A review of the current status of induced seismicity monitoring for hydraulic fracturing in unconventional tight oil and gas reservoirs

[1] The Aerospace Corporation's Nightglow Imager observed a large step function change in airglow in the form of a traveling front in the OH Meinel (OHM) and O2atmospheric (O2A) airglow emissions over Alice Springs, Australia, on 2 February 2003. The front exhibited nearly a factor of 2 stepwise increase in the OHM brightness and a stepwise decrease in the O2A brightness. There was significant (∼25 K) cooling behind the airglow fronts. The OHM airglow brightness behind the front was among the brightest for Alice Springs that we have measured in 7 years of observations. The event was associated with a strong phase-locked 2 day wave (PL/TDW). We have analyzed the wave trapping conditions for the upper mesosphere and lower thermosphere using a combination of data and empirical models and found that the airglow layers were located in a region of ducting. The PL/TDW-disturbed wind profile was effective in supporting a high degree of ducting, whereas without the PL/TDW the ducting was minimal or nonexistent. The change in brightness in each layer was associated with a strong leading disturbance followed by a train of weak barely visible waves. In OHM the leading disturbance was an isolated disturbance resembling a solitary wave. The characteristics of the wave train suggest an undular bore with some turbulent dissipation at the leading edge.

An intense traveling airglow front in the upper mesosphere--lower thermosphere with characteristics

The Earth’s crustal stress field controls active deformation and reflects the processes driving plate tectonics. Here we present the first quantitative synthesis of relative principal stress magnitudes throughout North America together with hundreds of new horizontal stress orientations, revealing coherent stress fields at various scales. A continent-scale transition from compression (strike-slip and/or reverse faulting) in eastern North America to strike-slip faulting in the mid-continent to predominantly extension in western intraplate North America is likely due (at least in part) to drag at the base of the lithosphere. Published geodynamic models, incorporating gravitational potential energy and tractions from plate motions or relative mantle flow, successfully predict most large-wavelength stress rotations but not the shorter-wavelength (<~200 km) rotations observed in the western USA. The stresses resulting from glacial isostatic adjustment appear to be much smaller than the magnitude of ambient tectonic stresses in the crust at depth. The authors here present a stress map of the North American crust that gives a new view of dynamics of the continent. The results can be applied to probabilistic seismic hazard analysis and resource development as well as to provide constraints for theoretical models of crustal dynamics.

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Multiscale variations of the crustal stress field throughout North America.

Since the beginning of the US shale gas revolution in 2005, the development of unconventional oil and gas resources has gathered tremendous pace around the world. This book provides a comprehensive overview of the key geologic, geophysical, and engineering principles that govern the development of unconventional reservoirs. The book begins with a detailed characterization of unconventional reservoir rocks: their composition and microstructure, mechanical properties, and the processes controlling fault slip and fluid flow. A discussion of geomechanical principles follows, including the state of stress, pore pressure, and the importance of fractures and faults. After reviewing the fundamentals of horizontal drilling, multi-stage hydraulic fracturing, and stimulation of slip on pre-existing faults, the key factors impacting hydrocarbon production are explored. The final chapters cover environmental impacts and how to mitigate hazards associated with induced seismicity. This text provides an essential overview for students, researchers, and industry professionals interested in unconventional reservoirs.

Unconventional Reservoir Geomechanics: Shale Gas, Tight Oil, and Induced Seismicity

In this, the twentieth Campbell Memorial lecture, I propose to discuss that aspect of metallurgy which has come to be known as mechanical metallurgy. This kind of metallurgy comprises our efforts to understand the relationships among the measured mechanical properties of metals and their mechanical behavior in service, and to understand the way in which these mechanical properties and service characteristics are controlled by chemical composition and structure. This field was not one which received a large amount of consideration from Professor Campbell, but I am sure that if he were alive today it would command his attention. In his day, the factors that influence the structure of alloys engaged the talents of some of the best men, and Professor Campbell and his contemporaries nobly inaugurated the work which has given us such a complete understanding of how to control the structure of metals, that many of us now feel free to turn to other problems. Of course this does not mean that no problems in that field remain to be solved. Rather, progress there has been so great that it now seems profitable to turn to other fields. I have chosen to concentrate on the problem of trying to understand quantitatively how alloying elements in solid solution, and undissolved compounds distributed in these solid solutions, affect mechanical properties. Broadly there are two aspects to what we mean by mechanical properties. These are succinctly called strength and ductility. By strength we mean the resistance of a substance to distortion or fracture, and by ductility we mean how much we may distort it before it fractures. There are many ways to measure these. I have chosen to concentrate on the simple tension test, for I believe it yields as much or more information than any other test. Indentation hardness, which has been widely used, yields much less information, and those who have used it only have missed much of interest. Even the tension test alone is inadequate to complete understanding, but by and large other tests are useful to supplement what the tension test can tell us. This applies particularly to measurements of strength, for which the tension test is almost wholly adequate; but it does not apply so well to ductility, where the conditions of loading are much more important and much less well understood in their action. There are two aspects to what we mean by strength. These are, first, resistance to flow, and second, resistance to fracture [1]. Both are functions of numerous variables, and can only be plotted on two-dimensional space by ignoring or holding constant all the variables but one. Both must be thought of as surfaces in multi-dimensional space. Among the variables which may influence both the flow and fracture stress, aside from composition and structure which are our principal concern today, we may list strain, time rate of straining, and temperature as the ones that most commonly are taken into consideration. In Fig. 1, the resistance to flow and the resistance to fracture are plotted against strain. I like to refer to these two curves simply as the flow curve and the fracture curve, for the effect of deformation on Reprinted from Transactions of the American Society for Metals, 36, 30–60 (1946). Copyright 1946 by the American Society for Metals, Cleveland, Ohio.

