The existing methods of well-logging interpretation cannot be applied accurately to the exploration and evaluation of carbonate reservoirs because of the fracture development. Based on the fracture density obtained by core analysis in a carbonate reservoir located in the Ordos basin, the west of China, three types of fracture density (low, medium and high fracture density) of the target formation were identified. We investigated the effect of fractures on acoustic logging signals in time and frequency domain by Hilbert-Huang transform (HHT) and extracted 11 features in time domain and 9 features in frequency domain. Then, we reduced the features in time and frequency domain to three principal components by principal component analysis (PCA). Finally, a new prediction model of GA-SVM method based on Hilbert-Huang transform of acoustic logging data was reported to predict the fracture density. The results show that fracture density has a greater effect on the attenuation of IMF2 and IMF3 components for three different types of formation by empirical mode decomposition (EMD) analysis. The energies of Stoneley wave and shear wave have higher sensitivity than compressional wave. Compared with time domain, the distribution in high frequency domain has a greater correlation with fracture density by Hilbert spectrum and marginal spectrum. The correlation coefficients between fracture density and 9 features in frequency domain (R²=0.5~0.7) are better than the coefficients with 11 features in time domain (R²=0.3~0.5). Core analysis and interpretation of resistivity image logging support the validity and effectiveness of our model. The prediction accuracy using the features in frequency domain can reach to 82~90%, which is much higher than using the features in time domain with accuracy of 52~59%. The application with more information of acoustic logging data in our model can avoid the error in velocity picking, which points out the direction for the future prediction.

Using data from nodal geophones and broadband seismometers, this study investigates the seismicity near Red Deer, Alberta, a region with increasing cases of hydraulic fracturing (HF)‐induced earthquakes. A cluster of 417 events was detected, and their spatial distribution and focal mechanisms reveal a NE trending rupture area with two strike‐slip fault planes. Reactivation of preexisting faults by pore pressure diffusion is likely responsible for the occurrence of the earthquake sequence following the M_L 4.18 mainshock. The temporal sequence of reactivated fault orientations suggests apparent changes in the local stress field following the mainshock, which is also responsible for a remotely triggered cluster observed 1 month after the mainshock. This secondary triggering process enhances our understanding of the trailing effect of HF‐induced seismicity.

Although hydraulic fracturing‐induced earthquakes have been widely reported in Alberta, Canada, only one seismic cluster (the Cordel Field) has thus far been linked to wastewater disposal (WD). In this study, we report a statistically significant spatiotemporal correlation between recent earthquakes and nearby WD wells near Musreau Lake—the second disposal‐induced earthquake swarm in Alberta. This newly occurred swarm contains five events with local magnitudes $ML>3$ from January 2018 to March 2020, forming into three tightly spaced clusters. The refined locations and focal mechanisms suggest a ∼10 km long northwest–southeast‐trending rupture along the northern Rocky Mountains that developed over time, during which both poroelastic effects and static stress transfer played key roles. Through a statistical analysis of all reported induced earthquake clusters in the western Canada sedimentary basin (WCSB), we propose a linear predictive relationship (i.e., the “Interpolated Strike Orientation” model) between fault rupture direction and fault distance to the Rocky Mountains. This observation‐based model, which is supported by both the focal mechanisms of the natural earthquakes and the nearby northwest‐striking geological faults, is a new and useful reference for future assessments of seismic hazard in the WCSB.

Fractures greatly increase the difficulty of oil and gas exploration and development in reservoirs consisting of interlayered carbonates and shales and increase the uncertainty of highly efficient development. The presence of fractures or layered media is also widely known to affect the elastic properties of rocks. The combined effects of fractures and layered media are still unknown. We have investigated the effects of fracture structure on wave propagation in interlayered carbonate and shale rocks using physical models based on wave theory and the similarity principle. We have designed and built two sets of layered physical models with randomly embedded predesigned vertically aligned fractures according to the control variate principle. We have measured the P- and S-wave velocities and attenuation and analyzed the effects of fracture porosity and aspect ratio (AR) on velocity, attenuation, and power spectral dimension of the P- and S-waves. The experimental results indicated that under conditions of low porosity ($<1.5\%$), Han’s empirical velocity-porosity relations and Wang’s attenuation-porosity relation combined with Wyllie’s time-average model are a good prediction for layered physical models with randomly embedded fractures. When the porosity is constant, the effect of different ARs on elastic wave properties can be described by a power law function. We have calculated the power spectrum fractal dimension $D$ of the transmitted signal in the frequency domain, which can supplement the S-wave splitting method for estimating the degree of anisotropy. The simple power law relation between the power spectrum fractal dimension of the P-waveform and fracture density suggests the possible use of P-waves for discriminating fracture density. The high precision and low error of this processing method give new ideas for rock anisotropy evaluation and fracture density prediction when only P-wave data are available.