stress field
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2022 ◽  
Vol 277 ◽  
pp. 108400
Tsutomu Ishimaru ◽  
Khin Thandar Hlaing ◽  
Ye Min Oo ◽  
Tin Mg Lwin ◽  
Kazuhiro Sasaki ◽  

L.K. Miroshnikova ◽  
A.Yu. Mezentsev ◽  
G.A. Kadyralieva ◽  
M.A. Perepelkin

This study focuses on the markers of tectonically stressed zones inside the rock mass, that were identified during the regional geodynamic zoning of the mine fields of the Talnakh orogenic system. Identification features for tracing geodynamically active structures within the western flank of the Talnakh orogenic system have been identified based on morphometric analysis of the Tunguska series sediments, which are the upper layer of the ore-bearing intrusions and associated ore deposits. In the larger morphostructural groups, the boundaries of contrastingly alternating zones of elevated and depressed absolute depths at the base and the roof of the Tunguska series sediments represent the boundaries of tectonic blocks of different elevation levels with sharply contrasting indices of terrain stress. The circular-shaped structures highlighted in the morphostructural schemes spatially coincide with the tectonic forms were formed as the result of strike-slip and torsional processes. A heterogeneity, which is reflected in the allocation of blocks with different values of the stress distribution coefficient (K) is identified in the initial stress field of the Tunguska series sediments. The boundaries of the geodynamic blocks that were identified using to different methods are identical. It is established that the assumed faults correspond to the faults identified based on the detailed exploration data.

2022 ◽  
Juan Briñez de León ◽  
Mateo Rico-García ◽  
Alejandro Restrepo-Martinez

2022 ◽  
Chengjun Feng ◽  
Guangliang Gao ◽  
Shihuai Zhang ◽  
Dongsheng Sun ◽  
Siyu Zhu ◽  

Abstract. The Tangshan region is one of the most seismically active areas in the North China, and the 1976 M 7.8 earthquake occurred on July 28th near the Tangshan fault zone. The Matouying Enhanced Geothermal Systems (EGS) field is located ~90 km away from Tangshan City. Since the late 2020, preliminary hydraulic stimulation tests have been conducted at depths of ~3965–4000 m. Fluid injection into geothermal reservoir facilitates heat exchanger system. However, fluid injection may also induce earthquakes. In anticipation of the EGS operation at the Matouying uplift, it is essential to assess how the fault slip potential of the nearby active and quiescent faults will change in the presence of fluid injection. In this study, we first characterize the ambient stress field in the Tangshan region by performing stress tensor inversions using 98 focal mechanism data (ML ≥ 2.5). Then, we estimate the principal stress magnitudes near the Matouying EGS field by analyzing in situ stress measurements at shallow depths (~600–1000 m). According to these data, we perform a quantitative risk assessment using the Mohr-Coulomb framework in order to evaluate how the main active faults might respond to hypothetical injected-related pore pressure increases due to the upcoming EGS production. Our results mainly show that most earthquakes in the Tangshan seismic region have occurred on the faults that have relatively high fault slip potential in the present ambient stress field. At well distances of less than 15 km, the probabilistic fault slip potential on most of the boundary faults increase with continuing fluid injection over time, especially on these faults with well distances of ~6–10 km. The probabilistic fault slip potential increases linearly with the fluid injection rate. However, the FSP values decrease exponentially with increased unit permeability. The case study of the Matouying EGS field has important implications for the deep geothermal exploitation in China, especially for Gonghe EGS (in Qinghai province) and Xiong’an New Area (in Hebei province) geothermal reservoirs that are close to the Quaternary active faults. Ongoing injection operations in the regions should be conducted with these understandings in mind.

