Microseismic Events Research Articles

Study on the Bending Effect and Rock Burst Mechanism of Middle Rock Pillars in Extremely Thick Subvertical Coal Seams

Rock bursts occur in nearly vertical coal seam mines at shallow to moderate burial depths, which endangers safe mining. To study the rock burst mechanisms of nearly vertical and extremely thick coal seams, the characteristics of rock bursts were studied via on-site investigation, and a field test of in situ stress was carried out. The mechanical behavior of rock pillars in the middle of the B1+2 and B3+6 coal seams was analyzed using theoretical and numerical simulation methods. The results show that the horizontal maximum principal stress orientation and the nearly vertical coal seam strike were both 82°. The bending of the rock pillars occurred due to the horizontal unbalanced force, and a large amount of bending energy was accumulated within 50 m above the mining level. Rock pillars were bent toward the B1+2 mining goaf and exerted a reverse bending and squeezing effect on the B3+6 coal seam below the mining levels. In addition to the inclination and compression of the B3+6 coal seam roof, stress concentration zones formed in the B3+6 coal seam, where a large amount of elastic energy had accumulated in the coal-rock mass. Consequently, both the rock pillars and the B3+6 coal body at the mining level are in an unstable state undue to mining disturbance. Rock burst energy theory and numerical calculation results showed that in the stress concentration zones of the B3+6 coal seam, the energy density of the coal mass reached or exceeded its critical value before rock burst occurred, and rock bursts were prone to occur under mining disturbances. The in situ microseismic results showed that high-energy microseismic events were mainly concentrated in middle rock pillars around the mining levels and the coal mass in high-stress concentration zones.

A high-resolution microseismic source location method based on contrast source algorithm

SUMMARY Passive source location estimation technology plays an extremely important role in microseismic monitoring. However, the energy of microseismic signals is generally weak and prone to be interrupted as noise, which will affect the detection and location accuracy of microseismic events. Besides, the existing wave-equation-based source location estimation methods are not suitable for strongly heterogeneous strata, such as faults, salt bodies, fractures filled with proppant and other fluids, etc. These complex geological structures often trigger scattered waves. Thus, based on scattering theory, a high-resolution microseismic source location estimation method with a new grouping imaging condition is proposed by implementing a contrast source numerical simulation method in the process of wavefield backpropagation. First, the contrast source algorithm is used to perform wavefield extrapolation by solving the Helmholtz equation in the frequency domain, which can provide more comprehensive, effective information to get high-precision locations. Moreover, an optimized grouping imaging condition is proposed, which can improve the imaging resolution and computational efficiency. The effectiveness of the method we propose is illustrated using numerical examples. Compared with traditional source location estimation methods, including with low signal-to-noise ratio or multisource location estimation, this method results in strong energy focusing achieved in an effecient manner.

Research on behavior of underground pressure in shallow coal seam with three-face goaf working face

During the mining process of coal mines, coal pillars are often formed in the goaf on three sides, and the stress behavior of coal seams has a significant impact on the safety of coal mines. The upper part of panel 3104 of Xinwang Coal Mine is the room-pillar goaf of the 2# coal seam, and the lower part is the goaf of the 7# coal seam. The same coal seam is adjacent to the goaf of panel 3102. Therefore, there are hidden dangers for safe mining in panel 3104 affected by the goaf on three sides. Through FLAC3D numerical simulation, on-site Pasat-M detection, microseismic monitoring, and support resistance measurement, the stress distribution law of surrounding rock in the working face is analyzed. The following conclusions are obtained. There are several high wave velocity stress concentration areas in the coal body of 3104 working face. During the mining of the working face, the mining influence 90m in front of the working face began to spread to the area of the left coal pillar, and the vertical stress at the left coal pillar began to increase. Stress concentration appears in 0–20m strata at the top of 3# coal seams. At the top of a 3# coal seam, the vertical stress concentration coefficient can reach 2 in the range of 4–12m. The influence range of 3104 working face mining advance can reach 200m. Under the influence of mining, a large number of microseismic events occurred at 160m above and 150m below 3# coal seam. This law has reference value for the mining strata behaviors of coal seams under similar conditions.

