Search Results (283)

Microseismic monitoring has become a critical technology for hydraulic fracturing in unconventional oil and gas reservoirs, owing to its high temporal and spatial resolution. It plays a pivotal role in tracking fracture propagation and evaluating stimulation effectiveness. However, the automatic picking of first-arrival times and accurate source localization remain challenging under complex noise conditions, which constrain the reliability of fracture parameter inversion and reservoir assessment. To address these limitations, we propose a hybrid approach that combines optimized variational mode decomposition (OVMD), wavelet thresholding denoising (WTD), and an adaptive fractal box-counting dimension algorithm for enhanced first-arrival picking and source localization. Specifically, OVMD is first employed to adaptively decompose seismic signals and isolate noise-dominated components. Subsequently, WTD is applied in the multi-scale frequency domain to suppress residual noise. An adaptive fractal dimension strategy is then utilized to detect change points and accurately determine the first-arrival time. These results are used as inputs to a particle swarm optimization (PSO) algorithm for source localization. Both numerical simulations and laboratory experiments demonstrate that the proposed method exhibits high robustness and localization accuracy under severe noise conditions. It significantly outperforms conventional approaches such as short-time Fourier transform (STFT) and continuous wavelet transform (CWT). The proposed framework offers reliable technical support for dynamic fracture monitoring, detailed reservoir characterization, and risk mitigation in the development of unconventional reservoirs. Full article

In recent years, the accurate evaluation of artificial fracture networks has become a key challenge in enhancing the effectiveness of reservoir stimulation in shale gas development. This paper systematically reviews the research progress on evaluation methods for artificial fracture networks in shale gas horizontal wells, covering two major technical systems: direct monitoring and dynamic inversion. Direct monitoring methods focus on technologies such as microseismic monitoring, tracers, wide-field electromagnetic methods, and distributed fiber optics. Dynamic inversion methods utilize data from fracturing construction curves, shut-in water hammer effects, and flowback production, and combine numerical simulations with artificial intelligence algorithms to infer fracture network parameters, although the issue of non-uniqueness in solutions remains to be addressed. Research shows that no single technology can comprehensively characterize fracture network features. Future directions should involve the integration of multi-source data (geophysical, chemical, fiber-optic, and dynamic production data) to construct intelligent evaluation frameworks, validated by field experiments and dynamic data simulations. The introduction of artificial intelligence and big data technologies provides new ideas for fracture network parameter inversion, but their effectiveness still requires support from more case studies. This paper provides theoretical guidance and practical reference for the optimization and integration of fracture network evaluation technologies in efficient shale gas development. Full article

The tight conglomerate oil reservoir in Xinjiang’s Mahu area is situated on the northwestern margin of the Junggar Basin. The reservoir comprises five stacked fan bodies, with the Triassic Baikouquan Formation serving as the primary pay zone. To delineate the study scope and conduct a field validation, the Ma-X well block was selected for investigation. Through triaxial compression tests and large-scale true triaxial hydraulic fracturing simulations, we analyzed the failure mechanisms of tight conglomerates and identified key factors governing hydraulic fracture propagation. The experimental results reveal several important points. (1) Gravel characteristics control failure modes: Larger gravel size and higher content increase inter-gravel stress concentration, promoting gravel crushing under confining pressure. At low-to-medium confining pressures, shear failure primarily occurs within the matrix, forming bypassing fractures around gravel particles. (2) Horizontal stress differential dominates fracture geometry: Fractures preferentially propagate as transverse fractures perpendicular to the wellbore, with stress anisotropy being the primary control factor. (3) Injection rate dictates fracture complexity: Weakly cemented interfaces in conglomerates lead to distinct fracture morphologies—low rates favor interface activation, while high rates enhance penetration through gravels. (4) Stimulation strategy impacts SRV: Multi-cluster perforations show limited effectiveness in enhancing fracture network complexity. In contrast, variable-rate fracturing significantly increases stimulated reservoir volume (SRV) compared to constant-rate methods, as evidenced by microseismic data demonstrating improved interface connectivity and broader fracture coverage. Full article

