hydraulic fracturing insights from microseismic...

16 Oilfield Review

Hydraulic Fracturing Insights from Microseismic Monitoring

Horizontal drilling and hydraulic fracturing revolutionized the exploitation of tight and

unconventional oil and gas reservoirs. Microseismic monitoring provides operators

with crucial information to improve these operations and helps reservoir engineers

with modeling and making decisions on well placement, completion design and

stimulation operations.

Joël Le CalvezRaj Malpani Jian XuHouston, Texas, USA

Jerry StokesMid-Continent Geological, Inc.Fort Worth, Texas

Michael WilliamsCambridge, England

Oilfield Review 28, no. 2 (May 2016).Copyright © 2016 Schlumberger.For help in preparation of this article, thanks to Julian Drew, Perth, Western Australia, Australia; Tony Probert and Ian Bradford, Cambridge, England; and Nancy Zakhour, Callon Petroleum, Houston.CMM, ECLIPSE, Mangrove, MS Recon, NetMod, Petrel, ThruBit, UFM, VISAGE, VSI and VSI-40 are marks of Schlumberger.

Operators producing from unconventional reser-voir plays face many challenges. Fluid flow through unconventional reservoir rocks is lim-ited by matrix permeability, which is generally several orders of magnitude smaller than that of conventional reservoir rocks. Preexisting faults and fracture networks often provide pathways for the flow of hydrocarbons and play an impor-tant role in increasing reservoir drainage vol-umes. Hydraulic fracture stimulation treatments can often connect the wellbore to existing natu-ral fracture networks; however, effective stimu-lation requires knowledge of the distribution of those networks.

Well completion engineers use geomechani-cal and fracture models to plan where to initiate hydraulic fractures and predict their propagation through the reservoir. These models require cali-bration and validation. Microseismic monitoring has proved to be a viable means for calibrating the models and for providing empirical data about the effectiveness of stimulation operations.

Microseismic monitoring is a technique that records and locates microseismic events—col-lectively referred to as microseismicity—which are small bursts of seismic wave energy gener-ated by minute rock movements in response to changes of the in situ stresses and rock volume such as those that occur during fracture stimula-tion operations.1 During these operations, frac-tures are created by injecting fluid at high pressure. These fractures propagate and are then held open using a solid proppant. Mapping the spatial and temporal pattern of these events has proved successful for monitoring the progress of

hydraulic fractures as they advance through and alter a formation.

Engineers may employ several techniques to determine the effectiveness of hydraulic stimula-tion operations.2 For instance, during stimulation operations, microseismic (MS) monitoring and tiltmeter measurements can indicate mechanical changes in the subsurface that occur over a wide area centered on the treatment well.3 Afterward, engineers have used radioactive and chemical tracers, temperature tools and production logs to provide complementary indications of changes in fluid pathways resulting from the stimulation.

Geophysical service companies often acquire MS data, which they interpret and integrate with other measurements to provide oil and gas opera-tors with an understanding of hydraulically induced fracture systems. The primary data used for evaluating MS events are waveform measure-ments acquired from a network of receivers placed either downhole or at the surface. Geoscientists use these data to map the extent and evolution of MS events. These maps provide valuable information related to strain and stress variations in the reservoir and surrounding for-mations and are used to guide stimulation deci-sions during job execution. If MS events indicate undesired fracture growth or fault activation, operators may choose to terminate stage pump-ing early, use diverter technology or skip stimula-tion stages.

Microseismic monitoring also provides infor-mation about the nature of the physical pro-cesses—induced fracturing of the rock or slippage on preexisting fractures—that occur at the

1. Seismic waves convey energy by means of the particle motion of solid materials.

2. For more on fracture diagnostic techniques: Bennett L, Le Calvez J, Sarver DR, Tanner K, Birk WS, Waters G, Drew J, Michaud G, Primiero P, Eisner L, Jones R, Leslie D, Williams MJ, Govenlock J, Klem RC and Tezuka K: “The Source for Hydraulic Fracture Characterization,” Oilfield Review 17, no. 4 (Winter 2005): 42–57.

3. A tiltmeter measures minute rotations—changes of inclination—of the ground in which it is embedded.

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location of the MS sources. Characterization of the population of MS sources helps quantify the magnitudes and directions of stress and displace-ment variations in the affected reservoir volume during the stimulation. To describe the magnitude and direction of the rock movements at each source location, geophysicists process MS wave-form recordings, account for propagation effects, determine the radiation pattern of the acoustic emission and invert for source properties—rock movements and energy released.4 Reservoir engi-neers then combine the space-time evolution of source characteristics with additional informa-tion to determine the state of stress and fluid flow paths in the reservoir. From this information, they make productivity predictions, which help opera-tors develop and manage their reservoirs.

In this article, we review the acquisition, pro-cessing and interpretation of MS monitoring data. Advances in these areas are described, and workflows that integrate MS data into geome-chanical modeling and reduce interpretation uncertainty are presented. A case study from an unconventional reservoir in Arkansas, USA, illus-trates performance trade-offs for surface and downhole acquisition geometries. Case studies from Texas, USA, illustrate how MS monitoring has added value to stimulation operations by helping geoscientists identify fault interactions, fracture growth and stage-to-stage variability of stimulation responses.

Typical Monitoring SystemsMicroseismic monitoring (MSM) is the detec-tion of signals generated by small seismic—or microseismic—events. Engineers began using this technique during hydraulic fracturing in oil and gas operations as early as the 1980s.5 The Cotton Valley Consortium—a research group studying hydraulic fracturing of the Cotton Valley Formation play in Texas and Louisiana, USA—used microseismic monitoring to understand fluid flow in a Cotton Valley reservoir in 1997.6 Operators also successfully applied MSM in evalu-ating fracture stimulations in the Barnett Shale in Texas, which helped improve their understanding of the fracture network development during stim-ulations, avoid geohazards and enhance produc-tion.7 These early MSM operations incorporated arrays of three-component (3C) geophones or accelerometers deployed near reservoir depth in a nearby vertical monitor well (Figure 1).8

Figure 1. Hydraulic fracture monitoring from a vertical well. Multicomponent sensors in a vertical monitoring borehole record microseismic events caused by hydraulic fracturing (top). Event locations determined from data processing allow engineers to monitor the progress of stimulation operations. To acquire high-fidelity seismic data, the VSI versatile seismic imager (bottom) uses three-axis (x, y and z) geophone accelerometers (inset ) that are acoustically isolated from the tool body by isolation springs. The VSI service is mechanically coupled to the casing or formation by a hydraulically powered anchoring arm. The acquisition engineer can test the coupling quality by activating an internal shaker before operations begin. The VSI-40 40-shuttle versatile seismic imager allows up to 40 sensor packages, or shuttles, to be linked together; however, 12 shuttles are typically used in hydraulic fracture monitoring operations.

Oilfield Review MAY 16Microseismic Fig 1ORMAY 16 MCSMC 1

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Extensive hydraulic fracturing of horizontal wells began after 1997 as a result of Mitchell Energy’s successful application of the method in the Barnett Shale; MSM from adjacent horizontal boreholes soon followed. The use of sensor arrays in horizontal wells next led to the evaluation of MSM performance.

The effectiveness of the sensor array geometry depends on the layout of the monitoring and treatment wells. Monitoring from vertical wells close to treatment stages results in improved location accuracy for in-zone and out-of-zone microseismicity. Monitoring from nearby horizon-tal wells often provides coverage along the length of a stimulated lateral well; similar coverage may be unavailable from surface arrays or vertical monitor wells. The recording geometry may require cost trade-offs between monitoring the entire treatment well and detecting MS events that may occur outside the target interval.9 Microseismic events that are detected outside the targeted interval can indicate unintended consequences of the stimulation program such as breaching the reservoir seal or activating exist-ing faults.10

Knowledge of MS measurement accuracy is crucial for understanding the validity of MS data interpretations.11 Survey designers have devel-oped modeling software that predicts the mini-mum detectable event magnitude with respect to the distance of the monitoring array from source locations. The software also outputs estimates of the associated uncertainty for locating and char-acterizing MS events.12 Accuracy of the estimated event hypocenters—3D locations using easting and northing geographic Cartesian coordinates along with the depth of event initiation points—is affected by the monitoring geometry and the accuracy of the velocity model that is used to transform waveform arrival times at recording instruments to distances of the instruments from the events.

The precision of hypocenter estimates depends on the geophone array geometry and data errors, which influence the determination of event arrival time and the direction of arrivals at the receivers.13 During stimulation operations, extraneous, high-amplitude noise sources are numerous. As a conse-quence, the low signal-to-noise ratio (S/N) is one of the greatest challenges in the acquisition and processing of MS data.