Basin-scale multi-decadal analysis of hydraulic fracturing and seismicity in western Canada shows non-recurrence of induced runaway fault rupture

Hydraulic fracturing (HF) is a reservoir stimulation technique that has been widely deployed in recent years to increase the productivity of light oil and/or natural gas from organic-rich, low-permeability formations. Although the process of fracturing a rock typically results in microseismic events of magnitude < 0, many cases of felt seismic events (typically magnitude 3.0 or larger) have also been reported. In the Western Canada Sedimentary Basin (WCSB), where more than 40,000 wells have been drilled and hydraulically fractured in the past two decades, the occurrence of HF-induced events has surged in some areas. Yet, many other areas of the WCSB have not experienced a significant increase in induced seismicity, despite a sharp increase in both the number of HF wells and the volumes of injected fluid. The relationship between injected volume and induced magnitudes can be quantified using the seismic efficiency ratio (SEFF), which describes the ratio between the net seismic moment release and the injected fluid volume. Runaway rupture, in which the fault rupture is dominated by the release of accumulated tectonic stresses, is inferred to be marked by an abrupt increase in SEFF to a value > 0.5. Most previous studies of induced earthquakes have been limited to a single operation and/or seismicity sequence. To better understand the observed variability of the seismic response to HF stimulations at a basin scale, we compiled HF data for all unconventional wells hydraulic fractured in the WCSB between 2000 and 2020, together with the seismicity reported during the same period. We grouped these observations into bins measuring 0.2° in longitude and 0.1° in latitude, or approximately 13 by 11 km. We identified 14 areas where large magnitude events resulted in high SEFF values, implying runaway rupture had taken place. However, we find that in these areas, sustained fluid injection did not lead to persistent high SEFF values. Instead, as injection continued, SEFF values returned to values less than 0.5. This suggests that there is a limited budget of tectonic strain energy available to generate runaway rupture events: once this is released, event magnitudes decrease even if high volume injection persists.

Deformation failure mechanism and concrete-filled steel tubular support control technology of deep high-stress fractured roadway

To address the problem of deep high-stress fractured roadway support, a water sump in a 30 m middle section of the Jinfeng Gold Deposit was taken as an engineering background. The deformation failure characteristics of the roadway under the original support scheme were analysed, and the deformation failure mechanism was assessed by combining the geological conditions, geological mechanism, and original support scheme. Numerical experiments were performed to analyse the bearing performance of concrete-filled steel tubular (CFST) supports and U-shaped steel supports under dynamic and static loads. The experiments showed that with the same lateral pressure coefficient, the stress of CFST support is approximately half of that of U-shaped steel support, and the deformation is decreased by 30–40 %. Meanwhile, under the same charge weight, the stress and deformation of the CFST support are always lower than those of the U-shaped steel support. In engineering practice, the support scheme based on circular-section CFST supports showed no obvious deformation after sixty days of use, and the surrounding rock produced few microseismic events and no significant expansion or concentration of microfractures. The stability of the roadway surrounding rock was efficiently maintained, which offers guiding implications for similar projects.

Head-On Dynamic Manifestations in the Roadway Driving with Small Coal Pillar under the Influence of Roof Drainage: A Case Study from Uxin Banner

