High‐resolution and robust microseismic grouped imaging and grouping strategy analysis

Abstract

As an advanced real-time monitoring technique, microseismic source-location imaging provides valuable information during hydraulic fracturing, for example, the development of fracture networks and the effective reservoir reconstruction volume. However, microseismic data always suffer from weak induced energy and susceptibility to noise interference. In the case of a low signal-to-noise ratio, it is extremely challenging to perform robust microseismic imaging. Here, we first introduce several state-of-theart imaging conditions and two hybrid imaging conditions, which are followed by a detailed analysis of the impact of different grouping strategies. Then, we briefly analyse the sensitivity of different imaging conditions to noise using a one-dimensional signal. Next, several benchmark models, including two-dimensional Marmousi-II and three-dimensional SEG Advanced Modeling, are used as numerical examples for testing the passive-source imaging algorithms. Finally, three-dimensional real microseismic data are used to further investigate the impact of the grouping strategy on the imaging. The numerical examples and field data demonstrate the effectiveness of the proposed grouping strategy for the grouped imaging conditions.

Application and Research of Microseismic Monitoring System and Hydraulic Fracturing Technology in Coal Mines

“…In the experiment, the original microseismic data information was first detected by the geophone, and static correction was carried out. Using the reference [6] method as the comparison method, the comparison maps of the two methods before and after static correction of the microseismic data are shown in Figure 3. After the microseismic events were identified by the method in this paper, the inversion and positioning experiments were carried out for the microseismic events, and the results are shown in Figure 4.…”

“…The microseismic data information after static correction using the method described in this paper. (c) The microseismic data information after static correction using the method are illustrated in the reference [6]. As shown in Figure 3a, using the microseismic data detected by the geophone, the first break of the hydraulic fracturing microseismic event has a certain time difference on each detection trace before static correction.…”

“…It can effectively improve the signal-tonoise ratio and vertical resolution of stacked data sections. As shown in Figure 3c, after using the method proposed in reference [6] for static time shifting, the first arrival wave becomes flattened, but it cannot be guaranteed that the time difference between the first arrival waves of each detection channel is equal. The correction effect is worse than that As shown in Figure 3a, using the microseismic data detected by the geophone, the first break of the hydraulic fracturing microseismic event has a certain time difference on each detection trace before static correction.…”

“…The trained neural network is used to predict microseismic events, but the convergence speed of this method is slow, Water 2024, 16, 1062 2 of 16 and overfitting is prone to occur. Savvaidis A et al [6] used microseismic source location imaging technology to monitor the hydraulic fracturing process used in coal mine management. First, they analyzed the microseismic source location imaging conditions, developed monitoring strategies based on the analysis results, and then combined the characteristics of the microseisms generated by hydraulic fracturing, carried out 2D Marmousi-II and 3D SEG advanced modeling.…”

“…(b)The microseismic data information after static correction using the method described in this paper. (c) The microseismic data information after static correction using the method are illustrated in the reference[6].…”