Early MSM from a single monitoring well pro-vided valuable information, although it had shortcomings. Microseismic monitoring from single wellbores imposes the requirement that all multicomponent sensors have the same vector

fidelity—accuracy for measuring signal magni-tude and direction—because accurate waveform polarization information is crucial for determin-ing the direction to each event hypocenter.14 In addition, seismic tools must record incoming MS signals with the same spectral fidelity—accu-racy for measuring frequency content—within the typical signal bandwidth of 10 to 1,000 Hz used in these operations. When monitored from single wells during a multistage stimulation, some stages may be too distant from the sensors for reliable event detection or characterization. Sensor geometries along a single linear array are insufficient to determine the source mecha-nisms—size, direction, orientation and duration of 3D rock movements—associated with MS events; thus, microseismic engineers seek to record seismic waveforms from multiple observa-tion points and azimuths.

Valid interpretation of MS data also requires careful signal analysis. The processing of MS data is preceded by the construction and calibration of a model of seismic P-wave and S-wave velocities extending from the planned stimulated volume

of interest to the receiver array. Geophysicists calibrate the velocity model using perforation shot, string shot, checkshot or VSP survey data.15 Analysts originally performed event detection and localization processing using P-wave and S-wave arrival time picks and polarization from 3C MS waveforms.16 Today, event localization algorithms, such as the CMM coalescence microseismic mapping procedure, use an automatic scanning and grid search algorithm that correlates signal traveltimes and waveform polarizations to locate hypocenters.17 Multicomponent waveforms are processed to assess how well the observed tim-ing and polarization of arrival phases across the receiver array match the modeled values associated with potential hypocenter locations in the volume of interest. Arrival time picking can also be performed automatically and then refined manually.

Analysts interpret MS locations to show induced fracture extent—length, height and azimuth. However, stimulations in tight and unconventional reservoirs often produce com-plex, nonplanar hydraulic fracture geometries.

4. The radiation pattern is a description in 3D space of the amplitude and sense of initial motion of P and S wavefronts as they propagate away from the initiation position of a microseismic event. For more on seismic sources and their radiation patterns: Lay T and Wallace TC: Modern Global Seismology. San Diego, California, USA: Academic Press, 1995.

5. For more on the context for microseismic monitoring: Maxwell SC, Rutledge J, Jones R and Fehler M: “Petroleum Reservoir Characterization Using Downhole Microseismic Monitoring,” Geophysics 75, no. 5 (September–October 2010): 75A129–75A137.

6. The Cotton Valley formation is a Cretaceous-age tight sandstone that stretches from Texas to northern Florida, USA. The main play produces mostly natural gas and is located in north Louisiana and northeast Texas. For more on the Cotton Valley Consortium project: Rutledge JT, Phillips WS and Mayerhofer MJ: “Faulting Induced by Forced Fluid Injection and Fluid Flow Forced by Faulting: An Interpretation of Hydraulic-Fracture Microseismicity, Carthage Cotton Valley Gas Field, Texas,” Bulletin of the Seismological Society of America 94, no. 5 (October 2004): 1817–1830.

7. For more on the use of microseismic monitoring in the Barnett Shale: Maxwell S: “Microseismic: Growth Born from Success,” The Leading Edge 29, no. 3 (March 2010): 338–343.

8. Seismic data acquired from three-component (3C) geophones use three orthogonally oriented geophones or accelerometers. The early Schlumberger microseismic acquisition system typically included a VSI tool with eight 3C geophones.

9. For more on the accuracy of hypocenter estimates: Maxwell S and Le Calvez J: “Horizontal vs. Vertical Borehole-Based Microseismic Monitoring: Which is Better?,” paper SPE 131780, presented at the SPE Unconventional Gas Conference, Pittsburgh, Pennsylvania, USA, February 23–25, 2010.

10. Induced seismicity refers to earthquakes that are attributable to human activities, which may alter the local stresses and strains in the Earth’s crust and cause rock movements that generate earthquakes.

11. Maxwell, reference 7.

12. To predict sensor network performance, Schlumberger engineers used the NetMod microseismic survey design and evaluation software. For more on microseismic survey design: Raymer DG and Leslie HD: “Microseismic Network Design—Estimating Event Detection,” presented at the 73rd EAGE Conference and Exhibition, Vienna, Austria, May 23–26, 2011.

13. The hypocenter, or focus, is the point within the Earth at which rupture starts during an earthquake or microseismic event. The point directly above it on the Earth’s surface is the epicenter.

14. For more on vector fidelity: Berg EW, Rykkelid, Woje G and Svendsen Ø: “Vector Fidelity in Ocean Bottom Seismic Systems,” paper OTC 14114, presented at the Offshore Technology Conference, Houston, May 6–9, 2002.

15. In a checkshot survey, seismic specialists measure the traveltime of seismic waves, usually P-waves, from the surface to known receiver depths. A vertical seismic profile (VSP) is a more extensive survey in which geophones are placed at regular, closely spaced positions in the borehole. Both surveys use a seismic source positioned on the surface. Perforation shots and explosive string shots serve as seismic sources in the treatment well, and traveltimes are measured downhole or at the surface. In all cases, the source and receiver locations are known and, from the observed traveltimes, velocity may be calculated.

16. The P-waves used for seismic processing are elastic body waves, or sound waves, in which particles oscillate in the direction the wave propagates. The S-waves are elastic waves in which particles oscillate perpendicular to the direction in which the wave propagates.

17. Drew J, Bennett L, Le Calvez J and Neilson K: “Challenges in Acoustic Emission Detection and Analysis for Hydraulic Fracture Monitoring,” paper presented at the 17th International Acoustic Emission Symposium, Kyoto, Japan, November 9–12, 2004.

Drew J, Leslie D, Armstrong P and Michaud G: “Automated Microseismic Event Detection and Location by Continuous Spatial Mapping,” paper SPE 95513, presented at the SPE Annual Technical Conference and Exhibition, Dallas, October 9–12, 2005.

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Consequently, geophysicists compute the effec-tive stimulated volume (ESV) as a measure of MS activity. The size, shape and extent of the ESV is based on a distribution of event locations and their uncertainties (Figure 2).18 The ESV provides information on the complexity of the hydraulic fracture network. A long, narrow ESV is probably dominated by a single planar through-going frac-ture, whereas a short, wide ESV probably consists of a complex, multibranching fracture network.

Taking the Broad View Engineers may conduct MSM from a single well, multiple wells, grids of shallow wells, surface arrays or networks of surface sensor patches. To meet acquisition goals, they may also combine a variety of designs (Figure 3).19 Typically, ana-lysts employ numerical simulation techniques that account for signal frequency content and attenuation and that make use of source, geol-ogy and noise models. They may use statistical analysis to predict the number of detectable events for given monitoring geometries. Analysts also recognize the importance of accounting for anisotropy in the velocity models. Seismic velocity and attenuation tomography based on crosswell surveys can be used to constrain these models.20 During perforating, data acquired from surface sensors can provide calibrated P-wave traveltimes.21

Monitoring from surface and near-surface positions offers a potentially larger field of view than that from monitoring wells alone, and it eliminates the need for providing dedicated deep monitoring wells.22 Surface monitoring enables long treatment laterals to be monitored along their entire lengths. However, because the S/N is often low, locating and characterizing MS events using data recorded at the surface may be diffi-cult. To overcome low S/N and detection uncer-tainty, survey designers use receiver arrays containing many hundreds to thousands of sen-sors. Data from multiple points can be processed to reduce noise and accentuate the true signal. Aided by recent improvements in signal process-ing, geophysicists can use these monitoring arrays to map microseismic events more completely over extended stimulations than is possible from an array placed in a single monitoring well.

When nearby observation wells are available, downhole monitoring offers proximity to treat-ment well stages and ensures higher S/N than that offered by surface monitoring. Broad bandwidth signals recorded by downhole arrays often retain more high-frequency content than do surface arrays. This high-frequency content is useful for MS event characterization. Recording P-wave and S-wave arrivals downhole using 3C sensors also improves localization accuracy compared with that from surface recordings of P-waves alone.

Monitoring from multiple wells provides observations of the source position from multiple directions and enables more-complete source characterization compared with that from sin-gle-well monitoring. Downhole monitoring requires correct velocity models to reduce event localization uncertainty, and it requires precise well deviation surveys to determine exact receiver positions. The models must also contain accurate values for Qp and Qs, the quality factors related to P-wave and S-wave attenuation during propagation. These factors are used to deter-mine wave amplitudes at the MS hypocenters and reduce uncertainty in the inversion for the source mechanism.23

Modern signal processors use nonlinear mathematical methods for the detection and localization of MS events. In combination with CMM processing, these mathematical methods have the potential to detect and locate weak MS events automatically without prior knowledge of the source mechanism and its radiation pattern.24 Analysts have also extended event localization algorithms to use the full MS waveforms. Early event localization methods used traveltimes and polarizations of the P-wave and S-wave direct arrivals only. However, geoscientists can use waveform synthetics to model the source time functions, the principal features of the waveform time series recorded by each instrument in the

Figure 2. Microseismicity and effective stimulated volume. Located events (circles, color-coded according to time) were generated during the stimulation of a horizontal well (red line) in the Barnett Shale in Denton County, Texas. Analysts built 3D cells in a model of the monitored volume of the reservoir. They counted the number of events that exceeded a predetermined threshold in each cell and calculated the resulting stimulated volume within the cells. The green envelope gives the effective stimulated volume, estimated here at 180 million ft3 [5 million m3]. Yellow and blue disks on the horizontal well mark perforation clusters for stimulation Stages 1 and 2, respectively. Isolated events shown outside the green envelope are not considered hydraulically connected to the stimulated volume. (Adapted from Le Calvez et al, reference 59.)


