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Mining operations can produce highly localized, low intensity earthquakes that are referred to as microseismic events. Monitoring of microseismic events is useful in predicting and comprehending hazards, and in evaluating the overall performance of a mine design. <p>A robust localization algorithm is used to estimate the source position of the microseismic event by selecting the hypothesized source location that maximizes an energy function generated from the sum of the time--aligned sensor signals. The accuracy of localization for the algorithm characterized by the variance depends in part upon the configuration of sensors. Two algorithms, MAXSRC and MINMAX, are presented that use the variance of localization error, in a particular direction, as a performance measure for a given sensor configuration.<p>The variance of localization error depends, in part, upon the energy spectral density of the microseismic event. The energy spectral density characterization of sensor signals received in two potash mines are presented and compared using two spectral estimation techniques: multitaper estimation and combined time and lag weighting. It is shown that the difference between the the two estimation techniques is negligible. However, the differences between the two mine characterizations, though not large, is significant. An example uses the characterized energy spectral densities to determine the variance of error for a single step localization algorithm.<p>The MAXSRC and MINMAX algorithms are explained. The MAXSRC sensor placement algorithm places a sensor as close as possible to the source position with the maximum variance. The MINMAX sensor placement algorithm minimizes the variance of the source position with the maximum variance after the sensor has been placed. The MAXSRC algorithm is simple and can be solved using an exhaustive search while the MINMAX algorithm uses a genetic algorithm to find a solution. These algorithms are then used in three examples, two of which are simple and synthetic. The other example is from Lanigan Potash Mine. The results show that both sensor placement algorithms produce similar results, with the MINMAX algorithm consistently doing better. The MAXSRC algorithm places a single sensor approximately 100 times faster than the MINMAX algorithm. The example shows that the MAXSRC algorithm has the potential to be an efficient and intuitively simple sensor placement algorithm for mine microseismic event monitoring. The MINMAX algorithm provides, at an increase in computational time, a more robust placement criterion which can be solved adequately using a genetic algorithm.

Successful development of shale gas reservoirs is highly dependent on hydraulic fracture treatments. Many questions remain in regards to the geometry of the created fractures. Production data analysis from some shale gas wells quantifies a much smaller stimulated pore volume than what would be expected from microseismic evidence and reports of fracturing fluids reaching distant wells. In addition, claims that hydraulic fracturing may open or reopen a network of natural fractures is of particular interest. This study examines hydraulic fracturing of shale gas formations with specific interest in fracture geometry. Several field cases are analyzed using microseismic analysis as well as net pressure analysis of the fracture treatment. Fracture half lengths implied by microseismic events for some of the stages are several thousand feet in length. The resulting dimensions from microseismic analysis are used for calibration of the treatment model. The fracture profile showing created and propped fracture geometry illustrates that it is not possible to reach the full fracture geometry implied by microseismic given the finite amount of fluid and proppant that was pumped. The model does show however that the created geometry appears to be much larger than half the well spacing. From a productivity standpoint, the fracture will not drain a volume more than that contained in half of the well spacing. This suggests that for the case of closely spaced wells, the treatment size should be reduced to a maximum of half the well spacing. This study will provide a framework for understanding hydraulic fracture treatments in shale formations. In addition, the results from this study can be used to optimize hydraulic fracture treatment design. Excessively large treatments may represent a less than optimal approach for developing these resources.

Mining operations can produce highly localized, low intensity earthquakes that are referred to as microseismic events. Monitoring of microseismic events is useful in predicting and comprehending hazards, and in evaluating the overall performance of a mine design. <p>A robust localization algorithm is used to estimate the source position of the microseismic event by selecting the hypothesized source location that maximizes an energy function generated from the sum of the time--aligned sensor signals. The accuracy of localization for the algorithm characterized by the variance depends in part upon the configuration of sensors. Two algorithms, MAXSRC and MINMAX, are presented that use the variance of localization error, in a particular direction, as a performance measure for a given sensor configuration.<p>The variance of localization error depends, in part, upon the energy spectral density of the microseismic event. The energy spectral density characterization of sensor signals received in two potash mines are presented and compared using two spectral estimation techniques: multitaper estimation and combined time and lag weighting. It is shown that the difference between the the two estimation techniques is negligible. However, the differences between the two mine characterizations, though not large, is significant. An example uses the characterized energy spectral densities to determine the variance of error for a single step localization algorithm.<p>The MAXSRC and MINMAX algorithms are explained. The MAXSRC sensor placement algorithm places a sensor as close as possible to the source position with the maximum variance. The MINMAX sensor placement algorithm minimizes the variance of the source position with the maximum variance after the sensor has been placed. The MAXSRC algorithm is simple and can be solved using an exhaustive search while the MINMAX algorithm uses a genetic algorithm to find a solution. These algorithms are then used in three examples, two of which are simple and synthetic. The other example is from Lanigan Potash Mine. The results show that both sensor placement algorithms produce similar results, with the MINMAX algorithm consistently doing better. The MAXSRC algorithm places a single sensor approximately 100 times faster than the MINMAX algorithm. The example shows that the MAXSRC algorithm has the potential to be an efficient and intuitively simple sensor placement algorithm for mine microseismic event monitoring. The MINMAX algorithm provides, at an increase in computational time, a more robust placement criterion which can be solved adequately using a genetic algorithm.