Most public seismic recordings, sampled at hundreds of Hz, tend to be unlabelled, i.e., not catalogued, mainly because of the sheer volume of samples and the amount of time needed by experts to confidently label detected events. This is especially challenging for very low signal-to-noise ratio microseismic events that characterise landslides during rock and soil mass displacement. Whilst numerous supervised machine learning models have been proposed to classify landslide events, they rely on a large amount of labelled datasets. Therefore, there is an urgent need to develop tools to effectively automate the data-labelling process from a small set of labelled samples. In this paper, we propose a semi-supervised method for labelling of signals recorded by seismometers that can reduce the time and expertise needed to create fully annotated datasets. The proposed Siamese network approach learns best class-exemplar anchors, leveraging learned similarity between these anchor embeddings and unlabelled signals. Classification is performed via soft-labelling and thresholding instead of hard class boundaries. Furthermore, network output explainability is used to explain misclassifications and we demonstrate the effect of anchors on performance, via ablation studies. The proposed approach classifies four landslide classes, namely earthquakes, micro-quakes, rockfall and anthropogenic noise, demonstrating good agreement with manually detected events while requiring few training data to be effective, hence reducing the time needed for labelling and updating models. Full article

To achieve more precise and effective microseismic magnitude estimation, a classification model based on transfer learning with an improved deep residual network is proposed for predicting microseismic magnitudes. Initially, microseismic waveform images are preprocessed through cropping and blurring before being used as inputs to the model. Subsequently, the microseismic waveform image dataset is divided into training, testing, and validation sets. By leveraging the pretrained ResNet18 model weights from ImageNet, a transfer learning strategy is implemented, involving the retraining of all layers from scratch. Following this, the CBAM is introduced for model optimization, resulting in a new network model. Finally, this model is utilized in seismic magnitude classification research to enable microseismic magnitude prediction. The model is validated and compared with other commonly used neural network models. The experiment uses microseismic waveform data and images of magnitudes 0–3 from the Stanford Earthquake Dataset (STEAD) as training samples. The results indicate that the model achieves an accuracy of 87% within an error range of ±0.2 and 94.7% within an error range of ±0.3. This model demonstrates enhanced stability and reliability, effectively addressing the issue of missing data labels. It validates that using ResNet transfer learning combined with an attention mechanism yields higher accuracy in microseismic magnitude prediction, as well as confirming the effectiveness of the CBAM. Full article

Analyzing the spatiotemporal characteristics of microseismic monitoring data is crucial for the monitoring and early prediction of coal–rock dynamic disasters during deep mining. Aiming to address the challenges hampering the early prediction of coal–rock dynamic disasters in deep mining, in this paper, we propose a method for analyzing the spatiotemporal characteristics of microseismic events in deep mining based on the ST-DBSCAN algorithm. First, a spatiotemporal distance metric model integrating temporal and spatial distances was constructed to accurately describe the correlations between microseismic events in spatiotemporal dimensions. Second, along with the spatiotemporal distribution characteristics of microseismic data, we determined the spatiotemporal neighborhood parameters suitable for deep-mining environments. Finally, we conducted clustering analysis of 14 sets of actual microseismic monitoring data from the Xinjulong Coal Mine. The results demonstrate the precise identification of two characteristic clusters, namely middle-layer mining disturbances and deep-seated activities, along with isolated high-magnitude events posing significant risks. Full article

Addressing the stability control challenges of roadways with composite roofs in the No. 34 coal seam of Donghai Mine under high-strength mining conditions, this study employed integrated methodologies including laboratory experiments, numerical modeling, and field trials. It investigated the mechanical response characteristics of the composite roof and developed a synergistic control system, validated through industrial application. Key findings indicate significant differences in mechanical behavior and failure mechanisms between individual rock specimens and composite rock masses. A theoretical “elastic-plastic-fractured” zoning model for the composite roof was established based on the theory of surrounding rock deterioration, elucidating the mechanical mechanism where the cohesive strength of hard rock governs the load-bearing capacity of the outer shell, while the cohesive strength of soft rock controls plastic flow. The influence of in situ stress and support resistance on the evolution of the surrounding rock zone radii was quantitatively determined. The FLAC^3D strain-softening model accurately simulated the post-peak behavior of the surrounding rock. Analysis demonstrated specific inherent patterns in the magnitude, ratio, and orientation of principal stresses within the composite roof under mining influence. A high differential stress zone (σ₁/σ₃ = 6–7) formed within 20 m of the working face, accompanied by a deflection of the maximum principal stress direction by 53, triggering the expansion of a butterfly-shaped plastic zone. Based on these insights, we proposed and implemented a synergistic control system integrating high-pressure grouting, pre-stressed cables, and energy-absorbing bolts. Field tests demonstrated significant improvements: roof-to-floor convergence reduced by 48.4%, rib-to-rib convergence decreased by 39.3%, microseismic events declined by 61%, and the self-stabilization period of the surrounding rock shortened by 11%. Consequently, this research establishes a holistic “theoretical modeling-evolution diagnosis-synergistic control” solution chain, providing a validated theoretical foundation and engineering paradigm for composite roof support design. Full article