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sensor arrays. Analysts then extract arrival times of direct, refracted and reflected P-wave and S-wave arrivals. An extension of the CMM method uses these additional arrivals to identify their energy for event detection and characterization.25

Tracking Microseismicity to the SurfaceIn 2011, Schlumberger and an independent operator acquired a comprehensive MS dataset while monitoring hydraulic fracturing operations in the Fayetteville Shale in Arkansas. Completion engineers stimulated two horizontal wells using a zipper fracture method, in which hydraulic fracturing is conducted sequentially in side-by-side wells.26 Concurrently, MS survey engineers conducted a test to assess and quantify the event detection capabilities, accuracy and resolution of surface, near-surface and downhole acquisi-tion systems. Sixteen stimulation stages were

Figure 3. Sensor network deployment options. Sensors for MS monitoring of hydraulic fracturing may be deployed in vertical (1), horizontal (2) or deviated monitoring boreholes. Survey engineers may use a grid of shallow wells (3) containing arrays of multicomponent sensors. On the surface, they may deploy single component or multicomponent geophones in 2D patches or in extensive linear arrays (4). Sensor networks that record MS waveform data over a broad area provide data that can be used to characterize MS

event compression (red ellipsoids) and dilation (blue ellipsoids) radiation patterns (5) and estimate source mechanisms. Schlumberger engineers use the MS Recon high-fidelity microseismic surface acquisition system to acquire MS data at the surface. The system incorporates proprietary geophone accelerometers (6) (inset ), ultralow-noise electronics and a nodal-based wireless acquisition technology (7). (Adapted from Le Calvez et al, reference 19.)


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18. Effective stimulated volume (ESV), also referred to as stimulated reservoir volume, is an estimate of the total rock volume affected by the hydraulic fracture stimulation.

19. For more on survey design: Le Calvez J, Underhill B, Raymer D and Guerra K: “Designing Microseismic Surface, Grid, Shallow and Downhole Surveys,” Expanded Abstracts, 85th SEG Annual International Meeting and Exposition, New Orleans (October 18–23, 2015): 2645–2649.

20. For more on the use of crosswell surveys: Le Calvez J, Marion B, Hogarth L, Kolb C, Hanson-Hedgecock S, Puckett M and Bryans B: “Integration of Multi-Scale, Multi-Domain Datasets to Enhance Microseismic Data Processing and Evaluation,” paper BG08, presented at the 3rd EAGE Workshop on Borehole Geophysics, Athens, April 19–22, 2015.

21. For more on the use of perforation shots to build velocity models: Probert T, Raymer D and Bradford I: “Comparing Near-Surface and Deep-Well Microseismic Data and Methods for Hydraulic Fracture Monitoring,” paper PS07, presented at the 4th EAGE Passive Seismic Workshop, Amsterdam, March 17–20, 2013.

22. For more on surface microseismic monitoring: Duncan PM and Eisner L: “Reservoir Characterization Using Surface Microseismic Monitoring,” Geophysics 75, no. 5 (September–October 2010): 75A139–75A146.

23. For more on uncertainty in microseismic monitoring: Eisner L, Thornton M and Griffin J: “Challenges for Microseismic Monitoring,” Expanded Abstracts, 81st SEG Annual International Meeting and Exhibition, San Antonio, Texas, USA (September 18–23, 2011): 1519–1523.

24. For more on nonlinear processing methods: Özbek A, Probert T, Raymer D and Drew J: “Nonlinear Processing Methods for Detection and Location of Microseismic Events,” paper Tu 06 06, presented at the 75th EAGE Conference and Exhibition, London, June 10–13, 2013.

25. For more on full MS waveform processing: Williams MJ, Le Calvez JH and Gendrin A: “Using Surface and Downhole Data to Drive Developments in Event Detection Algorithms,” Extended Abstracts, 76th EAGE Conference and Exhibition, Amsterdam, June 16–19, 2014.

26. Zipper fracture, or “simul-frac,” is a technique in which two or more parallel wells are drilled, perforated and stimulated via an alternating sequence of stages. This stimulation method results in a high-density network of fractures between the wells that increases production in both wells.

For more on advances in fracturing technology: Rafiee M, Soliman MY and Pirayesh E: “Hydraulic Fracturing Design and Optimization: A Modification to Zipper Frac,” paper SPE 159786, presented at the SPE Eastern Regional Meeting, Lexington, Kentucky, USA, October 3–5, 2012.

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monitored across a reservoir interval at 3,600 ft [1,100 m] TVD.

Survey engineers deployed a wide-aperture borehole seismic array that extended from the reservoir to the surface (Figure 4). This array acquired an MS dataset at the reservoir level as well as data that revealed how signals propagated and how noise levels varied between the reser-voir and the surface. Engineers acquired addi-tional MS data from a deep horizontal well and from five shallow vertical wells, each containing a seismic array. They also recorded MS data using an extensive surface seismic array that consisted of five radial lines fanning and emanating from the treatment wellhead, two parallel lines that crossed the radial lines and three 2D surface patches that were located about 3,000, 5,000 and

Figure 4. Fayetteville Shale MSM operation. The map view (top) shows the sensor network layout for a Fayetteville Shale stimulation. The 4,100-channel surface seismic array consisted of five radial lines (red, Lines 1 through 5) offset and emanating from the treatment wellhead, two crosslines (red, Lines 6 and 7) and three areal 2D patches (green squares). Data were also acquired using sensors deployed in a deep horizontal borehole (yellow), in one deep monitoring well (green) and in five vertical shallow wells (blue circles).The vertical section view (bottom) shows well trajectories used for the MS monitoring. The trajectories of treatment Wells 1H and 2H are shown in gray and yellow, respectively. Well M (green) is shown along with the vertical monitoring wells (blue). (Adapted from Schilke et al, reference 28.)


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8,000 ft [915, 1,520 and 2,440 m] from the treat-ment wellhead. Time synchronization between all recording systems ensured that the same MS events could be identified on all monitoring sys-tems (Figure 5).27

Data from this comprehensive test allowed analysts to compare the effectiveness of near-surface and downhole hydraulic fracture moni-toring. Analysts observed that surface array line segments can mitigate surface-wave noise from a known source, such as treatment wellhead pumps, but are less effective against distributed or moving sources of noise, which may be domi-nant in areas covered by the array.28 Surface patches—2D arrays of closely spaced sensors—can effectively remove noise coming from multi-

ple directions but cannot cover the same distances as can linear arrays.29 Sensor arrays in shallow wells are less sensitive to noise propagat-ing along the surface, but signal processing that discriminates against noise is hampered by the small number of sensors available in these arrays.30 Surface and near-surface array designs may be adapted to known noise conditions but are constrained by land access, environmental effects and cost concerns.

The results of the Fayetteville survey showed that a downhole array could detect MS events from nearby stage treatments better than from other array geometries. But the downhole array suffered reduced sensitivity and increased loca-tion uncertainty for distant stage treatments and events. Surface and near-surface monitoring,

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27. For more on the Fayetteville Shale MS test: Maxwell SC, Raymer D, Williams M and Primiero P: “Tracking Microseismic Signals from the Reservoir to Surface,” The Leading Edge 31, no. 11 (November 2012): 1300–1308.

Peyret O, Drew J, Mack M, Brook K, Maxwell S and Cipolla C: “Subsurface to Surface Microseismic Monitoring for Hydraulic Fracturing,” paper SPE 159670, presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, October 8–10, 2012.

28. For more on surface array performance: Schilke S, Probert T, Bradford I, Özbek A and Robertsson JOA: “Use of Surface Seismic Patches for Hydraulic Fracture Monitoring,” paper We E103 04, presented at the 76th EAGE Conference and Exhibition, Amsterdam, June 16–19, 2014.

although less sensitive to recording deep signals than downhole monitoring, offers more uniform sensitivity over a wider area. Surface patches are easily deployed over wide areas and may ulti-mately become the preferred surface monitoring configuration. However, their successful use will require the recording of sufficient signal and the effective application of noise attenuation meth-ods. The lessons learned in the test provide the planners of future MSM systems with a clearer understanding of the trade-offs involved when specifying acquisition equipment layouts.