Ground control, one of the key elements in mine safety, is an issue that warrants continuous improvement in the underground coal industry. The United States experienced over 3,300 injuries and 42 deaths between 2006 and 2012 from the fall of a roof or rib (MSHA, 2015). Out of the underground coal mining methods, room and pillar retreat mining lacks significant research to adequately understand the rockmass behavior associated with the process. A microseismic monitoring system was installed in a retreat mine in Southwest Virginia to provide more information about the changing stress conditions created by retreating and ultimately reduce risk to miners. Microseismicity has been proven to be an acceptable method of monitoring stress redistribution in underground coal mines and assist in explaining rockmass behavior (Luxbacher, et al, 2007). An array of geophones was placed underground along a single retreat panel to record failures due to stress redistribution throughout one panel of retreat. These microseismic events were located, and their moment magnitudes were found. An analysis was completed to observe the redistribution of stress and related gob formation throughout the panel's retreat. Expectations for the gob formation were consistent with the distribution of microseismic events. Over 13,000 microseismic events were found in 1.5 months of monitoring. Approximately 2,800 of these events were well enough located to provide analysis of the changing underground stress conditions from the retreat process. On average, recorded microseismic events during retreat produced a moment magnitude of -0.9, with no events higher than a magnitude of 2.0. / Master of Science

In contrast to large-scale earthquakes which are caused when energy is released as a result of rock failure along a fault, microseismic events are caused when human activities, such as mining or oil and gas production, change the stress distribution or the volume of a rockmass. During such processes, microseismic event location, which aims at estimating source locations accurately, is a vital component of observing, diagnosing and acting upon the dynamic indications in reservoir performance by tracking the fracturing properly. Conventional methods for microseismic event location face considerable drawbacks. For example, traveltime based methods require manual labor in traveltime picking and thus suffer from the heavy workload of human interactions and manmade errors. Migration based and waveform inversion based location methods demand large computational memory and time for simulating the wavefields, especially in face of tens of thousands of microseismic events recorded. In this thesis research, we developed an approach based on a deep CNN for the purpose of microseismic event location, which is completely automatic with no human interactions like traveltime picking and also computationally friendly due to no requirement of wavefield simulations. An example in which the network is well-trained on the synthetic data from the smooth SEAM model and tested on the true SEAM model has shown its accuracy and efficiency. Moreover, we have proved that this approach is not only feasible for the cases with a uniform receiver distribution, but also applicable to cases where the passive seismic data are acquired with an irregular spacing geometry of sensors, which makes this approach more practical in reality.

This study involves seismic monitoring of a deep coal mine. The purpose is to examine the processes responsible for induced seismicity. A seismic network consisting of five three-component short-period seismometers located above the mine recorded the seismic data. The events discussed here occurred from March 1, 2009 until April 7, 2011 during the mining of three longwall panels and the data was telemetered to Blacksburg, Virginia. A correlation equation was developed to relate local magnitude estimated by automatic data processing software in near real-time to seismic moment for well-recorded events. Local magnitude is a relative measure of relative size for a suite of earthquakes, while seismic moment is an objective measure of the actual physical size. Using the calculated seismic moments, we calculated "moment magnitudes" (Mw) for all events, which allowed us to do further studies in terms of their absolute size as a function of both time and space. The results indicate that there are two distinct classes of seismic events at the mine. The first class consists of small (M<=0) earthquakes recorded near the moving mine face. The second class of seismicity occurs in the mined-out "gob" area of the longwall panel at a greater distance behind the moving face. Their occurrence and relation to the mining history, depth of overburden and geology of the roof rocks is a significant interest. Results show that thick overburden due to elevated topography has a positive correlation with the number of seismic events but is not the only controlling factor; other factors include gob size and geological variability. Another important observation is the high seismic attenuation of the rock mass above the mine. This appears to be the result of the fracturing and caving processes associated with the creation of the gob and the resulting subsidence of the ground surface. / Master of Science

