Search Results (66)

The evaluation of the fracturing effect of coalbed methane (CBM) wells is crucial for the efficient development of CBM reservoirs. Currently, studies focusing on the evaluation of the hydraulic fracture stimulation effect of coal seams and the integrated analysis of “drilling-fracturing-monitoring” are relatively insufficient. Therefore, this paper takes three drainage and production wells in the coalbed methane block on the northwest wing of the Xiangxia anticline in the Bijie Experimental Zone of Guizhou Province as the research objects. In view of the complex geological characteristics of this area, such as multiple and thin coal seams, high gas content, and high stress and low permeability, the paper systematically summarizes the results of drilling and fracturing engineering practices of the three drainage and production wells in the area, including the application of key technologies such as a two-stage wellbore structure and the “bentonite slurry + low-solid-phase polymer drilling fluid” system to ensure wellbore stability, low-solid-phase polymer drilling fluid for wellbore protection, and staged temporary plugging fracturing. On this basis, a study on microseismic signal acquisition and tomographic energy inversion based on a ground dense array was carried out, achieving four-dimensional dynamic imaging and quantitative interpretation of the fracturing fractures. The results show that the fracturing fractures of the three drainage and production wells all extend along the direction of the maximum horizontal principal stress, with azimuths concentrated between 88° and 91°, which is highly consistent with the results of the in situ stress calculation from the previous drilling engineering. The overall heterogeneity of the reservoir leads to the asymmetric distribution of fractures, with the transformation intensity on the east side generally higher than that on the west side, and the maximum stress deformation influence radius reaching 150 m. The overall transformation effect of each well is good, with the effective transformation volume ratio of fracturing all exceeding 75%, and most of the target coal seams are covered by the fracture network, significantly improving the fracture connectivity. From the perspective of the transformed planar area per unit fluid volume, although there are numerical differences among the three wells, they are all within the effective transformation range. This study shows that microseismic fracture monitoring technology can provide a key basis for the optimization of fracturing technology and the evaluation of the production increase effect, and offers a solution to the problem of evaluating the hydraulic fracture stimulation effect of coal seams. Full article

Microseismic monitoring technology serves as a vital tool for assessing the stability of coal and rock masses. The precision of energy calculations and the ability to integrate data across different systems have a direct impact on the effectiveness of early warning systems for hazards such as rockburst. This study utilized the 6306 working face of Shandong Energy Group’s Dongtan Coal Mine as its experimental site to address data inconsistencies caused by variations in sensor responses, localization algorithms, and energy calculation methods among microseismic monitoring systems. Two microseismic monitoring platforms, designated as System A and System B, were deployed to conduct a comparative and integrative study of cross-system energy calculations. The optimization of sensor layout facilitated a comprehensive analysis of the differences between the two systems in terms of P-wave arrival times, amplitude–frequency characteristics, and localization accuracy. Results indicated that System A achieved significantly lower localization errors, with an average of 49 m, compared to System B’s average of 70 m. Substantial differences were also found in waveform amplitude and dominant frequency, with a correlation coefficient of only 0.59 between the two systems. To bridge these disparities, an energy calculation method based on the displacement gauge function was developed. By fitting a localized gauge function R(Δ) and incorporating empirical coefficients, the energy calculation outputs of both systems were harmonized. Validation experiments demonstrated that the linear correlation coefficient of energy calculations between Systems A and B increased to 0.977 under the new method, confirming its effectiveness for data unification. This research provides critical theoretical and technical guidance for integrating microseismic data across systems and establishing unified early warning standards, thus advancing the monitoring and prediction of dynamic hazards in mining environments. Full article

The development of mineral resources in China is increasingly targeting the recovery of residual ore bodies, which significantly elevates geotechnical risks, including goaf collapse and pillar instability. To address these challenges, this study developed an integrated ground pressure control system that coordinates mining, backfilling, and support technologies. The system is dynamically optimized through a microseismic monitoring-based feedback mechanism, forming a closed-loop disaster management framework. Based on a two-year microseismic monitoring campaign (October 2012–September 2014), which captured 103 located events, a strong spatial clustering of seismic activity was observed, with over 7% of event pairs occurring within 20 m, particularly at the abutment of the syncline ore body. A zonal early-warning model was established using key parameters (event rate ratio ≥ 3, cluster density ≥ 15 events/10,000 m³, energy ratio ≥ 4). The effectiveness of the control system was validated by a 5.9% reduction in located events and a decrease in maximum magnitude from 0.3 to −0.2, despite a 34% year-on-year increase in ore production in 2019. These results demonstrate that the integrated approach provides a reliable and adaptive solution for ground pressure disaster prevention during residual ore recovery. Full article