Figure 5. Data record from one microseismic event. A microseismic event was detected across the downhole, near-surface and surface sensor arrays during a test in the Fayetteville Shale. The modeled waveforms (top, purple) from the event are shown along with sensor positions (green). Waveforms propagate from the event’s hypocenter, the location of which was estimated from the data. Recorded waveforms from the event are shown from the vertical seismic array (middle left ), the array in the horizontal monitoring well (bottom left ) and the 3C arrays in the five shallow vertical MSM wells (middle right ). Five stacked traces from the vertical component waveforms recorded on the five surface radial lines are also shown (lower right ). (Adapted from Peyret et al, reference 27.)


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29. For more on signal processing approaches applied to surface patch data: Petrochilos N and Drew J: “Noise Reduction on Microseismic Data Acquired Using a Patch Monitoring Configuration: A Fayetteville Formation Example,” Expanded Abstracts, SEG 84th Annual International Meeting and Exposition, Denver (October 26–31, 2014): 2314–2318.

30. The amplitude of seismic surface waves decreases as its position away from the surface increases. For more on noise sources in the Fayetteville Shale test: Drew J, Primiero P, Brook K, Raymer D, Probert T, Kim A and Leslie D: “Microseismic Monitoring Field Test Using Surface, Shallow Grid and Downhole Arrays,” paper SEG 2012 0910, presented at the 82nd SEG Annual International Meeting and Exposition, Las Vegas, Nevada, USA, November 4–9, 2012.

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Making Sense of the Microseismic Cloud Geophysicists studying earthquakes use charac-teristics such as the seismic moment, moment magnitude, stress drop, stress change and source dimensions to describe the physical processes occurring at earthquake hypocenters.31 In earth-quake seismology, a typical earthquake is caused by shear displacement—surface parallel slip—along a preexisting fault plane. Earthquake intensity is related to the seismic moment, MO, which can be determined by measuring the amplitudes of seismic waves generated during the event (Figure 6).32

In 1977, Japanese seismologist Hiroo Kanamori used the relationship between seismic moment and energy to introduce the moment magnitude (Mw) scale. Today, geophysicists in the oil and gas industry are applying earthquake seismology concepts to analyze MS data; moment magnitude is routinely used to characterize the size of MS events. Individual source dimensions such as incremental fracture surface areas and lengths can be estimated for MS events from their waveform spectra and source models.

The distribution of MS source locations pro-vides an indication of the rock volume affected by the hydraulic stimulation. Reservoir engineers initially related, with some success, well produc-tivity to MS activity using the ESV as a measure of the volumetric extent of reservoir stimulation (Figure 7).33 Pinpointing the location of MS events, however, is sometimes insufficient for accurately predicting reservoir performance. This insufficiency may result from inaccurate

estimates of the event density and the actual extent of the ESV.

When determining ESV, geoscientists must take into account the spatial density of intercon-nected fractures within the stimulated volume and their surface area in contact with the reservoir. The set of MS event locations—the microseismic cloud—may include stress-induced events in nonhydraulically connected areas. Thus,

the active volume of microseismicity may be an overestimation of the hydraulically connected vol-ume. To reduce this uncertainty, some analysts consider how many events occur in the neighbor-hood of each event location when computing ESV. For MSM that has limited array coverage, distant low-amplitude events may not be detected. This phenomenon, referred to as monitoring bias, may also reduce the computed ESV.

Figure 6. Seismic moment, energy and magnitude equations. The seismic moment, Mo, of an earthquake source is defined as the product of the shear modulus (µ) of the host rock that the fault cuts through, the average shear displacement (D) along that surface and the affected fault surface area (A). The amplitudes of emitted seismic waves are directly proportional to the seismic moment. Seismologists have also related seismic moment to the seismic energy (Es), which is radiated when a fault slips, resulting in a change in the static shear stress (Δσs) along the fault. The moment magnitude (M w) is computed from Mo and is a logarithmic measure of the energy released during a seismic event. In this equation, Mo is expressed in units of N.m. When expressed in units of dyne.cm, 10.73 is used instead of 6.06; for units of lbf.ft, the constant is 5.97.


Mo μ D A.

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Es = 0.5 (Δσs / μ) Mo.

Figure 7. Eagle Ford Shale effective stimulation. The map view (bottom) shows MS events (colored dots), effective stimulated volume (ESVs, colored opaque envelopes) and production log results (dashed red lines) for the stimulation of a horizontal well (dashed blue line) in the Eagle Ford Shale in Texas. The ESVs were calculated based on the density and magnitude of MS events for each perforated interval. The length of the bisecting red lines from the production data are related to the contribution of individual perforation clusters to the total flow of hydrocarbons. Engineers observed a definite correlation (top) between production contribution from individual perforated intervals (red circles) and the ESV derived from the hydraulic fracture model analysis. For the plotted data, R2 is a linear regression measurement related to the quality of curve fitting. A value of 0.80 indicates a good fit. (Adapted from Inamdar et al, reference 33.)


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Microseismic events are produced when rapid deformation occurs within the reservoir or sur-rounding formations in response to stress changes arising from increased pressure during fracture stimulation operations. The deformation consists of slip of unknown length along failure planes of unknown area and orientation.34 Analysts estimate the seismic moment of indi-vidual events using the amplitudes and frequency content of received seismic waveforms.35 Geophysicists can use the seismic moment to enhance the interpretation of MS data.36 By sum-ming these moment values over time for all events within distinct spatial volumes or grid cells, analysts obtain the cumulative moment as a function of time and space.

Engineers may compare time series of cumu-lative moment and stimulation treatment data to better understand the stimulation process (Figure 8). An increase in cumulative moment over time indicates progressive deformation. Maps of final values of cumulative moment indi-cate the spatial distribution of seismic deforma-tion observed during the stimulation. Because large, detectable events contribute significantly more moment than numerous, small, undetect-able events, cumulative moment provides a mea-sure of stimulation response that is less sensitive to monitoring bias than is an ESV based on event locations alone. Using 3D mapping of seismic moment or cumulative moment provides insight

into fracture behavior during stimulations; such insights can be used to calibrate complex hydrau-lic fracture models.

Borrowing concepts from earthquake seis-mology, analysts use statistical measures such as b-values and D-values to further describe groups of detected MS events.37 The relative frequency of occurrence of earthquakes over a range of magni-tudes is described by b-values. Many more small magnitude events tend to occur than do large ones, and the b-value quantifies this tendency. The statistics of the distances separating earth-quake hypocenters is described by D-values. Populations of events occurring on the same frac-ture and fault planes tend to present characteris-tic distributions of spatial separation.

31. For more on source parameters: Shearer PM: Introduction to Seismology, 2nd ed. Cambridge, England: Cambridge University Press, 2009.

32. Seismic moments range from 105 N.m [105 lbf.ft] in the case of the smallest detectable microearthquakes to 1023 N.m [1023 lbf.ft] in the case of great earthquakes.

33. For more on ESV versus reservoir production: Inamdar A, Malpani R, Atwood K, Brook K, Erwemi A, Ogundare T and Purcell D: “Evaluation of Stimulation Techniques Using Microseismic Mapping in the Eagle Ford Shale,” paper SPE 136873, presented at

Figure 8. Cumulative seismic moment. Engineers plotted cumulative seismic moment (black) along with the MS event rate (gray vertical bars) and pumping parameters to help them understand fracture stimulation job performance during the treatment of a well in the Eagle Ford Shale. The pump rate (blue), surface pressure (red) and proppant concentration (green) are shown. Analysts use these plots to identify the time-dependent response of MS events to the stimulation. An abrupt increase in cumulative seismic moment indicated that deformation increased significantly about halfway through the planned pumping schedule. By comparing multiple treatments, engineers can determine how microseismicity changes in response to adjustments to the pumping schedule and whether it is consistent across stages. (Adapted from Downie et al, reference 34.)


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34. Downie R, Xu J, Grant D, Malpani R and Viswanathan A: “Utilization of Microseismic Event Source Parameters for the Calibration of Complex Hydraulic Fracture Models,” paper SPE 163873, presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, February 4–6, 2013.

35. Seismic sources are formally treated as “displacement discontinuities” to describe the difference in motion of material on opposing faces of fracture surfaces. This motion need not be parallel to the surfaces.

36. Analysts use source models, formation material properties and measured frequency spectra to estimate the fracture surface area of individual events. Slip lengths can then be inferred from seismic moment estimates.

37. For more on b-values and D-values: Grob M and van der Baan M: “Inferring In-Situ Stress Changes by Statistical Analysis of Microseismic Event Characteristics,” The Leading Edge 30, no. 11 (November 2011): 1296–1301.

