Method for Predicting Transverse Wave Velocity Using a Gated Recurrent Unit Based on Spatiotemporal Attention Mechanism

Transverse wave velocity plays an important role in seismic exploration and reservoir assessment in the oil and gas industry. Due to the lack of transverse wave velocity data from actual production activities, it is necessary to predict transverse wave velocity based on longitudinal wave velocity and other reservoir parameters. This paper proposes a fusion network based on spatiotemporal attention mechanism and gated recurrent unit (STAGRU) due to the significant correlation between the transverse wave velocity and reservoir parameters in the spatiotemporal domain. In the case of tight sandstone reservoirs in the Junggar Basin, the intersection plot technique is used to select four well logging parameters that are sensitive to transverse wave velocity: longitudinal wave velocity, density, natural gamma, and neutron porosity. The autocorrelation technique is employed to analyze the depth-related correlation of well logging curves. The relationship between the spatiotemporal characteristics of these well logging data and the network attention weights is also examined to validate the rationale behind incorporating the spatiotemporal attention mechanism. Finally, the actual measurement data from multiple wells are utilized to analyze the performance of the training set and test set separately. The results indicate that the predictive accuracy and generalization ability of the proposed STAGRU method are superior to the single-parameter fitting method, multiparameter fitting method, Xu-White model method, GRU network, and 2DCNN-GRU hybrid network. This demonstrates the feasibility of the transverse wave velocity prediction method based on the spatiotemporal attention mechanism in the study of rock physics modeling for tight sandstone reservoirs

Shear-Wave Velocity Prediction Method via a Gate Recurrent Unit Fusion Network Based on the Spatiotemporal Attention Mechanism

AbstractCompression-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

Three-Component Microseismic Data Denoising Based on Re-Constrain Variational Mode Decomposition

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

A Novel Polarity Correction Method Developed on Cross Correlation Analysis for Downhole Migration-Based Location of Microseismic Events

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

A Novel Polarity Correction Method Developed on Cross Correlation Analysis for Downhole Migration-Based Location of Microseismic Events

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

Predicting Shear Wave Velocity Using a Convolutional Neural Network and Dual-Constraint Calculation for Anisotropic Parameters Incorporating Compressional and Shear Wave Velocities

As the exploration of unconventional reservoirs progresses, characterizing challenging formations like tight sandstone becomes increasingly complex. Anisotropic parameters play a vital role in accurately characterizing these unconventional reservoirs. In light of this, this paper introduces an approach that uses a dual-constraint anisotropic rock physics model based on compressional and shear wave velocities. The proposed method aims to enhance the precision of anisotropic parameter calculations, thus improving the overall accuracy of reservoir characterization. The paper begins by applying a convolutional neural network (CNN) to predict shear wave velocity, effectively resolving the issue of incomplete shear wave logging data. Subsequently, an anisotropic rock physics model is developed specifically for tight sandstone. A comprehensive analysis is conducted to examine the influence of quartz, clay porosity aspect ratio, and fracture density on compressional and shear wave velocities. Trial calculations using the anisotropic model data demonstrated that the accuracy of calculating anisotropic parameters significantly improved when both compressional and transverse wave velocity constraints were taken into account, as opposed to relying solely on the compressional wave velocity constraint. Furthermore, the rationality of predicting anisotropic parameters using both the shear wave velocity predicted by the convolutional neural network and the measured compressional wave velocity was confirmed using the example of deep tight sandstone in the Junggar Basin