Rockburst disasters have become increasingly prevalent as distinct forms of subsurface geotechnical engineering advanced to the deep earth. Confronted with such a threatening subsurface geopressure disaster that poses a risk to personnel and equipment safety, the microseismic monitoring technology has been employed to track signals generated from rock fracture and collapse in the field. To guide the prevention and control of the hazard, the investigation conducted an effective microseismic data mining method. Through deep feature engineering and interpretable intelligence, a practical and available short-term prediction approach for the rockburst intensity class was developed. On the basis of rockburst case database collected from various underground geotechnical engineering, the neural network-based feature extraction method was conducted in the process of model training. The optimized model was obtained by combining the K-fold cross-validation approach with the structural parameter search methodology. The evaluation among the considered artificial intelligence models on the testing dataset was conducted and compared. Through analyses, the interpretable coupling intelligent model combining convolutional and recurrent neural networks for rockburst prediction were demonstrated with the most robust performance by evaluation metrics. Among them, the proposed adaptive feature extraction method leads the benchmark method by 6% for both accuracy and precision; meanwhile, the proposed metric generalization loss rate (GLR) for accuracy and precision in the validation–testing process reached 1.5% and 0.2%. Furthermore, the Shapley additive explanations (SHAP) approach was employed to verify the model interpretability by deciphering the model prediction from the perspective of the fined impact of input features. Therefore, the investigation demonstrates that the proposed method can predict rockburst intensity with robust generalization and feature extraction capabilities, which possess substantial engineering significance and academic worth. Full article

The regular ore body of the Xianglushan tungsten mine has been completely exploited. The remaining residual ore bodies face numerous hidden dangers, such as large and numerous abandoned mining areas, disorderly and small-scale mining sequences, delayed filling processes, and poor effectiveness. To achieve the zoning warning of ground pressure disasters such as roof caving, caving, and pillar collapse during the mining process of the hidden-danger ore body in the mine, a targeted warning technology system is proposed. We use microseismic monitoring systems to analyze events in the main monitoring areas and summarize specific ground pressure manifestation areas and event characteristics. Based on microseismic monitoring data that identified areas of significant ground pressure, a zoning model was constructed for risk rating and area locking. Based on this model, a safety warning technology for mining residual ore bodies with hidden dangers was established. Summarizing and analyzing, it is found that the disaster warning mode for controlling hidden dangers and residual ore body mining processes through microseismic monitoring is effective and has played a certain demonstration role, providing reference value for other similar mines. Full article

Rockburst disasters in deep underground engineering present significant safety hazards due to complex geological conditions and high in situ stresses. To address the limitations of traditional microseismic (MS) monitoring methods—namely, vulnerability to noise interference, low recognition accuracy, and limited computational efficiency—this study proposes an intelligent real-time monitoring and early warning framework that integrates deep learning, MS monitoring, and Internet of Things (IoT) technologies. The methodology includes db4 wavelet-based signal denoising for preprocessing, an improved Gaussian Mixture Model for automated waveform recognition, a U-Net-based neural network for P-wave arrival picking, and a particle swarm optimization algorithm with Lagrange multipliers for event localization. Furthermore, a cloud-based platform is developed to support automated data processing, three-dimensional visualization, real-time warning dissemination, and multi-user access. Field application in a deep-buried railway tunnel in Southwest China demonstrates the system’s effectiveness, achieving an early warning accuracy of 87.56% during 767 days of continuous monitoring. Comparative verification further indicates that the fine-tuned neural network outperforms manual approaches in waveform picking and event identification. Overall, the proposed system provides a robust, scalable, and intelligent solution for rockburst hazard mitigation in deep underground construction. Full article