In the Uxin Banner area of the Inner Mongolia Autonomous Region, mines that have experienced rock bursts have all been affected by the presence of confined water in the roof strata. Strong mine pressure appears in the working face with about 30 m wide coal pillar in this area, and the research and practice of prevention and control of rock burst by small coal pillar are carried out. Mining-related events linked to the preservation of small coal pillars in the 2-2 coal seam of this region, and studies of the phenomenon, have shown that head-on rock bursts occur during gob-side roadway driving at the edge of the drained area, making it necessary to identify the sources of pressure triggering the events, to determine the related threshold criteria, and to identify methods to reduce the occurrence and magnitude of the rock burst events. This study used the gob-side entry driving in the drained area of the 2202 auxiliary haulage roadway of a mine as a case study. The paper built a structural and computational model for overburdens (including the drained stratum) before mining disturbance, based on the theory for a continuously distributed Winkler elastic foundation. The models used and developed in our study were used to identify, quantify, and describe the changes to stress and the transfer of stress during the drainage process. On the basis of the effects of drainage stress induced by the gob-side entry driving, we outlined a method for ranking the level of rock burst hazard associated with the areas in and adjacent to the drained area. The level of rock burst hazard associated with the entry driving could be arranged in descending order as follows: parallel boundary &gt; vertical boundary &gt; undrained area &gt; drained area. Stress threshold criteria for head-on ejection were also proposed in the paper, namely, (1) the occurrence (or not) of coal ejection during loading and (2) the critical stress for the occurrence of ejection. Field monitoring indicated that microseismic events and energy are mainly concentrated in the driving region parallel to the boundary of the drained area. We concluded that head-on large-diameter borehole destressing would effectively reduce the likelihood of a rock burst occurrence.

The use of weighted self-organizing maps to interrogate large seismic data sets

SUMMARY Modern microseismic monitoring systems can generate extremely large data sets with signals originating from a variety of natural and anthropogenic sources. These data sets may contain multiple signal types that require classification, analysis and interpretation: a considerable task if done manually. Machine learning techniques may be applied to these data sets to expedite and improve such analysis. In this study, we apply an unsupervised technique, the Self-Organizing Map (SOM), to high-volume data recorded by an in-mine microseismic network. This represents a good example of a large seismic data set that contains a wide range of signals, owing to the diversity of source processes occurring within the mine. The signals are quantified by extracting a number of features (temporal and spectral) from the waveforms which are provided as input data for the SOM. We develop and implement a weighted variant of the SOM in which the contributions of various different features to the training of the map are allowed to evolve. The standard and weighted SOMs are applied to the data, and the output maps compared. Both variants are able to separate source types based on the waveform characteristics, allowing for rapid, automatic classification of signals and the ability to find sources with similar waveforms. Fast classification of such signals provides practical benefit by automatically discarding waveforms associated with anthropogenic sources within the mine while seismic signals originating from genuine microseismic events, which constitute a small fraction of all signals, can be prioritized for subsequent processing and analysis. The weighted variant provides an exploratory tool through quantification of the contribution of different features to the clustering process. This helps to optimize the performance of the SOM through the identification of redundant features. Furthermore, those features that are assigned large weights are considered to be more representative of the source generation processes as they contribute more to the cluster separation process. We apply weighted SOMs to data from a mine recorded during two different time periods, corresponding to different stages of the mine development. Changes in feature importance and in the observed distribution of feature values indicate evolving source generation processes and may be used to support investigatory analysis. The weighted SOM therefore represents an effective tool to help manage and investigate large seismic data sets, providing both practical benefit and insight into underlying event mechanisms.

Integration analysis of 3D fractures network reconstruction and frac hits response in shale wells

The frac hits affected a drilling platform in the Changning region, Sichuan Basin, China. The microseismic events of the three wells coincided in the adjacent fracturing stages. When the intermediate well fracturing, the production pressure of two adjacent wells decreases, and gas lift occurs. This study first reconstructed the 3D discrete complex fracture network through microseismic events using the local RANSAC algorithm and quantitatively evaluated the interaction and connectivity of hydraulic fracture networks between wells using kernel density estimation. An integrated multi-physical fields model was established under the dual-porosity/dual permeability concept and embedded discrete fracture models, including poroelastic deformation, desorption/adsorption, Knudsen diffusion, and viscous flow. The finding showed that frac hits during the fracturing operations could change the occurrence frequency, moment magnitude, and spatial distribution of microseismic events. The maximum dip direction of hydraulic fracture changed by 53° because of the pore pressure connection. The stress deflexion induced by the old well exploitation inward deflected between 13° and 45°, which means that the hydraulic fractures preferentially would grow asymmetrically and propagate to the pressure depletion zone. This research integrates geology and engineering and can give direct and practical guidance to oilfield fracturing construction, hydraulic fracture monitoring, and gas production.

