Compressional and shear wave velocities (Vp and Vs, respectively) are important elastic parameters to predict reservoir parameters, such as lithology and hydrocarbons. Due to acquisition technologies and economy, the shear wave velocity is generally lacking. Over the last few years, some researchers proposed deep learning algorithms to predict the shear wave velocity using conventional logging data. However, these algorithms focus either on spatial feature extraction for different physical properties of rocks or on sequential feature extraction in the depth direction of rocks. Only focusing on feature extraction in a direction of rocks might lead to a decrease in prediction accuracy. Therefore, we propose a hybrid network of a two-dimensional convolutional neural network and the gated recurrent unit (2DCNN-GRU), which can establish more complex nonlinear relationships between the input and output data based on the spatial features extracted by 2DCNN and the sequential features extracted by GRU. In this study, two cases are used to validate the reliability and prediction accuracy of the proposed network. Comparing the prediction results of 2DCNN, GRU, and the proposed network, the proposed network shows better performance. Meanwhile, for improving the prediction accuracy of the proposed network, the relationship is analyzed between the prediction accuracy of the proposed network and the length of the input sample.

Compression-wave velocity and shear-wave velocity are important elastic parameters describing deeply tight sandstone. Limited by cost and technical reasons, the conventional logging data generally lack shear-wave velocity. In addition, the existing rock physics theory is difficult to accurately establish the rock physics models due to the complex pore structure of tight sandstone reservoir. With the rapid development of the artificial intelligence, the attention mechanism that can increase the sensitivity of the network to important characteristics has been widely used in machine translation, image processing, and other fields, but it is rarely used to predict shear-wave velocity. Based on the correlation between the shear-wave velocity and the conventional logging data in the spatiotemporal direction, a gate recurrent unit (GRU) fusion network based on the spatiotemporal attention mechanism (STAGRU) is proposed. Compared with the convolutional neural network (CNN) and gate recurrent unit (GRU), the network proposed can improve the sensitivity of the network to important spatiotemporal characteristics using the spatiotemporal attention mechanism. It is analyzed that the relationship between the spatiotemporal characteristics of the conventional logging data and the attention weights of the network proposed to verify the rationality of adding the spatiotemporal attention mechanism. Finally, the training and testing results of the STAGRU, CNN, and GRU networks show that the prediction accuracy and generalization of the network proposed are better than those of the other two networks.

Microseismic monitoring is an important technology used to evaluate hydraulic fracturing, and denoising is a crucial processing step. Analyses of the characteristics of acquired three-component microseismic data have indicated that the vertical component has a higher signal-to-noise ratio (SNR) than the two horizontal components. Therefore, we propose a new denoising method for three-component microseismic data using re-constrain variational mode decomposition (VMD). In this method, it is assumed that there is a linear relationship between the modes with the same center frequency among the VMD results of the three-component data. Then, the decomposition result of the vertical component is used as a constraint to the whole denoising effect of the three-component data. On the basis of VMD, we add a constraint condition to form the re-constrain VMD, and deduce the corresponding solution process. According to the synthesis data analysis, the proposed method can not only improve the SNR level of three-component records, it also improves the accuracy of polarization analysis. The proposed method also achieved a satisfactory effect for field data.

Migration-based approaches depending on waveform stacking are generally used to locate the microseismic events in hydro-fracturing monitoring. A simple waveform stacking with polarity correction normally provides better results than any of the absolute value-based methods. However, the existing polarity estimation method based on cross correlation analysis selects only individual waveform as a reference waveform, which may affect the precision of migration-based methods. Therefore, a novel polarity correction method based on cross correlation analysis is introduced for a migration-based location in order to accurately locate the microseismic events in a borehole system. The proposed method selects all waveforms from one event having high signal-to-noise ratio (SNR) as corresponding reference waveforms, instead of only selecting a single high SNR waveform from one target event as the corresponding reference waveform. Compared with the above-mentioned conventional method, this proposed method provides a more accurate migration-based location of microseismic events with minimum error. The presented method was successfully tested on synthetic and field data acquired from a single monitoring well during a hydraulic fracturing process. Our study distinctly demonstrates that the proposed method provides more robust and reliable results, even in low SNR circumstances.

To improve the prediction of thin reservoirs with special geophysical responses, a geostatistical inversion technique is proposed based on an in-depth analysis of the theory of geostatistical inversion. This technique is based on the Markov chain Monte Carlo algorithm, to which we added the contents of facies-constrained. The feasibility of the technique and the reliability of the prediction results are demonstrated by a prediction of the sand bodies in the braided river channel bars in the Xiazijie Oilfield in the Junggar Basin. Based on the MCMC algorithm, the results show that leveraging the lateral changes in the seismic waveforms as geologically relevant information to drive the construction of the variogram and the optimization of the statistical sampling can largely overcome the obstacle that prevents traditional geostatistical inversions from accurately delineating the sedimentary characteristics; thereby, the proposed algorithm truly achieves facies-constrained geostatistical inversion. The case study of the Xiazijie Oilfield showed the feasibility and reliability of this technology. The prediction accuracy of the FCMCMC algorithm-based geostatistical inversion is as high as 6 m for thin interbedded reservoirs, and the coincidence rate between the prediction results and the well log data is more than 85%, which confirms the reliability of the technique. The demonstrated performance of the proposed technique provides a preliminary reference for the prediction of the thin interbedded reservoirs formed in terrestrial sedimentary basins and characterized by small thicknesses and rapid lateral changes with special geophysical responses.