26 Oilfield Review

In seismology, the Gutenberg-Richter empiri-cal law relates the magnitude of earthquakes, M, to their frequency of occurrence, N (Figure 9). In microseismic studies, geoscientists have substi-tuted moment magnitude, Mw, in this relation and have explored how hydraulic fracture param-eters affect the values of a and b, the slope and

intercept, respectively, of the log10 N versus Mw relationship. Some evidence suggests a possible relationship between the value of a—the num-ber of events at the intercept at Mw equaling 1—and the pump rates used in hydraulic fracturing, for example, when the cumulative volume of pumped fluid influences the microseismic event

rate.38 Global seismology data often show the slope b to be about 1 for tectonic earthquakes. In some geologic settings, MS interpreters use b-values to distinguish failure along naturally occurring faults from that along hydraulically induced fractures.

Determination of b-values may also provide completion engineers with an indication of stress changes over the course of multistage stimula-tions. During hydraulic fracturing treatments, b-values greater than 1 are typically observed, whereas b at about 1 has been observed dur-ing MS episodes dominated by movement along faults.39 Scientists have observed relationships between the b-value and local stress conditions.40 Some studies of MS data have shown variations in b-values within regional shale formations being stimulated.41 Other studies have shown that b-values may be time dependent and vary as stress changes throughout the stimulation process.42

Seismologists use D-values to convey the spa-tial statistics of earthquake hypocenter occur-rence. Computed from event locations, D-values may be used to summarize interevent distance statistics. If the cloud of events maps onto a point, D is expected to equal 0. A D-value of 1 is expected if the geometric distribution is linear, 2 if planar and 3 if dispersed.43 The distribution of MS hypocenters has the potential to reveal the location of interconnected fracture surfaces, and analysts have developed a variety of techniques to extract linear and planar features from the microseismic cloud.44

In one method, D-values were computed sepa-rately around each detected MS event location (Figure 10). Closely spaced events are more likely to show some random scatter due to processing effects (in which D equals approximately 3). Events occurring on the same fracture and fault plane will tend to align (in which D equals approx-imately 2). Interpreters identified linear and pla-nar structures in the data by selecting only events for which the D-value is less than or equal to 2. Analysts have used changes in both b-values and D-values to infer stress changes in the reservoir.

Applying additional concepts from earth-quake seismology, geophysicists measure P-wave and S-wave amplitudes across broad receiver net-works to determine radiation patterns and then invert them to estimate seismic moment tensors, which describe the orientation, magnitude and slip of individual MS events.45 For MSM, geophysi-cists use moment tensor inversion (MTI), which is an advanced seismic processing technique, to provide information about the mechanism of fail-ure at fracture sites.46 They then decompose each moment tensor into its constituents to estimate

Figure 9. Plot of b-values. In seismology, the Gutenberg-Richter empirical law relates the magnitude (M ) of earthquakes to their frequency of occurrence, N, where N is the number of events of magnitude M or greater, and a and b are constants. Events observed during a stimulation stage in the Barnett Shale in Denton County, Texas (red dots), show that the cumulative frequency is equal to N divided by 336, the total number of detected events. A statistical method has been used to estimate b, which accounts for the limited ability to detect low-magnitude events. Because data from small magnitude events cannot be recorded reliably, events that have magnitudes smaller than the magnitude of completeness, Mc (dashed line), are not used in the calculation because the S/N is too low. (Adapted from Williams et al, reference 61.)


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38. Shapiro SA, Dinske C, Langenbruch C and Wenzel F: “Seismogenic Index and Magnitude Probability of Earthquakes Induced During Reservoir Fluid Stimulations,” The Leading Edge 29, no. 3 (March 2010): 304–309.

39. Cipolla C, Maxwell S and Mack M: “Engineering Guide to the Application of Microseismic Interpretations,” paper SPE 152165, presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, February 6–8, 2012.

40. Schorlemmer D, Wiemer S and Wyss M: “Variations in Earthquake-Size Distribution Across Different Stress Regimes,” Nature 437 (September 22, 2005): 539–542.

Downie RC, Kronenberger E and Maxwell SC: “Using Microseismic Source Parameters to Evaluate the Influence of Faults on Fracture Treatments—A Geophysical Approach to Interpretation,” paper SPE 134772, presented at the SPE Annual Technical Conference and Exhibition, Florence, Italy, September 19–22, 2010.

41. Boroumand N: “Hydraulic Fracture b -Value from Microseismic Events in Different Regions,” presented at the GeoConvention 2014, Calgary, May 12–16, 2014.

42. Zorn EV, Hammack R and Harbert W: “Time Dependent b and D-values, Scalar Hydraulic Diffusivity, and Seismic Energy From Microseismic Analysis in the Marcellus Shale: Connection to Pumping Behavior During Hydraulic Fracturing,” paper SPE 168647, presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, February 4–6, 2014.

43. Grob and van der Baan, reference 37.44. Williams MJ, Khadhraoui B and Bradford I: “Quantitative

Interpretation of Major Planes from Microseismic Event Locations with Application in Production Prediction,” Expanded Abstracts, 80th SEG Annual International Meeting and Exposition, Denver (October 17–22, 2010): 2085–2089.

the relative proportion of each failure mode—such as shear slip, tensile opening, expansion or other process or a combination of them—and the orientation of local fracture planes and the direc-tion of shear slip. Recently, mathematicians have developed theoretical extensions of MTI in terms of potency tensors; such extensions display unique fracture planes and displacement vectors (Figure 11).47

Although the deformation represented by MS signals constitutes a small fraction of the total deformation and fracture volume created dur-ing stimulation, MTI processing holds promise to provide insights into natural fracture characteris-tics and local stress fields. Geophysicists extract planar features for input to construct discrete fracture networks (DFNs), which represent the distribution, orientation, shape, connectedness and fluid flow properties of a population of frac-tures. The MTI results may provide constraints to help build these networks and calibrate hydraulic fracture models.48

Integrating MS Data and Geomechanical Modeling Engineers integrate MS data with geologic mod-els, mechanical earth models (MEMs), formation imaging logs and production logs to characterize reservoirs and aid their understanding of micro-seismicity. In the past, engineers predicted future reservoir production based on correlations between poststimulation production and ESV. Analysts now use geomechanical modeling to enhance the forecasting.

Geomechanics can aid in the design of hydraulic stimulations to maximize the hydraulic fracture surface area exposed to the reservoir and to the system of natural fractures within. For planning wells and determining in situ stress states, engineers perform stress simulations using static and time-lapsed 3D MEMs that are integrated with results from reservoir simulation models.49 Geomechanical modeling can provide insight into the extent of fracture-to-fracture interference between fracture stages in a treat-ment well, nearby treated wells and the natural fracture system.

Knowledge of the regional stress state and the characteristics and distribution of natural frac-tures in the reservoir is important for predicting the effectiveness of reservoir stimulations. During stimulation operations, hydraulic frac-tures interact with preexisting natural fractures. Slip along natural fractures generally increases the permeability of the stimulated fractures. The density and orientation of the natural fracture population are significant factors that influence

Figure 11. Source mechanism expansion, opening and slip. The estimated moment tensor for each event detected during a well stimulation stage (top) has been decomposed into expansion, opening and slip components and displayed as glyphs. For reference, the well (magenta) is shown with perforation clusters (red disks). A glyph is composed of two disks and a wireframe sphere superimposed over them (bottom). The wireframe sphere represents expansion if red or contraction if blue. The thickness of the disks represents opening, and their relative displacement represents the degree of slip. The glyph’s central plane, which is parallel to the disks’ planar surfaces, is oriented with respect to the strike and dip of the fracture plane activated or caused by the event. (Adapted from Leaney et al, reference 47.)


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45. By analyzing the amplitudes of waveforms received at an array of recording sensors, geophysicists can determine the location, shape, size and orientation of the motions of the causative event. Geophysicists then use the amplitude data to invert for the moment tensor, a system of point-force couples, which is the best-fit seismic radiation pattern equivalent to that observed from seismic event displacement discontinuities. For more on radiation patterns and moment tensors: Lay and Wallace, reference 4.

46. Moment tensor inversion (MTI) is now integrated with the Petrel software platform and Mangrove reservoir-centric stimulation design software workflows.

47. For more on the theory, decomposition and display of moment tensors: Leaney S, Chapman C and Yu X: “Anisotropic Moment Tensor Inversion, Decomposition and Visualization,” Expanded Abstracts, 84th SEG Annual International Meeting and Exposition, Denver (October 26–31, 2014): 2250–2255.

48. For more on deriving DFN from MS data: Yu X, Rutledge JT, Leaney SW and Maxwell S: “Discrete-Fracture-Network Generation from Microseismic Data by Use of Moment-Tensor- and Event-Location-Constrained Hough Transforms,” paper SPE 168582, presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, February 4–6, 2014.

