Microseismic Monitoring Applications Research Articles

Application of Micro-Seismic Monitoring in Post-Fracturing Evaluation of Shale Gas: A Case Study of Well X from Puguang Area, China

Multi-stage fracturing of horizontal wells is an indispensable technology to create complex fracture networks, which can unlock production potential and enable commercial productivity for shale gas with low porosity and permeability. Real-time monitoring of fracture networks is essential for adjusting key parameters, mitigating fracturing risks, and achieving optimal fracturing effects. Micro-seismic monitoring technology accurately captures and describes the development of fracture networks by detecting micro-seismic waves generated through rock ruptures, providing valuable insights into the evaluation of post-fracturing. In this study, we first introduced the basic parameters of well X that were obtained by laboratory experiments and logging interpretation, including porosity, gas-bearing properties, mineral composition, rock mechanics, and crustal stress. Then, the hydraulic fracturing scheme was designed on the basis of the geological engineering characteristics of well X. Finally, we conducted a comprehensive analysis of various factors that can affect hydraulic fracturing. This included an examination of the impact of pre-fluid temporary plugging and fracture complexity on the overall effectiveness of the operation. Based on the laboratory experiments and theoretical analysis, the following conclusions can be drawn: (1) fracture size is essentially formed when the fluid strength exceeds 35 m3/m; (2) both preflush with high viscosity and the amount of power sand exceeding 20 cubic meters are conducive to the propagation of fracture height; (3) temporary plugging balls facilitate the balanced propagation of multiple fracture clusters within a stage, whereas temporary plugging particles promote the formation of complex fractures; and (4) geological conditions are a prerequisite for creating a complex network of fractures, and only engineering techniques can facilitate the appropriate enhancement of fracture complexity. This study provides an essential method for the fracturing design of shale gas.

Application of Microseismic Monitoring in Analysing the Stability of Underground Cavern

Abstract Long term stability of an underground cavern is significant for construction engineers. For structures constructed in the geologically active regions of the Himalayas, continuous monitoring is required to ward off the sustained threats due to seismic activities and associated local geological hazards. Underground powerhouse of Tapovan Vishnugad Hydropower Power Project (TVHPP), Chamoli, Uttarakhand is one such powerhouse that encountered various types of rockmass failures both during and post construction. The project area is located about 2.0 km north of the Main Central Thrust (MCT) and the rockmass cavern has numerous joints, shear and seam zones and fractures. This pose threat to the long term stability of this underground powerhouse cavern. To analyse the rockmass stability of the powerhouse, real time microseismic monitoring system was installed in this underground cavern. Spatio-temporal variation of the recorded seismic events has been analysed for microseismic events distribution in terms of seismic energy, displacement, cumulative apparent volume and various other contours led to the identification of potentially hazardous zones in the underground rockmass structure.

An integrated 3D fracture network reconstruction method based on microseismic events

3D fracture network modeling remains a challenging work with the wide application of microseismic monitoring in multi-stage hydraulic fracturing. Due to the existence of invalid noise events, conventional methods often have significant deviations in detecting fracture geometry for fracture modeling after multi-stage fracturing.To solve the problem, we present an integrated 3D fracture reconstruction method that incorporated random sampling consensus algorithm (RANSAC), density-based spatial clustering of applications with noise algorithm (DBSCAN), and the alpha-shape method. To evaluate the effectiveness and robustness quantitatively, we also develop a verification workflow based on synthetic events generated by discrete fracture networks (DFN) with different fracture numbers, fracture scales, and dip angles. Through a large number of simulations, it is found that our method has perfect accuracy and good anti-noise ability in handling events with a high proportion of noise. Besides, our method has been successfully applied in a horizontal well stimulated by multi-stage fracturing in Sichuan Basin of China. The reconstructed fracture network is very consistent with the field production after source mechanism analysis and history matching. Our method is helpful to obtain a more accurate 3D fracture network model after stimulation and provides a basis for fracturing modeling in shale gas development.