CHARACTERIZATION OF FRACTAL-LIKE FRACTURE NETWORK USING TRACER FLOWBACK TESTS FOR A MULTIFRACTURED HORIZONTAL WELL IN A TIGHT FORMATION

Horizontal drilling in combination with hydraulic fracturing has been successfully used to efficiently and effectively exploit tight oil/ gas reservoirs where a multibranched fracture network may be generated near a horizontal well which has been confirmed with microseismic events (MSE). Due to their inherent constraints, limited attempts have been made to characterize such complex fracture networks at an individual fracturing stage using tracer flowback tests. In this work, numerical models have been developed, validated, and applied to describe the tracer flowback behavior and characterize the complex fractal-like discrete fracture networks in a tight formation. To be specific, the embedded discrete fracture model (EDFM) is employed to accurately capture the fracture geometry by first discretizing the fractures into various segments and then incorporating such discretized fractures into the matrix. The perpendicular bisector (PEBI) grids are then generated to flexibly conform to the fractures and reduce the grid orientation effects. Subsequently, a reservoir with complex fracture networks is discretized into two separate domains (i.e. matrix and fracture), while nonneighboring connections (NNCs) which can provide fluid communications between the two domains are applied to couple the matrix and embedded fractures. Furthermore, the tracer flowback profiles, which can reveal the complexity of fractal-like discrete fracture networks, are quantified by considering tracer advection, dispersion, and adsorption. Sensitivity analysis has been conducted to examine the effects of iteration number, branch number, deviation angle, scale factor, and the ratio of permeabilities of two cluster fractures on the tracer flowback concentration curves. It is found that the iteration number will greatly affect the tracer flowback concentration, while the deviation angle imposes a minor effect on tracer flowback behavior. An increase in both the branch number and scale factor will decrease the tracer flowback concentration. Two concentration peaks will appear when the permeabilities of the two cluster fractures are different. The larger the difference between the permeabilities of the two cluster fractures is, the greater the difference in the peak arrival time and the peak amplitude will be. In addition, the newly proposed model was verified and then applied to a field case to characterize the complex fractal-like fracture networks, which are confirmed with the microseismic events.

Research on the Development Law of Pre-Mining Microseisms and Risk Assessment of Floor Water Inrush: A Case Study of the Wutongzhuang Coal Mine in China

Coal-mining areas are widely distributed in Northern China, but are under threat from confined water in the mining operation, resulting in a series of floor water- inrush hazards. Therefore, it is significant to effectively evaluate the floor water inrush to ensure safe and efficient coal mining. The 182602 working face of the Wutongzhuang coal mine served as the background for our research. The concept of “pre-mining microseisms” was proposed, and based on this, microseismic monitoring equipment was arranged on site. The correlation between microseismic events and the water abundance of an aquifer was analyzed, and a floor water inrush evaluation method was constructed based on the three elements of an aquifer and pre-mining microseisms. The main results are as follows: first, the microseismic events were excited by artificial disturbances before the mining of the working face including slurry diffusion and neighboring mining, which had the characteristics of sporadicity, clustering, and periodicity. Second, the regional distribution of water abundance was determined by taking the water inflow, water pressure, and grouting volume as the outward performance characteristics of water abundance in the Shanvuqing aquifer. Furthermore, the correlation coefficient between the pre-mining microseisms and the three elements of the aquifer (water inflow, water pressure, and grouting volume) was larger than 0.7. On this basis, an evaluation method associated with the water inrush risk along the strike of the working face was established based on pre-mining microseisms, dividing the working face into dangerous zones, suspected dangerous zones, and safe zones. Furthermore, pre-mining microseisms, water abundance, and structures were introduced as risk-evaluation indices, and the complete weight was calculated using an analytic hierarchy process and entropy-weight technique, before a vulnerability index model of floor water inrush was built. Finally, targeted treatment procedures were efficiently implemented to ensure the safe mining of working face 182602 due to the successful prediction of potential water risk zones. The research results provide scientific and technological support for pre-mining microseisms combined with water abundance as a technical method to prevent floor water inrush.