This study focuses on the multiphase flow in horizontal wells based on distributed acoustic sensing (DAS) monitoring. Through laboratory experiments and field data analysis, it was found that the micro-seismic differences in flow patterns can be clearly observed in the fiber optic micro-seismic waterfall chart. In the case of slug flow, the DAS acoustic energy decreases when the inclination angle increases. The performance of annular flow is similar to that of bubble flow, with the DAS energy increasing as the inclination angle increases. Overall, the order of DAS acoustic energy from the strongest to weakest is slug flow, followed by annular flow, and then bubble flow. The research shows that fiber optic DAS monitoring signals can effectively identify differences in gas volume, well inclination, and flow pattern, which provides an important technical basis and research foundation for the monitoring and analysis of multiphase flow in horizontal wells. Full article

The Jidong Oilfield No. 2 Underground Gas Storage (UGS), located in an active fault zone in Northern China, is a key facility for ensuring natural gas supply and peak regulation in the Beijing–Tianjin–Hebei region. To evaluate the effectiveness of a combined surface and shallow borehole monitoring system under deep reservoir conditions, a 90-day microseismic monitoring trial was conducted over a full injection cycle using 16 surface stations and 1 shallow borehole station. A total of 35 low-magnitude microseismic events were identified and located using beamforming techniques. Results show that event frequency correlates positively with wellhead pressure variations instead of the injection volume, suggesting that stress perturbations predominantly control microseismic triggering. Events were mainly concentrated near the bottom of injection wells, with an average location error of approximately 87.5 m and generally shallow focal depths, revealing limitations in vertical resolution. To enhance long-term monitoring performance, this study recommends deploying geophones closer to the reservoir, constructing a 3D velocity model, applying AI-based phase picking, expanding array coverage, and developing a microseismic-injection coupling early warning system. These findings provide technical guidance for the design and deployment of long-term monitoring systems for deep reservoir conversions into UGS facilities. Full article

To improve the efficiency and accuracy of microseismic event extraction from time-series data and enhance the detection of anomalous events, this paper proposes a Multi-scale Fusion Convolution and Dilated Convolution Autoencoder (MDCAE) combined with a Constraint Shape-Based Distance algorithm incorporating volatility (CSBD-Vol). MDCAE extracts low-dimensional features from waveform signals through multi-scale fusion and dilated convolutions while introducing the concept of waveform volatility (Vol) to capture variations in microseismic waveforms. An improved Shape-Based Distance (SBD) algorithm is then employed to measure the similarity of these features. Experimental results on a microseismic dataset from the 802 working faces of a mining site demonstrate that the CSBD-Vol algorithm significantly outperforms SBD, Shape-Based Distance with volatility (SBD-Vol), and Constraint Shape-Based Distance (CSBD) in classification accuracy, verifying the effectiveness of constrained time windows and volatility in enhancing performance. The proposed clustering algorithm reduces time complexity from $O\left(n^2\right)$ to $O(nlogn)$, achieving substantial improvements in computational efficiency. Furthermore, the MDCAE-CSBD-Vol approach achieves 87% accuracy in microseismic time-series waveform classification. These findings highlight that MDCAE-CSBD-Vol offers a novel, precise, and efficient solution for detecting anomalous events in microseismic systems, providing valuable support for accurate and high-efficiency monitoring in mining and related applications. Full article