49. Schlumberger reservoir engineers couple ECLIPSE 3D simulations with the VISAGE finite-element geomechanics simulator to create dynamic, time-lapse models of stress and production history of single and multiple wells and fields. For more on integrated modeling: Alexander T, Baihly J, Boyer C, Clark B, Waters G, Jochen V, Le Calvez J, Lewis R, Miller CK, Thaeler J and Toelle BE: “Shale Gas Revolution,” Oilfield Review 23, no. 3 (Autumn 2011): 40–55.

28 Oilfield Review

the stimulated fracture network development and control reservoir productivity.50

Hydraulic fracturing creates a tensile frac-ture that opens slowly, and most of the rock deformation occurs aseismically at much lower frequencies than the typical MS signal band.51 Shear deformation occurs in the process zone around the fracture tip, in the vicinity of the frac-ture face as a result of leakoff into preexisting natural fractures and at doglegs and other geo-metric deflections. In contrast to the aseismic nature of tensile dilation, shear deformation often emits sudden, high-frequency, audible seis-mic energy.

Reservoir engineers have developed a variety of approaches to characterize poststimulation fracture networks (Figure 12).52 In one approach, analysts use information from seismic reflection surveys, well logs and cores to build a DFN, which they combine with a set of earth models to describe the reservoir and surrounding forma-tions.53 Fracture information may often be derived from resistivity or ultrasonic image logs (Figure 13). Hydraulic fracture models such as

the UFM unconventional fracture model can then be used to predict the fracture geometries that result from the stimulation.54

Modelers can use treatment data such as pump pressure, fluid volumes and proppant loadings as inputs to the numerical calculations and then use MS data to constrain the results. These UFM simu-lations yield predictions of stimulated fracture geometry and conductivity distributions. Analysts iteratively calibrate the model by adjusting the input parameters to the UFM simulation to achieve a match between fracture geometry predictions and observed MS event locations and deformation, or seismic moments (Figure 14). Adjustable parameters include horizontal stresses and prop-erties of the DFN and fracturing fluid. Analysts use volumes of fluids pumped and stresses to constrain the fracture model to reduce the set of possible solutions. They conduct sensitivity analyses to determine how much the answer changes as input parameters are varied and to ensure that non-unique multiple solutions give results that are reasonable in terms of predicted production.

Reconciling the UFM modeling results with the MS event patterns requires the evaluation of multiple DFN realizations and may not result in an exact match but rather a statistically probable match to the MS pattern. After the UFM simula-tions are calibrated, the fluid flow characteristics predicted by the UFM technique can be incorpo-rated in a reservoir simulation. In this process, the hydraulic fractures are explicitly gridded in the reservoir model to honor the 3D hydraulic fracture geometry and proppant distribution.

In addition to the reservoir grid, a fluid model, a set of relative permeabilities, stress-dependent hydraulic fracture conductivity profiles, histori-cal production rates and bottomhole pressure are input into the reservoir simulator. After the res-ervoir models are calibrated, analysts can use them to forecast hydrocarbon recovery and per-form sensitivity analyses. The analysts can vary completion parameters, including the number of stimulation stages, the number of perforation clusters per stage and reservoir parameters such as permeability, porosity and saturations to maxi-mize future stimulation operations.

50. For more on fracture network development: Johri M and Zoback MD: “The Evolution of Stimulated Reservoir Volume During Hydraulic Stimulation of Shale Gas Formations,” paper SPE 168701/URTeC 1575434, presented at the Unconventional Resources Technology Conference, Denver, August 12–14, 2013.

51. Maxwell SC and Cipolla C: “What Does Microseismic Tell Us About Hydraulic Fracturing?,“ paper SPE 146932, presented at the SPE Annual Technical Conference and Exhibition, Denver, October 30–November 2, 2011.

52. For more on an early, computationally efficient geomechanical modeling approach: Xu W, Le Calvez J and Thiercelin M: “Characterization of Hydraulically-Induced Fracture Network Using Treatment and Microseismic Data on a Tight-Gas Formation: A Geomechanical Approach,” paper SPE 125237, presented at the SPE Tight Gas Completions Conference, San Antonio, Texas, June 15–17, 2009.

53. For more on the construction of DFNs: Will R, Archer R and Dershowitz B: “Integration of Seismic Anisotropy and Reservoir Performance Data for Characterization of Naturally Fractured Reservoirs Using Discrete Feature Network Models,” paper SPE 84412, presented at the SPE Annual Technical Conference and Exhibition, Denver, October 5–8, 2003.

Offenberger R, Ball N, Kanneganti K and Oussoltsev D: “Integration of Natural and Hydraulic Fracture Network Modeling with Reservoir Simulation for an Eagle Ford Well,” paper SPE 168683/URTeC 1563066, presented at the Unconventional Resources Technology Conference, Denver, August 12–14, 2013.

54. UFM processing is embedded in the Mangrove software platform and uses the output of the VISAGE simulator. For more on UFM processing: Weng X, Kresse O, Cohen C, Wu R and Gu H: “Modeling of Hydraulic-Fracture-Network Propagation in a Naturally Fractured Formation,” SPE Production and Operations 26, no. 4 (November 2011): 368–380.

For more on UFM modeling: Cipolla C, Weng X, Mack M, Ganguly U, Gu H, Kresse O and Cohen C: “Integrating Microseismic Mapping and Complex Fracture Modeling to Characterize Fracture Complexity,” paper SPE 140185, presented at the SPE Hydraulic Fracturing Technology Conference and Exhibition, The Woodlands, Texas, January 24–26, 2011.

Figure 12. Workflow for completion and stimulation design and field development applications of microseismic mapping. Using seismic data, well logs and core data along with treatment and production data (left ), engineers build a series of earth models and discrete fracture network (DFN) models (middle). They use these models to generate hydraulic fracture predictions for the fracture geometry and conductivity distribution resulting from stimulation operations. They also use MS event locations and deformation to calibrate the earth and fracture models. Reservoir engineers are then able to predict fracture network geometry and conductivity and generate reservoir performance simulations (right ). (Adapted from Cipolla et al, reference 39.)

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Figure 14. Flowchart for calibrating UFM processing and DFN simulations. Analysts build a DFN model (top right, light gray lines) based on geologic, geophysical, well log and core data. Preexisting discrete fractures directly affect the hydraulic fracture system. The UFM simulations predict fracture geometry (middle right, heavy blue lines) based on treatment parameters and an earth model that includes the estimated stress field. The stress field can be calculated with a 3D geomechanical simulator using wellbore measurements as calibration points. Analysts compared the fracture geometry predicted from UFM processing with maps of the observed MS event pattern (bottom left, red dots), taking into consideration the deformation represented by the seismic moment obtained from MS monitoring. The engineers then executed an iterative calibration loop, adjusting UFM processing inputs for multiple DFN realizations (bottom right ) to arrive at the best overall agreement between the modeled fracture geometries and the observed deformations. (Adapted from Cipolla et al, reference 54.)


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Figure 13. Construction of a discrete fracture network. For a fracture stimulation in the Eagle Ford Shale, analysts processed logging data obtained from a well drilled with oil-base mud using an imaging tool and were able to detect fractures and determine their orientations from a rose plot (top). The red dots indicate dip azimuth and inclination angle of the poles of the primary fracture planes; blue dots are dip azimuth and inclination angle of the poles of the secondary fracture planes. From the inside to the outside edge of the rose plot, the dip varies from 0° to 90°. The black lines represent fracture strike orientation and the length is related to the abundance along a given direction. Analysts identified a primary and a secondary fracture set. The intensity of fracture occurrence along the wellbore correlated with formation curvature determined using reflection seismic surveys. Co-kriged—a geostatistical technique for data interpolation—fracture intensity and curvature were used to populate the 3D volume of interest with fractures and build the DFN (bottom). The SW–NE trending band of fracture intensity corresponds to an area of high formation curvature. The well trajectory (blue line) is superimposed on the DFN. (Adapted from Offenberger et al, reference 53.)


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30 Oilfield Review

Engineered CompletionsThe Eagle Ford Formation, an upper Cretaceous marl in South Texas, is a target for oil and gas development. Because the formation is highly laminated and has ultralow permeability, effec-tive completion designs are required to maximize production. In September 2013, an operator tested MSM as a method for guiding hydraulic fracture operations and evaluating fault interac-tions during stimulation.