Strength and Ductility

We extend a full-waveform modeling method to invert source focal-plane mechanisms for microseismic data recorded with dual-borehole seismic arrays. Combining inverted focal-plane mechanisms with geomechanics knowledge, we map the pore pressure distribution in the reservoir. Determining focal mechanisms for microseismic events is challenging due to poor geometry coverage. We use the P-wave polarities, the P- and S-wave similarities, the SV/P amplitude ratio, and the SH/P amplitude ratio to invert the focal-plane mechanisms. A synthetic study proves that this method can effectively resolve focal mechanisms with dual-array geometry. We apply this method to 47 relatively large events recorded during a hydraulic fracturing operation in the Barnett Shale. The focal mechanisms are used to invert for the orientation and relative magnitudes of the principal stress axes, the orientation of the planes slipping in shear, and the approximate pore pressure perturbation that caused the slip. The analysis of the ...

Estimating geomechanical parameters from microseismic plane focal mechanisms recorded during multistage hydraulic fracturing

An immediate report of the source focal mechanism with full automation after a destructive earthquake is crucial for timely characterizing the faulting geometry, evaluating the stress perturbation, and assessing the aftershock patterns. Advanced technologies such as Artificial Intelligence (AI) has been introduced to solve various problems in real-time seismology, but the real-time source focal mechanism is still a challenge. Here we propose a novel deep learning method namely Focal Mechanism Network (FMNet) to address this problem. The FMNet trained with 787,320 synthetic samples successfully estimates the focal mechanisms of four 2019 Ridgecrest earthquakes with magnitude larger than Mw 5.4. The network learns the global waveform characteristics from theoretical data, thereby allowing the extensive applications of the proposed method to regions of potential seismic hazards with or without historical earthquake data. After receiving data, the network takes less than two hundred milliseconds for predicting the source focal mechanism reliably on a single CPU. The authors here present a deep learning method to determine the source focal mechanism of earthquakes in realtime. They trained their network with approximately 800k synthetic samples and managed to successfully estimate the focal mechanism of four 2019 Ridgecrest earthquakes with magnitudes larger than Mw 5.4.

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Real-time determination of earthquake focal mechanism via deep learning.

\n The stability and robustness of determining earthquake magnitude are of great significance in earthquake monitoring and seismic hazard assessment. The routine workflow of determining earthquake local magnitude, such as the widely used Richter magnitude, may result in an unreliable measurement of earthquake magnitude because it relies on individual amplitude measurement of a single station, which is prone to be influenced by natural impulsive noise or anthropogenic noise. In this study, we present an automated estimation of earthquake magnitude by applying a deep-learning algorithm named magnitude neural network (MagNet) based on the full-waveform recordings from a network of seismic stations at China seismic experimental site (CSES). The MagNet consists of a compression component that extracts the global features of waveform data and an expansion component that yields a Gaussian probability distribution representing the magnitude estimation. The MagNet is trained with an augmented data set, which includes 21,700 training samples with evenly distributed magnitudes. From the prediction results on the test data set, the mean errors and standard deviations are −0.017 and 0.21, respectively, for 600 moderate earthquakes with magnitudes ranging from 3 to 5.9, and −0.011 and 0.14, respectively, for 70 small earthquakes with magnitudes ranging from 2.3 to 3.5. However, it remains challenging for large earthquakes (magnitude>6.5), due to the lack of sufficient historical large earthquakes as training data. In addition, testing results show that the new method is capable of minimizing the impact of abnormal noises in the data. These results demonstrate the validity and merits of the proposed deep-learning method in predicting earthquake magnitude automatically.

Network‐Based Earthquake Magnitude Determination via Deep Learning

Microseismic monitoring is a practical technique for mapping hydraulic fractures. Accurate imaging of the event location is critical for fracture interpretation. Microseismic data, however, often present weak P-waves or no P-waves at all because of low signal-to-noise ratio. S-waves, on the other hand, are relatively large, and reliably observed. Therefore, it will be significantly meaningful if we can use only S-phase to determine the source location. We design two synthetic experiments to understand the possibility of using only S-phase to locate the microseismic events. In the first experiment we use only S-wave traveltime information to locate events, while in the second experiment we utilize S-wave traveltime along with incident angle to enhance accuracy. The experiment results suggest that using only S-wave traveltime information is not feasible to locate the microseismic events. But using S-wave traveltime and incident angle can produce an acceptable solution if the source-receiver distance is comparable with the acquisition aperture.

Locating microseismic events with S-wave data only

Time domain early-arrival waveform tomography is an advanced approach to image the near-surface velocity structures. For the reason of computation efficiency, we solve an acoustic wave equation problem instead of elastic wave equation for forward modeling. Since we are fitting the amplitude and phase of the early arrivals, but the waveform includes converted waves, we should understand the validity of such acoustic waveform tomography for imaging the near-surface velocity structures in reality. We design numerical experiments to compute the waveforms by applying elastic wave equation modeling and then apply acoustic early-arrival waveform tomography to study the liability of the imaging solutions. The results suggest that the imaging solutions is sensitive to the window size. It should be reliable as long as the time window of the early arrivals is properly selected. Testing the foothill velocity model with elastic waveform input produces a reasonable near-surface solution, but does require more sophisticated processing to remove elastic effects.

Wenhuan Kuang

Papers

Estimating geomechanical parameters from microseismic plane focal mechanisms recorded during multistage hydraulic fracturing

Real-time determination of earthquake focal mechanism via deep learning.

Network‐Based Earthquake Magnitude Determination via Deep Learning

Locating microseismic events with S-wave data only

Validity of Acoustic Early-Arrival Waveform Tomography For Near-Surface Imaging