This study focuses on the monitoring and early warning of landslide hazards induced by blasting caving in the Shizhuyuan polymetallic mine. A 30-channel microseismic monitoring system was deployed to capture the spatiotemporal characteristics of rock mass fracturing during a large-scale directional stratified blasting operation (419 tons) conducted on 21 June 2012. A total of 85 microseismic events were recorded, revealing two distinct zones of intense rock failure: Zone I (below 630 m elevation, P1–P3, C6–C8) and Zone II (above 630 m elevation, P4–P5, C1–C6). The upper slope collapse occurred within 5 min post-blasting, as documented by real-time monitoring and video recordings. Principal component analysis (PCA) was applied to 54 microseismic events in Zone II to determine the kinematic characteristics of the slip surface, yielding a dip direction of 324.6° and a dip angle of 73.2°. Complementary moment tensor analysis further revealed that shear failure dominated the slope instability, with pronounced shear fracturing observed in the 645–700 m height range. This study innovatively integrates spatial microseismic event distribution with geomechanical mechanisms, elucidating the dynamic evolution of blasting-induced landslides. The proposed methodology provides a novel approach for monitoring and forecasting slope instability triggered by underground mining, offering significant implications for disaster prevention in similar mining contexts. Full article

In this study, we analyzed the microearthquake seismicity in the Enguri area (Georgia) recorded between 2020 and 2023 using a newly installed seismic network developed within the DAMAST project. The high sensitivity of the network allowed the detection of even very small seismic events, enabling a detailed investigation of the temporal dynamics of local seismicity. Statistical analyses suggest that the seismic activity around the Enguri Dam is influenced by a combination of natural tectonic processes and subtle reservoir-induced stress changes. While the dam does not appear to exert strong seismic forcing, the observed ≈7-month delay between water level variations and seismicity may indicate a triggering effect. Localized stress variations and temporal clustering further support the hypothesis that water level fluctuations modulate seismic activity. Additionally, the mild persistence in interoccurrence times is consistent with a stress accumulation and delayed triggering mechanism associated with reservoir loading. Full article

Coal mine disaster safety monitoring often employs microseismic technology for its high sensitivity and real-time capability. However, nonlinear, non-stationary, and multi-scale signals limit traditional time series models (e.g., ARMA, ARIMA). This paper proposes a hybrid deep learning model—VMD-SE-CEEMDAN-BO-CNNGRU—integrating variational mode decomposition, sample entropy, CEEMDAN, Bayesian optimization, and a CNN-GRU architecture. Microseismic data from the 08 working face in D mine (Weibei mining area) were used to predict daily maximum energy, average energy, and frequency. The model achieved high predictive performance with R² values of 0.93, 0.89, and 0.88, significantly outperforming baseline models lacking modal decomposition. Comparative experiments verified the superiority of the VMD-first, SE-reconstruction, and CEEMDAN-second decomposition strategy, yielding up to 13% greater accuracy than reverse-order schemes. The model maintained R² above 0.80 on another dataset from the 03 working face in W mine (Binchang mining area), demonstrating robust generalization. Although performance declined during fault disturbances, accuracy for average energy and frequency rebounded post-disturbance, indicating strong adaptability. Overall, the VSCB-CNNGRU model enhances both accuracy and stability in microseismic prediction, supporting dynamic risk assessment and early warning in coal mining. Full article

Sudden coal and gas outbursts pose a significant hazard in deep-seated coal seam extraction, necessitating reliable risk assessment methods. Traditionally, assessments focus on gas-dynamic parameters, but experience shows they must be supplemented with tectonic factors such as fault-related disturbances, weak interlayers, and increased fracturing. Even minor faults in the Karaganda Basin can weaken the coal massif and trigger outbursts. The integration of 3D modeling enhances risk evaluation by incorporating both dynamic (gas-related) and static (tectonic) parameters. Based on exploratory drilling and geophysical studies, these models map coal seam geometry, fault positioning, and high-risk structural zones. In weakened coal areas, stress distribution changes can lead to avalanche-like gas releases, even under normal gas-dynamic conditions. An expert scoring system was used to convert geological and gas-dynamic data into a comprehensive risk index guiding preventive measures. An analysis of Karaganda Basin incidents (1959–2021) shows all outbursts occurred in geological disturbance zones, with 43% linked to fault proximity, 30% to minor tectonic shifts, and 21% to sudden coal seam changes. Advancing 3D modeling, geomechanical analysis, and microseismic monitoring will improve predictive accuracy, ensuring safer coal mining operations. Full article