Deep Convolutional Neural Network for Microseismic Signal Detection and Classification

Reliable automatic microseismic waveform detection with high efficiency, precision, and adaptability is the basis of stability analysis of the surrounding rock mass. In this paper, a convolutional neural network (CNN)-based microseismic detection network (CNN-MDN) model was established and well trained to a high degree of accuracy using a dataset with 16,000 preprocessed waveforms. By comparison with other methods, 4000 waveforms were tested to evaluate the precision, recall, and F1-score. The results revealed that the CNN-MDN demonstrated the highest performance in microseismic detection. Moreover, the low sensitivity of the CNN-MDN to noise of different intensities was proved by testing on semi-synthetic data. The model also possesses good generalization ability and superior performance capability for microseismic detection under different geological structure backgrounds, and it can correctly detect the microseismic events with Mw ≥ 0.5. These preliminary results show that the CNN-MDN can be directly applied to unprocessed microseismic data and has great potential in real-time microseismic monitoring applications.

Parallel Source Scanning Algorithm using GPUs

The use of methods using waveform stacking are nowadays more common in microseismic monitoring applications because they avoid manual or automatic phase picking. The Source Scanning Algorithm (SSA) is a widely known technique in which the source location is estimated using a brightness function obtained from stacking the normalized absolute amplitude seismograms recorded at several stations. The SSA has the advantage of the straightforwardness of its implementation but has the inconvenience of being computationally costly even for small-scale experiments. Our approach is then to parallelize the sequential SSA using graphics processing units (GPUs), and we named this parallel version pSSA. We have parallelized the Stacking step of the SSA Algorithm because this is by far the most computationally demanding Step. This can be done efficiently because of the spatial independence of the data. In our test cases we performed sequential and parallel computations of the SSA and pSSA in two different platforms. Additionally, we compared the performance of pSSA with a parallel implementation using OpenMP. We demonstrate that pSSA has produced speedups up to 125× as compared to the sequential version. We implemented a client–server architecture to receive and process the data. This architecture can treat with various simultaneous clients and also with out-of-order data packets. This allows for re-sending lost or corrupted data. We anticipate that pSSA has the impact of allowing SSA like algorithm to be used in microseismic experiment design and the use of on-site real-time denoising techniques, as well as the potential of being used in traffic light warning systems for fluid injection operations.

Automated Seismic Event Location by Travel-Time Stacking: An Application to Mining Induced Seismicity

Online Material: Additional synthetic test results, plots, and tables. The automated location of seismic events is an important and challenging task in microseismic monitoring applications (e.g., to analyze induced seismicity following oil/geothermal field exploitation and mining operations), where we deal with a large number of seismic events and weak signals characterized by low signal‐to‐noise ratios. Given the large number of seismic events, manual location procedures are time consuming, or not feasible. Standard automated location routines require precise automated picking procedure and phases identification (Gharti et al. , 2010). These methods are, generally, modified versions of the Geiger (1910, 1912) algorithm, based on the minimization of time residuals between theoretical and observed arrival times of body waves (generally first P and S onsets) by iterative inversion algorithms. In the last two decades a large number of picking algorithms have been developed; although P onsets can now be accurately picked, the automatic picking of later seismic phases (including S onsets) is still problematic. Their performance is limited in the presence of noisy data, when picking and phase identification might be difficult. The increasing interest on microseismic monitoring applications pushed the recent development of alternative techniques for automated seismic event location. These methods, similar to migration techniques used in reflection seismology, exploit the full waveforms and do not need any prior phase identification. Some methods are …

Solution of Microseismic Monitoring Non-Linear Location Equations Based Matlab

Microseismic will produce when rock mass break. The occurrence of microseismic monitoring has close relationship to the energy release and crack's evolution in the rock mass. Therefore the microseismic monitoring can be used to predict the future rupture of the rock mass. MS location is the base of this prediction. The method of solving locating equation and the stability of answer are the key matter of microseismic monitoring applications in engineering fields. This paper uses Gauss-Newton iterative method to solve locating equation in order to advancing the precision and stability of answer and reducing the calculation workload. Based on the optimization toolbox of Matlab, the non-linear locating equations are solved and the visible locating results of microseismic are achieved.

Application of microseismic monitoring to characterise geomechanical conditions in longwall mining

To characterise the geomechanical conditions and responses to underground coal mining in different geological settings, we have undertaken microseismic monitoring at two longwall mines. Our work has concentrated on major concerns of the mines such as the patterns of induced fracturing in the roof and floor, the effect of geological faults and the possibility of water and methane gas inflows. Our results have comprehensively defined the fracture patterns. Applications for microseismic monitoring also exist for the mapping of hydraulic fracture growth in reservoir engineering and the data can be used for the study of shear wave anisotropy in fractured media.