Estimation of an ultra-high-temperature geothermal reservoir model in the Kakkonda geothermal field, northeastern Japan

The probable locations of ultra-high-temperature geothermal reservoirs in the Kakkonda geothermal field were modeled under varying conditions using resistivity and temperature data. The results indicate that ultra-high-temperature geothermal reservoirs are likely present at elevations of ≥ –4000 m. The centers of these reservoirs, characterized by low-resistivity and high-permeability values, are approximately in the Kakkonda granite (0.09 ± 0.01 Ma). The area covered by the modeled reservoirs is consistent with estimates from other approaches, such as the microseismic event distribution. This study suggests that an ultra-high-temperature geothermal reservoir is present in the area.

Improvement of microseismic source location during cavern excavation in faulted rock mass using fast marching method

Understanding the temporospatial distribution of microseismic events is critical for the early warning of induced geohazards during deep rock excavation. However, the accuracy of microseismic source location can be influenced by many geological and engineering factors, such as rock discontinuities and excavated openings. This study addresses the application of fast marching method (FMM) to improve the location accuracy in faulted rock masses and takes a case of powerhouse excavation in the Shuangjiangkou hydropower project as an example. The FMM-based method includes five steps, such as inputs, gridding, velocity assignment, calculation, and output, and can be readily processed using a travel time database. Our results exhibit that the wave paths obtained from the FMM-based method are consistent with those from the analytical solution based on the Snell law and better than those from the commonly used uniform velocity model. The results also signify that the FMM-based method can improve the location accuracy for P-wave propagating across a low-velocity layer and an excavated opening. Moreover, this method can be further improved, such as reasonable determination of grid spacing and thorough detection of major discontinuities.

Stability analysis of surrounding rock mass in underground powerhouse considering damage effect of microfractures

A high-precision microseismic (MS) monitoring system was built to monitor surrounding rock microfractures in the underground powerhouse on the left bank of Shuangjiangkou Hydropower Station. The surrounding rock damage area with spatiotemporal clustering of MS activities was studied for qualitative analysis of the damage mechanism of surrounding rock microfractures, based on the source parameters of MS events. The surrounding rock microfracture scale characterized by the source radius of MS events was considered to establish the constitutive relation. MS information was imported into the model for numerical analysis using fast Lagrangian analysis of continuain 3 dimensions (FLAC3D). The results indicated that the numerical simulation results considering MS damage can better reflect the actual situation of the field. The surrounding rock microfractures mainly showed mixed failure characteristics. Shear failures appeared in localized areas while the fracture scale of sections from K0–33 m to K0–15 m on the vault was large. The deformation increment caused by microfracture damage in the shallow surrounding rock of the top arch accounted for 10%–13%, and the stress decrement in the surrounding rock caused by microfracture damage accounted for about 10%.

Microseismic event detection in noisy environments with instantaneous spectral Shannon entropy.

The detection of information-bearing signals in a time series is very important for describing and analyzing a wide variety of complex physical systems. However, identifying events in low signal-to-noise ratio circumstances remains a challenge once heavy data preprocessing is usually required. In this work, we propose a robust methodology based on the instantaneous-spectral Shannon entropy for capturing microseismic events in noisy environments without the requirement of data preprocessing. We call our proposal the instantaneous spectral entropy detection (ISED) method. We tested the ISED in a couple of real empirical seismic records to detect microseismic events. Our methodology detects microseismic patterns even without any preprocessing, in contrast to usual methods in the literature which need appreciable data preprocessing.

