Fracturing is a critical process affecting rock deformation and geofluid flow in underground engineering. However, previous studies coupled 3-D geomechanics and geofluids rarely considered the process of rock fracture in real time due to the lack of experimental apparatus. In this work, we present an auxiliary device for an existing true triaxial geological apparatus, with which we implanted AE modules into loading platens with grooved fluid channels, to better collect fracture-related acoustic emission signals and their locations. We minimize the end-friction effect in terms of the performance of the auxiliary device, loading method, and program of experiments. Our case study used a cubic sandstone specimen to investigate stress-strain behaviors, the evolution of intrinsic permeability, and the spatiotemporal characteristics of acoustic emissions (AE) under true triaxial stress coupled with CO2 gas flow (1 MPa at the inlet and atmosphere at the outlet). We found an obvious correlation between AE characteristics and the evolution of permeability. The AE signals could be applied to identify the fracture mode that triggered the essential change in rock permeability from a decrease to an increase. Furthermore, the AE source locations suggest the polymodal faulting mode of specimen failure, which is consistent with previous studies and our computed tomography (CT) scanning images. The case study also demonstrated the effectiveness and reliability of the device developed, which can play an important role in studies of oil and gas exploration and geological sequestration of CO2.

In this study, experimental investigations were conducted on low-porosity sandstone specimens to reveal their pre-peak cracking characteristics under constant mean stress (sigma(m), 100 MPa) and varying stress Lode angles (theta(sigma), -30 degrees to +30 degrees). The acoustic emission (AE) characteristics 50 s before specimen failure, as well as the strengths and fracturing surface networks, are presented and discussed. The results show that the values of the three principal stresses when the specimen failure occurs change linearly with increasing theta(sigma), in which the maximum principal stress linearly decreases. The AE energy distribution exhibits three ranges, which can be speculated as the different stages of rock cracking propagation. The AE peak frequency is distributed unevenly in seven bands, in which over 90% of the AE signals are located in the 75-95 kHz and 96-130 kHz ranges, which correspond to the tensile and shear cracks, respectively. The RA-AF dynamic evolution curves reveal that the ultimate failure is driven by shear fractures under 3D stress states (for all theta(sigma)). The pattern of macrocrack networks shows the "double-X'' through-going mode, and clear tensile and shear areas were directly observed on the fracturing surfaces. These findings are deemed essential for determining the failure mechanism of deep engineering rock masses.

Tight sandstone gas (TSG) reservoirs are developed in large buried depths and complex geo-stress field environments. During exploitation, the surrounding rock of a drill well is often damaged or can collapse under artificial engineering disturbances. Understanding the failure mechanism of tight sandstone under high and complex three-dimensional (3-D) stress states is essential for the safe and efficient exploitation of TSG. In this study, using the stress Lode angle (θσ) as a variable, failure experiments of low porosity sandstone specimens under various 3-D stress paths are performed, and the mechanical responses (e.g., stress–strain behavior, strength, fracture pattern, and acoustic emission characteristics) are analyzed. The results show that as θσ increases, the strength of the specimen as well as the deviatoric stress required its failure decrease linearly, whereas its brittleness increases. The failure of the specimens is primarily due to numerous micro tensile cracks and a few macro shear cracks. θσ significantly affects the cracking mode during failure. Acoustic emission (AE) parameters show that the failure process can be categorized into three stages within the time-to-failure window, among which Stage 2 (acceleration stage) can be regarded as the precursor stage of the ultimate failure of the specimen. The descriptive statistical results of AE energy show that uniaxial stress, hydrostatic stress, and true triaxial stress compression impose different effects on the damage mode of the specimens. Under the high 3-D stresses, the multiple fracture surfaces formed inside the specimen are intertwined to present several "X"-shaped fracture pairs. These findings facilitate the understanding of the failure mechanism of rock mass surrounding the wellbore, and of significance in stability designing in well trajectory of TSG reservoir actual development.

Complex civil structures require the cooperation of many building materials. However, it is difficult to accurately monitor and evaluate the inner damage states of various material systems. Based on a convolutional neural network (CNN) and the acoustic emission (AE) time-frequency diagram, we used the transfer learning method for classifying the AE signals of different materials under external loads. The results show the CNN model can accurately classify cracks that come from different materials based on AE signals. The recognition accuracy can reach 90% just by retraining the full connection layer of the pretrained model, and its accuracy can reach 97% after retraining the top 2 convolutional layers of this model. A realization of cracking source identification mainly depends on the differences in mineral particles in materials. This work highlights the great potential for real-time and quantitative monitoring of the health status of composite civil structures.