In horizontal well technology, hydraulic fracturing has been established as an essential technique for enhancing hydrocarbon production. However, the complex architecture of fracture networks challenges conventional monitoring methods. Micro-seismic monitoring, recognized for its superior resolution and sensitivity, enables precise fracture morphology characterization. This study advances diagnostic capabilities through integrated field–laboratory investigations and multi-domain signal processing. Hydraulic fracturing experiments under varied geological conditions generated critical micro-seismic datasets, with quantitative analyses revealing asymmetric propagation patterns (total length 312 ± 15 m, east wing 117 m/west wing 194 m) forming a 13.37 × 10⁴ m³ stimulated reservoir volume. Spatial event distribution exhibited density disparities correlating with geophone offsets (west wing 3.8 events/m vs. east 1.2 events/m at 420–794 m distances). Advanced time–frequency analyses and inversion algorithms differentiated signal characteristics demonstrating logarithmic SNR (Signal-to-Noise Ratio)–magnitude relationships (SNR 0.49–4.82, R² = 0.87), with near-field events (<500 m) showing 68% reduced magnitude variance compared to far-field counterparts. Coupled numerical simulations confirmed stress field interactions where fracture trajectories deviated 5–15° from principal stress directions due to prior-stage stress shadows. Branch fracture networks identified in Stages 4/7/9/10 with orthogonal/oblique intersections (45–65° dip angles) enhanced stimulation reservoir volume (SRV) by 37–42% versus planar fractures. These geometric parameters—including height (20 ± 3 m), width (44 ± 5 m), spacing, and complexity—were quantitatively linked to micro-seismic response patterns. The developed diagnostic framework provides operational guidelines for optimizing fracture geometry control, demonstrating how heterogeneity-driven signal variations inform stimulation strategy adjustments to improve reservoir recovery and economic returns. Full article

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

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

Based on the engineering conditions of the 1303 working face in Zhaoxian Coal Mine, this study investigates the characteristics of mine pressure behavior and the stress-relief mechanism of advanced presplit blasting in a working face with a thick and hard roof in an extra-thick coal seam. Through a combination of numerical simulations and field experiments, the effects of advanced presplit blasting on stress distribution, roadway stability, and microseismic activity are analyzed. Corresponding mitigation measures and optimization strategies are proposed. The results indicate that the primary cause of deformation in the gob-side roadway is the superposition of lateral abutment pressure from the goaf and the front abutment pressure of the advancing working face. Advanced presplit blasting effectively reduces the magnitude of front abutment stress, inhibits its transmission, decreases the hanging area of the goaf roof, and alleviates vertical stress on the roadway side adjacent to the goaf. Furthermore, both the daily average and peak microseismic energy levels decrease as the working face approaches the advanced blasting zone. The implementation of advanced presplit blasting technology in working faces with thick and hard roofs within extra-thick coal seams significantly mitigates rockburst hazards, enhances roadway stability, and improves overall mining safety. Full article

The reserves of hot dry rock (HDR) geothermal resources are huge. The main method used to develop HDR geothermal resources is called an enhanced geothermal system (EGS), and this generally uses hydraulic fracturing. After nearly 50 years of research and development, more and more countries have joined the ranks engaged in the exploration and development of HDR in the world. This paper summarizes the base technologies, key technologies, and game-changing technologies used to promote the commercialization of HDR geothermal resources. According to the present situation of the exploration, development, and utilization of HDR at home and abroad, the evaluation and site selection, efficient and low-cost drilling, and geothermal utilization of HDR geothermal resources are defined as the base technologies. Key technologies include the high-resolution exploration and characterization of HDR, efficient and complex fracture network reservoir creation, effective microseismic control, fracture network connectivity, and reservoir characterization. Game-changing technologies include downhole liquid explosion fracture creation, downhole in-situ efficient heat transfer and power generation, and the use of CO₂ and other working fluids for high-efficient power generation. Most of the base technologies already have industrial applications, but future efforts must focus on reducing costs. The majority of key technologies are still in the site demonstration and validation phase and have not yet been applied on an industrial scale. However, breakthroughs in cost reduction and application effectiveness are urgently needed for these key technologies. Game-changing technologies remain at the laboratory research stage, but any breakthroughs in this area could significantly advance the efficient development of HDR geothermal resources. In addition, we conducted a comparative analysis of the respective advantages of China and the United States in some key technologies of HDR development. On this basis, we summarized the key challenges identified throughout the discussion and highlighted the most pressing research priorities. We hope these technologies can guide new breakthroughs in HDR geothermal development in China and other countries, helping to establish a batch of HDR exploitation demonstration areas. In addition, we look forward to fostering collaboration between China and the United States through technical comparisons, jointly promoting the commercial development of HDR geothermal resources. Full article