Precursor Information Recognition of Rockburst in the Coal-Rock Mass of Meizoseismal Area Based on Multiplex Microseismic Information Fusion and Its Application: A Case Study of Wudong Coal Mine

Abstract In recent years, the rockburst induced by steeply inclined coal seam mining in the Urumqi mining area has become serious. In this paper, the evolution law of multiplex microseismic information before and after the rockburst is obtained through in-depth mining of the field microseismic data. In addition, the evolution characteristics of microseismic activities before and after the rockburst of steeply inclined coal-rock mass in the meizoseismal area are revealed from three important scales: time, space, and strength. The results show the following: (1) The microseismic activity of the Wudong Coal Mine is mainly of stress migration type. The sandwiched rock pillar is the primary inducement of rockburst, and the b value decreases greatly with the mining progresses (by 23.9%). It indicates that the risk of rockburst induced by the local failure of rock mass in this area is increasing. (2) From the time scale and strength index, the precursory indexes of rockburst are put forward, respectively: ① the daily total energy and the frequency of microseisms suddenly rise and fall rapidly at the same time in the shock start-up period (5 days before rockburst), and the daily total energy of microseisms decreases to the abnormal valley value within 30 days. ② The abnormal growth rate of microseismic events exceeded 60% in a certain stage, and “induced shock events” appeared. (3) The shock risk is positively correlated with the decline rate of energy index, the growth rate of cumulative apparent volume, and Schmidt. It is determined that the rockburst will occur within 19 days after entering the shock early warning period. The results of prediction examples show that this method has a good prediction effect on rockburst in strong meizoseismal areas, which can provide a reference for rockburst prevention in the mining process in strong meizoseismal areas.

Detection and Characterization of Microseismic Events from Fiber-Optic DAS Data Using Deep Learning

Abstract Microseismic analysis is a valuable tool for fracture characterization in the Earth’s subsurface. Distributed acoustic sensing (DAS) fibers are deployed at depth inside wells, so they hold vast potential for high-resolution microseismic analysis. However, the accurate detection of microseismic signals in continuous DAS data is challenging and time consuming. We designed, trained, and deployed a deep learning model to detect microseismic events in DAS data automatically. We created a curated dataset of nearly 7000 manually selected events and an equal number of background noise examples. We optimized the deep learning model’s network architecture together with its training hyperparameters by Bayesian optimization. The trained model achieved an accuracy of 98.6% on our benchmark dataset and even detected low-amplitude events missed during manual labeling. Our methodology detected more than 100,000 events, allowing for a far more accurate and efficient reconstruction of spatiotemporal fracture development than would have been feasible by traditional methods.

Research on casing deformation mechanism and prevention measures based on micro-seismic signal distribution

Casing deformation is a severe problem in shale gas well development. The field data show that multiple deformations usually occur in the same casing well, and the distance is close, and the degree of deformation is different. In this paper, firstly, based on the distribution of micro-seismic event points in the Ning M well, the fracture group is inverted, and to establish the finite element numerical model to simulate the multi-stage fracturing, Then, the stress at each point of the casing is analyzed with the fracturing time, the circumferential stress at each point after fracturing and the axial stress of casing, and the displacement variation of casing in the directions of maximum, minimum and vertical principal stress are discussed. Finally, the effect of the intensive fracturing technology on controlling casing deformation is discussed. The results show that: a) Casing stress increases in different amplitude at a local point, circumferential direction, an axial direction, and regional stress difference is formed; b) Some local areas around the wellbore are subjected to twice or more re-fractures, and the corresponding rock properties (such as elastic modulus and Poisson's ratio) will change differently; c) Casing deformations with different directions and displacements appear in some reconstructed areas, showing “S” shape deformation, consistent with the actual deformation situation on site; d) The intensive fracturing technology can alleviate the non-uniformity of fracturing and reduce the non-uniform load of the casing, and effectively reduce the casing deformation rate after field application.

Application of Convolutional Neural Network to GIS and Physics

Human life and property are often seriously threatened and lost due to natural disasters such as earthquakes. As a spatial information system, the geographic information system (GIS) can collect, store, and manage the local or whole related physical data of the surface space to be measured with the support of software and hardware. The physical data is collected through GIS for performance testing. The data are collected from the aftershock event records of the Wenchuan earthquake. Among them, 14,000 Wenchuan earthquake events are used as the original data set, and 8,800 aftershock events are used as the test data set. Seismic data involves the detection of multiple physical quantities, which makes the seismic data gradually increase, many data have no obvious linear relationship, and traditional detection methods are difficult to meet the detection requirements. The artificial intelligence method led by a convolutional neural network (CNN) can perform pattern matching on complex nonlinear variables, and models with general characteristics can be generated from different seismic waveforms for the prediction of seismic waveforms. The results show that GIS can effectively intercept and collect seismic physical signals. The training and detection accuracy of CNN combined with GIS physical data is higher than 90%. Compared with traditional training methods, CNN is obviously superior in detection accuracy and recall rate. At the same time, a large number of microseismic events that are easily missed by manual selection can also be found.