The operator drilled two horizontal lateral wells in the gas-condensate window of the Eagle Ford Shale trend in Karnes County, Texas. These wells, which were drilled parallel to each other and approximately 330 ft [100 m] apart, crossed a major fault. After the operator drilled the wells, measurements of petrophysical and geomechani-cal properties were acquired using the ThruBit through-the-bit logging services.55 Using the Mangrove engineered stimulation design module in the Petrel platform, engineers developed a completion design for one of the two laterals.56

Figure 15. Vertical section view of MS events recorded during the stimulation of two horizontal wells in the Eagle Ford Shale. Microseismic event hypocenters (spheres, color-coded by stage number) and the trajectories of Wells A (orange), B (blue) and C (yellow) are shown in conjunction with the formation tops (horizontal tan and light blue) and faults (vertical gray and green). Treatment Wells A and B were stimulated in a 21-stage zipper fracture operation. Engineers used a 12-level geophone array positioned in the vertical portion of Well C to monitor 16 of the 21 stages

of the stimulation operation. Microseismic events were largely confined to the Eagle Ford Formation, bounded above by the Austin Chalk and below by the Buda Limestone. During stimulation Stage 8 (dark blue) of Well A, the MS events aligned with the adjacent major fault and propagated downward into the Buda Limestone. This observation suggested the fault affected fracture growth during the stimulation. A decision was made to abort Stages 9 (teal) and 10 (crimson) and proceed with Stage 11 (orange) on the other side of the fault. (Adapted from Zakhour et al, reference 58.)


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Stimulation design engineers used reservoir and completion quality parameters derived from the Mangrove software to aid in optimizing stage intervals and the placement of perforations along the lateral.57 Flow measurements in some previ-ous wells that had geometric—evenly spaced—completions had shown unequal contribution to production across perforations. The engineered stages grouped perforation clusters in regions of the lateral that had similar horizontal stress. Completion engineers anticipated that all perfo-ration clusters in a stage would break down and initiate fractures simultaneously because each cluster had similar stress characteristics. Measurements made during stimulation con-firmed that lower than average treating pres-sures were required for the engineered completion than had been used for the lateral that was stimulated using a geometric model.

Survey engineers acquired MS data during the stimulation using a 12-receiver downhole array in a nearby vertical well. Using these MS data, the operator monitored hydraulic fracture development near a fault system, identified fault interaction and adjusted the completion design to avoid the fault. Engineers later studied the mechanisms of fault interaction through postjob integration of MS data with treatment data.

Completion designers normally try to avoid the interaction of hydraulic fractures with large faults. Avoiding faults can prevent the loss of treatment fluid and proppant to thief zones along the fault. In this project, the completion design excluded stimulation stages that were within 250 ft [76 m] of the identified major fault.

Monitoring revealed that MS events were generally well bounded within the target Eagle Ford Formation and the overlying Austin Chalk (Figure 15). However, for some stages, MS activ-ity and treating pressure records indicated unex-pected fracture bridging and potential proppant screenout. Analysis of MS event clusters and b-values alerted the engineers that the hydrau-lic fractures had encountered the nearby fault, which blocked and limited fracture develop-ment and led to premature stage terminations. Real-time interpretation allowed modification of the completion strategy, and several stimulation stages planned near the fault were abandoned. Recommendations were made for future comple-tion designs to increase buffer zones from 250 ft to about 400 ft [120 m] on either side of major faults to minimize the risk of pumping nonpro-ductive stages.58

Maximizing RecoveryIn 2011, engineers and geoscientists with Teleo Operating, LLC and Eagleridge Energy, LLC con-ducted a multistage, multilateral stimulation in the Barnett Shale in Denton County, Texas. The operators drilled three parallel horizontal wells into the lower Barnett Shale. Well trajectories were about 500 ft [150 m] apart; the central well was landed about 80 ft [25 m] shallower than the outside laterals. Because of lease boundary con-straints, the wells had to be placed in the vicinity of several large faults. The lateral sections of the wells were drilled away from the major fault and through a smaller fault (Figure 16). Fault throws

55. For more on ThruBit services: Aivalis J, Meszaros T, Porter R, Reischman R, Ridley R, Wells P, Crouch BW, Reid TL and Simpson GA: “Logging Through the Bit,” Oilfield Review 24, no. 2 (Summer 2012): 44–53.

56. For more on the Mangrove service: Ajayi B, Aso II, Terry IJ Jr, Walker K, Wutherich K, Caplan J, Gerdom DW, Clark BD, Ganguly U, Li X, Xu Y, Yang H, Liu H, Luo Y and Waters G: “Stimulation Design for Unconventional Resources,” Oilfield Review 25, no. 2 (Summer 2013): 34–46.

57. For more on determining reservoir and completion quality: Slocombe R, Acock A, Fisher K, Viswanathan A,

varied from 20 to 100 ft [7 to 30 m]. Engineers used a zipper fracture stimulation on the central Well 1H and eastern-most lateral 3H. The west-ern-most lateral 2H was stimulated later.

Microseismic survey engineers monitored stimulations on Wells 1H and 3H using a 3C-accelerometer array that was placed in hori-zontal Well 2H using a tractor and repositioned according to the well and stage to be monitored. Operations on Well 2H were later monitored using an array deployed in a vertical section below the 3H wellhead. During monitoring, engi-neers observed MS activity across all pumped stages (Figure 17).

Figure 17. Microseismic events detected during stimulation of three horizontal wells in the Barnett Shale. Fault traces (cyan) are mapped at the depth of laterals 1H (red), 2H (green) and 3H (yellow). The zipper fracturing performed on Wells 1H and 3H was monitored from Well 2H. Engineers also monitored four stimulation stages performed on Well 2H using sensors in Well 3H. Color coding and symbols are used to represent stages and wells. Microseismicity for stages closer to the fault system tended to be compact, an observation that was explained later by fracture modeling as stimulation-fault interaction. Longer hydraulic fracture wings occurred in the stages that were executed closer to the toe of the well than those executed close to the heel. Microseismicity overlap observed between successive stages indicates insufficient fracture isolation. (Adapted from Le Calvez et al, reference 59.)


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Chadwick C, Reischman R and Wigger E: “Eagle Ford Completion Optimization Using Horizontal Log Data,” paper SPE 166242, presented at the SPE Annual Technical Conference and Exhibition, New Orleans, September 30–October 2, 2013.

58. For more on horizontal completion optimization across a major fault: Zakhour N, Sunwall M, Benavidez R, Hogarth L and Xu J: “Real-Time Use of Microseismic Monitoring for Horizontal Completion Optimization Across a Major Fault in the Eagle Ford Formation,” paper SPE 173353, presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, February 3–5, 2015.

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During zipper fracturing of Wells 1H and 3H, interpretation of MS event location trends indi-cated the hydraulic fracture azimuth, N50–55°E, was consistent with the expected maximum hori-zontal stress direction; however, the extent of the microseismicity varied from stage to stage. The MS locations for the early stages, near the toe of the wells, extended farther from the wellbore than those observed during later stages, toward the heel of the wells. These later stages were closer to the main fault and displayed a more compact microseismicity pattern and shorter fracture wings away from the wellbore.

The MS locations observed in these later stages overlapped with those observed in some early stages. Several later stages near the heels of the wells displayed microseismicity that was out

of the target depth interval. Observations revealed downward fracture growth and alerted the operator to stop pumping to avoid fracturing into the water-bearing Viola Limestone below the zone of interest. During another stage, engineers recognized that the planar alignment of MS events indicated slip along a fault and were able to stop pumping for that stage and bypass a faulted zone before resuming stimulation. Microseismic monitoring allowed the operator to modify the stimulation program during the ongo-ing job operation.59

Postsurvey modeling and data integration provided a more complete explanation of the stimulation and MS responses.60 Analysts con-structed a complete history of the treatment

using a range of analytical techniques and incor-porated summary statistics of MS event attri-butes within the workflow. Event attributes included seismic moment and moment magni-tude. From these parameters, analysts deter-mined summary b-value statistics and inferred relative stress magnitudes for each stimulation stage.61 They used D-value estimates to extract fracture planes from the clouds of microseismic events—D-values near two indicated planar alignments—and then used the planes for DFN construction. Monitoring geometries that can provide full MTI offer an additional means to con-strain DFN construction and modeling but were not available in this study. Additional input to the analysis included earth model parameters such as the rock mechanical properties of layers and natural fracture geometries along with stimula-tion data such as well geometry, flow rates, fluid types and surface pumping pressures.

Engineers combined the Mangrove, UFM and VISAGE software to model the hydraulic fractur-ing process, the interaction of induced and natu-ral fractures and the stress field.62 These models predicted the chronological development of the interconnected fracture network and were cali-brated iteratively using the observed evolution of MS activity. They tested fracture propagation sce-narios by matching time-distance relationships within the microseismicity pattern and then iteratively improved the interpretation by updat-ing the location and properties of the natural fractures. Engineers constrained the simulation using material balance, which reconciled the fracture volume opened during stimulation with the volumes of pumped fluid and proppant and the volume of fluid estimated to have leaked off into the formation. The simulation provided a description of proppant placement together with a prediction of which natural fractures might

59. Le Calvez J, Xu W, Williams M, Stokes J, Moros H, Maxwell S and Conners S: “Unconventional Approaches for an Unconventional Faulted Reservoir—From Target Selection to Post-Stimulation Analysis,” paper P336, presented at the 73rd EAGE Conference and Exhibition, Vienna, Austria (May 23–26, 2011).