The reservoir stimulation technology based on horizontal-well hydraulic fracturing has become one of the key means for efficient development of shale gas reservoir. Accurately describing the geometric shape and statistical characteristics of fractures is an indispensable key point. In this paper, a novel regularization model is proposed to inverse the fracture parameters with joint constraints of production data and microseismic data. Fractal theory is firstly introduced to model the fracture network and the geometric shape can be controlled by several parameters. Fractures are adaptive at the height in same rank and then a novel inversion model is presented based on regularization theory. An alternative iterative algorithm is presented to approximate the optimal solution. Relative errors of 4.94% and 6.78% are found with the results of two synthetic tests. The mean square relative error of the history match is about 7.73% in the test on real data. The numerical experiments show the accuracy and efficiency of the proposed model and algorithm. Full article

Distributed Acoustic Sensing (DAS) has emerged as a groundbreaking technology in seismology, transforming fiber-optic cables into dense, cost-effective seismic monitoring arrays. DAS makes use of Rayleigh backscattering to detect and measure dynamic strain and vibrations over extended distances. It can operate using both pre-existing telecommunication networks and specially designed fibers. This review explores the principles of DAS, including Coherent Optical Time Domain Reflectometry (COTDR) and Phase-Sensitive OTDR (ϕ-OTDR), and discusses the role of optoelectronic interrogators in data acquisition. It examines recent advancements in fiber design, such as helically wound and engineered fibers, which improve DAS sensitivity, spatial resolution, and the signal-to-noise ratio (SNR). Additionally, innovations in deployment techniques include cemented borehole cables, flexible liners, and weighted surface coupling to further enhance mechanical coupling and data accuracy. This review also demonstrated the applications of DAS across earthquake detection, microseismic monitoring, reservoir characterization and monitoring, carbon storage sites, geothermal reservoirs, marine environments, and urban infrastructure surveillance. The study highlighted several challenges of DAS, including directional sensitivity limitations, vast data volumes, and calibration inconsistencies. It also addressed solutions to these problems, such as advances in signal processing, noise suppression techniques, and machine learning integration, which have improved real-time analysis and data interpretability, enabling DAS to compete with traditional seismic networks. Additionally, modeling approaches such as full waveform inversion and forward simulations provide valuable insights into subsurface dynamics and fracture monitoring. This review highlights DAS’s potential to revolutionize seismic monitoring through its scalability, cost-efficiency, and adaptability to diverse applications while identifying future research directions to address its limitations and expand its capabilities. Full article

Distributed Acoustic Sensing (DAS) offers numerous advantages, including resistance to electromagnetic interference, long-range dynamic monitoring, dense spatial sensing, and low deployment costs. We initially deployed a water–land DAS system at the Xinfengjiang (XFJ) Reservoir in Guangdong Province, China, to monitor earthquake events. Environmental noise analysis identified three distinct noise zones based on deployment conditions: periodic 18 Hz signals near surface-laid segments, attenuated low-frequency signals (<10 Hz) in the buried terrestrial sections, and elevated noise at transition zones due to water–cable interactions. The system successfully detected hundreds of teleseismic and regional earthquakes, including a Mw7.3 earthquake in Hualien and a local ML0.5 microseismic event. One year later, the DAS system was upgraded with two types of spiral sensor cables at the end of the submarine cable, extending the total length to 5.51 km. The results of detecting both active (transducer) and passive sources (cooperative vessels) highlight the potential of integrating DAS interrogators with spiral sensor cables for the accurate tracking of underwater moving targets. This field trial demonstrates that DAS technology holds promise for the integrated joint monitoring of underwater acoustics and seismic signals beneath lake or ocean bottoms. Full article