This thesis explores two ways of developing a fracture diagnostics tool capable of estimating hydraulic fracture propped length and orientation. Both approaches make use of an electrically conductive proppant. The fabrication of an electrically conductive proppant is believed to be possible and an option currently on the market is calcined petroleum coke. The first approach for tool development was based on principles of antenna resonance whereas the second approach was based on low frequency magnetic induction. The former approach had limited success due to the lack of resonant features at the stipulated operating conditions. Low frequency induction is a more promising approach as electromagnetic fields showed measurable changes that were dependent on fracture length in simulations. The operation of a logging tool was simulated and the data showed differences in the magnetic field magnitude ranging from 2% to 107% between fracture sizes of 20m, 50m, 80m, and 100m. Continuing research of the topic should focus not only on simulating more diverse fracture scenarios but also on developing an inversion scheme necessary for interpreting field data. / text

Unconventional reservoirs are typically characterized by very low permeabilities, and thus, the pressure depletion from a producing well may not propagate far from the well during the life of a development. Currently, two approaches are widely utilized to perform unconventional reservoir analysis: analytical techniques, including the decline curve analysis and the pressure/rate transient analysis, and numerical simulation. The numerical simulation can rigorously account for complex well geometry and reservoir heterogeneity but also is time consuming. In this thesis, we propose and apply an efficient technique, fast marching method (FMM), to analyze the shale gas reservoirs. Our proposed approach stands midway between analytic techniques and numerical simulation. In contrast to analytical techniques, it takes into account complex well geometry and reservoir heterogeneity, and it is less time consuming compared to numerical simulation. The fast marching method can efficiently provide us with the solution of the pressure front propagation equation, which can be expressed as an Eikonal equation. Our approach is based on the generalization of the concept of depth of investigation. Its application to unconventional reservoirs can provide the understanding necessary to describe and optimize the interaction between complex multi-stage fractured wells, reservoir heterogeneity, drainage volumes, pressure depletion, and well rates. The proposed method allows rapid approximation of reservoir simulation results without resorting to detailed flow simulation, and also provides the time-evolution of the well drainage volume for visualization. Calibration of reservoir models to match historical dynamic data is necessary to increase confidence in simulation models and also minimize risks in decision making. In this thesis, we propose an integrated workflow: applying the genetic algorithm (GA) to calibrate the model parameters, and utilizing the fast marching based approach for forward simulation. This workflow takes advantages of both the derivative free characteristics of GA and the speed of FMM. In addition, we also provide a novel approach to incorporate the micro-seismic events (if available) into our history matching workflow so as to further constrain and better calibrate our models.

A storage and enhanced coalbed methane (CO2-ECBM) test will store up to 20,000 tons of carbon dioxide in a stacked coal reservoir in southwest Virginia. The test involves two phases of CO2 injection operations. Phase I was conducted from July 2, 2015 to April 15, 2016, and injected a total of 10, 601 tons of CO2. After a reservoir soaking period of seven months, Phase II is scheduled to begin Fall 2016. The design of the monitoring program for the test considered several site-specific factors, including a unique reservoir geometry, challenging surface terrain, simultaneous CBM production activities which complicate the ability to attribute signals to sources. A multi-scale approach to the monitoring design incorporated technologies deployed over different, overlapping spatial and temporal scales selected for the monitoring program include dedicated observation wells, CO2 injection operations monitoring, reservoir pressure and temperature monitoring, gas and formation water composition from offset wells tracer studies, borehole liquid level measurement, microseismic monitoring, surface deformation measurement, and various well logs and tests. Integrated interpretations of monitoring results from Phase I of the test have characterized enhanced permeability, geomechanical variation with depth, and dynamic reservoir injectivity. Results have also led to the development of recommended injection strategy for CO2-ECBM operations. The work presented here describes the development of the monitoring program, including design considerations and rationales for selected technologies, and presents monitoring results and interpretations from Phase I of the test. / Ph. D.