Microseismic Monitoring and Analysis Using Cutting-Edge Technology: A Key Enabler for Reservoir Characterization

Microseismic monitoring is a useful enabler for reservoir characterization without which the information on the effects of reservoir operations such as hydraulic fracturing, enhanced oil recovery, carbon dioxide, or natural gas geological storage would be obscured. This research provides a new breakthrough in the tracking of the reservoir fracture network and characterization by detecting the microseismic events and locating their sources in real-time during reservoir operations. The monitoring was conducted using fiber optic distributed acoustic sensors (DAS) and the data were analyzed by deep learning. The use of DAS for microseismic monitoring is a game changer due to its excellent temporal and spatial resolution as well as cost-effectiveness. The deep learning approach is well-suited to dealing in real-time with the large amounts of data recorded by DAS equipment due to its computational speed. Two convolutional neural network based models were evaluated and the best one was used to detect and locate microseismic events from the DAS recorded field microseismic data from the FORGE project in Milford, United States. The results indicate the capability of deep neural networks to simultaneously detect and locate microseismic events from the raw DAS measurements. The results showed a small percentage error. In addition to the high spatial and temporal resolution, fiber optic cables are durable and can be installed permanently in the field and be used for decades. They are also resistant to high pressure, can withstand considerably high temperature, and therefore can be used even during field operations such as a flooding or hydraulic fracture stimulation. Deep neural networks are very robust; need minimum data pre-processing, can handle large volumes of data, and are able to perform multiple computations in a time- and cost-effective way. Once trained, the network can be easily adopted to new conditions through transfer learning.

Calibration of Complex Discrete Fracture Network Using Microseismic Events and Fracture Propagation Modelling with Seamless Reservoir Production Simulation

Abstract During the unconventional reservoir development, a proper modelling of the underground fracture networks and their effects on production is crucial for reservoir development potential and realistic economic analysis. Conventionally, the complex fracture system formed by hydraulic and natural fractures is extremely difficult to capture, let alone to numerically simulate it. Most importantly, the current best solution can only rely on the knowledge of the natural fractures from the geology and geophysics team and hydraulic fractures from the engineering team. Nevertheless, this solution fails to realize the dynamic stress regime variations when fracturing jobs are done within the horizontal wellbore. In this study, a variety of data source and modelling tools is harnessed to delineate a more realistic and representative discrete fracture network (DFN). The first step is to obtain the original natural fractures already depicted from geological and geophysical information and the statistical information regarding the spatial configurations of this DFN. Next, a new set of natural fractures is generated by an in-house natural fracture generator while preserving the spatial characteristics of the original natural fractures at the same time. Then, a combined DFN of the original natural fracture and newly generated natural fractures is accomplished. This combined DFN is then intensity-calibrated by the given microseismic cloud events, especially focusing on the near-wellbore region. Then, a displacement discontinuity method- (DDM-) based in-house hydraulic fracture propagation model is used to generate hydraulic fractures with complex boundaries, honoring the fracturing job logistics from the engineering team. After this step, an ultimate and highly representative DFN can be achieved. By applying this very novel workflow, DFN characterizations of both a single-well scenario and well-pad (3 wells) scenarios have been highly successful. Statistics such as the cluster-wise hydraulic fracture half-length, height, aperture, and numbers of activated/nonactivated natural fractures can be easily presented. Through the powerful numerical method called the embedded discrete fracture model (EDFM), production simulation and stimulated reservoir volume evaluation can be seamlessly studied. Extents of 3D drainage volumes can also be plotted with ease. Overall, a holistic picture regarding the unconventional reservoir’s underground DFN can be reliably depicted, using the proposed workflow.