2022 ◽  
Sheng Zheng ◽  
Wei Zhou ◽  
Xiaoguang Wang ◽  
Liang Chen ◽  
Dan Xie ◽  

Abstract China has abundant low-permeability oil and gas resources. A lot of practice has proved that low-permeability reservoirs must undergo hydraulic fracturing to obtain commercial production capacity. Geomechanical characteristics are the key factor for fracturing. It plays a very important role in the oil field exploration and production. It is not only the driving force for oil and gas migration, but also provides a basis for wellbore stability analysis and drilling optimization design. The state of the formation stress field and the mechanical properties of the rock jointly determine the direction, shape and orientation of the fracture extension of the fracturing. Together it affects the stimulation effect of fracturing. Realizing the high-efficiency development of low-permeability reservoirs is a key and difficult problem facing for oil filed operator. Horizontal wells drilling and hydraulic fracturing are the core technology for increasing single well production in low-permeability reservoirs. The effectiveness of reservoir reconstruction directly determines the production capacity of the reservoir. In order to clarify the influence of static and dynamic geomechanics on the scale of reservoir stimulation in the process of horizontal well fracturing, and ultimately provide effective technical support for the formulation and optimization of reservoir stimulation design. This study is based on the study of single well one-dimensional geomechanics, using the structural characteristics and seismic attributes of low-permeability reservoirs to study the characteristics of the three-dimensional spatial distribution of mechanics. On this basis, combined with real-time fracturing treatment data and micro-seismic monitoring data, dynamic (four-dimensional) stress field simulations are continuously carried out. The research results can be mainly used to guide the optimization of reservoir stimulation and the evaluation of filed development plan.

2022 ◽  
Vol 9 ◽  
Zhiwei Zhang ◽  
Chuntao Liang ◽  
Feng Long ◽  
Min Zhao ◽  
Di Wang

The June 17, 2019, MS 6.0 Changning earthquake is the largest recorded event in the Sichuan basin, spatiotemporal variations of stress field may shed light on the seismogenic mechanism of the earthquake. We determined the focal mechanism solutions (FMSs) of 124 earthquakes with MS ≥ 3.0 occurring in the Changning area from April 1, 2007, to February 29, 2020, and analyzed changes of FMSs and stress field before and after Changning earthquake. The Changning aftershocks were predominantly thrust fault earthquakes, followed by strike slip. The P-axis azimuths of the aftershock FMSs were oriented predominantly in the NEE direction, notably differing from the NWW-oriented P-axis azimuths of pre-earthquake FMSs; it shows the rotation of local stress field before and after the Changning earthquake, it is speculated that the change of stress field in Changning area may be caused by long-term water injection and salt mining activities. From the southeast to the northwest of the aftershock zone, the azimuths of principal compressive stress (S1) change from NEE to near-EW in both horizontal and vertical planes. Significant changes occurred in the FMS types and stress field of the aftershock zone following the Changning earthquake, the FMSs became diverse, the S1 azimuth of the Changning area changed from NWW to NEE, and then EW, the plunge and stress tensor variances increased, it reflects that the stress field of the Changning area adjusts continually with time.

2022 ◽  
pp. 108128652110555
Ankit Shrivastava ◽  
Jingxiao Liu ◽  
Kaushik Dayal ◽  
Hae Young Noh

This work presents a machine-learning approach to predict peak-stress clusters in heterogeneous polycrystalline materials. Prior work on using machine learning in the context of mechanics has largely focused on predicting the effective response and overall structure of stress fields. However, their ability to predict peak – which are of critical importance to failure – is unexplored, because the peak-stress clusters occupy a small spatial volume relative to the entire domain, and hence require computationally expensive training. This work develops a deep-learning-based convolutional encoder–decoder method that focuses on predicting peak-stress clusters, specifically on the size and other characteristics of the clusters in the framework of heterogeneous linear elasticity. This method is based on convolutional filters that model local spatial relations between microstructures and stress fields using spatially weighted averaging operations. The model is first trained against linear elastic calculations of stress under applied macroscopic strain in synthetically generated microstructures, which serves as the ground truth. The trained model is then applied to predict the stress field given a (synthetically generated) microstructure and then to detect peak-stress clusters within the predicted stress field. The accuracy of the peak-stress predictions is analyzed using the cosine similarity metric and by comparing the geometric characteristics of the peak-stress clusters against the ground-truth calculations. It is observed that the model is able to learn and predict the geometric details of the peak-stress clusters and, in particular, performed better for higher (normalized) values of the peak stress as compared to lower values of the peak stress. These comparisons showed that the proposed method is well-suited to predict the characteristics of peak-stress clusters.

JOM ◽  
2022 ◽  
Chao Wang ◽  
Chong Deng ◽  
Yuru Wu ◽  
Linjiang Chai ◽  
Kang Xiang ◽  

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