coal mines for safe and efficient production.

The lithology of underground formations can be determined using logging data, which is important for a variety of subsurface geoscience and industrial applications. Deep learning technology offers the advantage of discovering a potential relationship between input and output variables, making it a great choice for generating fast and cost-effective lithology classification models. To automatically characterize lithologies, a multiclass image segmentation problem is considered and an improved Unet as a solution is adopted. The model’s input data is two-dimensional images composed of rock feature data at different depths, and the outcome is a result of one-dimensional rock lithology classification. The algorithm’s practicality was tested using the logging data set from the Xinjing mining area in Shanxi Province, in north-central China, and an open-source data set of Canadian strata. Our model is tested against the 1D-convolutional neural network (CNN) and XGBoost algorithms using a good logging data set of the same depth and different depths for testing. The results show that the improved Unet method outperforms the 1D-CNN and XGBoost algorithms in the classification of rock lithologies. This algorithm has high application potential in the automatic interpretation of rock lithologies.