60. Williams MJ, Le Calvez JH and Stokes J: “Towards Self-Consistent Microseismic-Based Interpretation of Hydraulic Stimulation,” paper Th 01 15, presented at the 75th EAGE Conference and Exhibition, London, June 10–13, 2013.

61. Williams MJ, Le Calvez JH, Conners S, Xu W: “Integrated Microseismic and Geomechanical Study in the Barnett Shale Formation,” Geophysics 81, no. 3 (May–June 2016): 1–13.

62. In the Barnett Shale case study, Schlumberger engineers used Mangrove software with the UFM complex fracture simulator to model fracture interaction and the VISAGE simulator to model stress.

63. For more on modeling of faulted rock masses: Pande GN, Beer G and Williams JR: Numerical Methods in Rock Mechanics. Chichester, New York, USA: John Wiley and Sons Ltd., 1990.

Figure 18. Geomechanical modeling using finite element analysis. The stimulation of the Barnett Shale Well 3H, Stage 5, was near a major fault. The fault plane projections (olive green) are shown intersecting Wells 1H (blue), 2H (green) and 3H (yellow). Simulations were run for materials near the fault that had equivalent high stiffness (top) and low stiffness (bottom). The fault properties affect the extent of the region perturbed by fracturing in the simulations. Analysts consider the region perturbed by fracturing to be similar to the region where microseismicity occurs. In this case, failure and microseismicity are expected to be limited in the region near the fault. The colored volumes show only the simulation elements where the stresses are perturbed toward failure by the stimulation. The colors correspond to the minimum in situ effective stress; purple is low compression, showing the region where tensile failure is most likely to occur, and red is high compression. (Adapted from Williams et al, reference 61.)


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open and which ones behaved as barriers that promoted vertical or asymmetric fracture growth.

Modeling for this Barnett Shale stimulation helped analysts understand the downward growth of microseismicity into the Viola Limestone. The complex fracture simulator reproduced observa-tions of hydraulic fracture interactions with natural fractures, which acted as barriers to prop-agation in the Barnett Shale and in the underlying Viola Limestone. By tuning the fault properties within the finite-element geomechanical model, analysts were able to match results with the observed distribution of MS events.

Analysts used finite-element geomechanical modeling to study how fault properties influ-enced the zone that a hydraulic fracture perturbs toward failure (Figure 18). In this type of model-ing, the rock mass, including its embedded faults and fractures, is described as an equivalent medium that has standard mechanical properties such as elastic modulus and strength.63 The pres-ence of an active fault may alter the regional stress field in its vicinity, pushing outward, nor-mal to its plane, and increasing the stress on frac-ture interfaces. The simulations revealed that the compact distribution of MS events in stages pumped close to the fault was consistent with

large values of fault-related stiffness. By simulat-ing all stages and varying fault-related stiffness, modelers demonstrated that the interaction with the fault was consistent with microseismicity that was more compact in the stages toward the heel than in those toward the toe. The under-standing of fault interaction gained from this case study should benefit reservoir engineers planning future refracturing operations in these wells or stimulation treatments in nearby wells.

Challenges and the FutureAs operators develop unconventional resources, determining the optimum well spacing and com-pletion strategies that maximize ultimate recov-ery is critical. To help operators achieve these objectives, MSM provides key data for constrain-ing and calibrating models used to help geoscien-tists with data interpretation and integration. Microseismicity is induced as the reservoir and adjoining formations respond to stimulation treatments. Models used to predict how these for-mations should respond to stimulations must simulate and faithfully reproduce the observed microseismicity. Challenges for geoscientists are the accurate measurement of MS events and extracting of maximal information from them.

Advances in acquisition, processing, interpre-tation and integration of MS data are providing unique insights into and increased understand-ing of stimulated reservoir behavior. Models help engineers interpret and constrain MS data, and geomechanical earth models help them charac-terize the variability of reservoir properties. Fracture network modeling facilitates predic-tions of the interactions between hydraulic frac-tures and rock fabric. Reservoir simulations assist in predicting field drainage patterns, and productivity may be validated through produc-tion history matching. Monitoring microseismic-ity offers valuable data for validating these models and simulations.

Methods such as MSM give operators insight into reservoir dynamics that far exceeds what was possible even a few years ago. Success in develop-ing unconventional reservoirs owes much to the pioneers working in plays such as the Barnett Shale. Recent advances in tools and technolo-gies are allowing operators to develop unconven-tional reservoirs with greater certainty, reduced risk and deeper understanding of the nature of these formations. —HDL

Joël Le Calvez is a Schlumberger Geophysics Advisor and Microseismic Domain Expert in Houston. He works on development and commercialization of microseismic and borehole seismic products while heading a team of geophysicists, geologists and stimulation engineers working on various plays around the world. He has man-aged the Microseismic Services Answer Product Center and the borehole seismic processing and crosswell seis-mic groups in Houston since 2014. His main responsibili-ties are the processing and interpretation of data for geologic, geophysical and geomechanical applications. He also works with product centers defining and testing software programs and with research centers on defin-ing and testing of algorithms. Joël joined Schlumberger in 2001, and after several years in the field acquiring and processing seismic data, he led the microseismic processing and interpretation team in Dallas from 2008 until 2011. He then moved to Houston to manage the North America microseismic processing and interpreta-tion center. He earned a BSc degree in mathematics and physics and an MSc degree in geology and geophysics, both from the Université de Nice Sophia Antipolis, France; a Diplôme d’Etudes Approfondies in tectono-physics from the Université Pierre et Marie Curie, Paris; and a PhD degree in geology from The University of Texas at Austin, USA.

Raj Malpani is a Senior Completions and Production Engineer with Schlumberger Technology Corporation in Houston. For the past 10 years, he has been a part of integrated teams that address technical challenges pertaining to unconventional reservoirs. His interests

include hydraulic fracture treatment design and evalu-ation, production data analysis, reservoir simulation, geomechanics, microseismic monitoring, restimulation, multiwell pad development and weak interface model-ing. Raj holds a BTech degree in petrochemical engi-neering from Dr. Babasaheb Ambedkar Technological University, Lonere, Maharashtra, India, and an MS degree in petroleum engineering from Texas A&M University, College Station.

Jerry Stokes is the President and Owner of Mid-Continent Geological, Inc. in Fort Worth, Texas. He has been a certified petroleum geologist with the AAPG for more than 35 years. Since 1987, he has been involved in oil and gas exploration, geologic consulting and sales and marketing of geologic projects throughout Texas and nearby states. As a geologist for Panhandle Eastern Pipeline, Jerry was responsible for the early develop-ment of underground gas storage fields in Kansas, Louisiana, Illinois and Michigan, USA. He then worked for Rust Oil Corporation as the exploration manager for the Permian basin. He is a member of the Society of Independent Professional Earth Scientists and the Fort Worth Wildcatters. Jerry has a BSc degree in geology and geophysics from Texas Tech University, Lubbock.

Michael Williams is a Principal Reservoir Engineer in Geophysics at Schlumberger Gould Research (SGR), Cambridge, England. Since 2008, he has worked in the area of interpretation of microseismicity, specifically the accurate recovery of statistical information from detection-limited microseismic data, and the application of microseismic interpretation to reservoir simulation

and geomechanical modeling. He joined Schlumberger GeoQuest in 1997 as a commercialization software engineer and worked as project leader and team leader in Abingdon, England. In 2002, he was a team leader in Sugar Land, Texas, developing the first hydraulic frac-ture monitoring software to support the interpretation of microseismic information in the context of fracture stimulation. He joined SGR as a senior research scien-tist in 2004, where he worked in applied reservoir engi-neering, fluid measurements (as program manager) and well test interpretation. Michael received a BS degree in physics and an MS degree in geophysics, both from Imperial College of Science, Technology and Medicine, University of London. He also has a PhD degree in phys-ics from the University of Wales, Aberystwyth.

Jian Xu is a Senior Microseismic Services Engineer in Houston. He focuses on microseismic data interpreta-tion, hydraulic fracture monitoring and stimulation program evaluation in unconventional plays. He joined Schlumberger in 2008 as a field engineer in Bryan, Texas. He held various positions, including access field engineer, production stimulation engineer and microseismic services engineer working on several unconventional plays in the US, all while located in Houston. Before his current assignment, Jian was a senior production stimulation engineer at the Production Technology Integration Center in Houston. He obtained BS and MS degrees in electrical engineer-ing from Tianjin University, China, and a PhD degree in petroleum engineering from Texas A&M University, College Station.

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