Microseismic Maps Production Volume. The âBetter Businessâ Publication Serving the Exploration / Drilling / Producti
JANUARY 2016 The “Better Business” Publication Serving the Exploration / Drilling / Production Industry
Microseismic Maps Production Volume New microseismic monitoring techniques capture lower-amplitude signals and continuous background seismicity to image the pretreatment natural-fracture network, as well as the amount of reservoir rock stimulated during pressure pumping and the active portions of the stimulated volume during the production phase.
By Jan Vermilye and Charles Sicking HOUSTON–Ambient monitoring of microseismic activity before, during and after hydraulic fracture treatments can provide valuable information on the size and geometry of the rock volumes and fractures that contribute to production. A major goal of microseismic monitoring of hydraulic fracture treatments is to determine the size of the stimulated reservoir volume (SRV), which is the volume of rock containing the connected fracture network generated by the stimulation. The size of the SRV is most commonly estimated from the volume of the microseismic event (sometimes referred to as hypocenter) cloud. The events that define this cloud are observed during the well stimulation. SRV has been of great interest because it was at first thought to be positively correlated to well performance. However, this relationship is being challenged. The size of the SRV is measured while the well is being actively stimulated, but not all of these active fractures will contribute to production. Changes in stress and fluid pressure that take place after stimulation is complete and during flowback result in some fractures closing. This is especially true for those fractures that did not receive proppant.
Reproduced for Global Geophysical Services with permission from The American Oil & Gas Reporter www.aogr.com
JANUARY 2016 Frequency (Hz)
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By monitoring the well after it has been put on production, the size of an alternative volume–the active production volume (APV)–can be determined. Seismic emission activity caused by the stress and fluid perturbations that occur during production is used to map the APV. Most microseismic monitoring methods use microearthquake (MEQ) detection and location as the basis for determining SRV. Energy from these short-duration events is mapped and the SRV is generated by identifying the high-density zone of MEQs surrounding the wellbore. Lower amplitude signals that are continuous for longer durations are not always detected by these methods. Ambient monitoring captures this continuous background seismicity and also the MEQs that are too small to generate detectable arrivals. Figure 1 shows a comparison of spectrograms for an MEQ and a long-duration-signal (LDS) event. Both spectrograms are one minute long. The vertical axis is frequency from 0 to 80 hertz. Warm colors indicate more energy at a given frequency and time. The time of high-intensity activity for the microearthquake event is only two seconds, while the LDS event is active for at least 15 seconds. The LDS event also appears to have a greater drop-off in strength with frequency, resulting in a lower level of high-frequency energy. This long–duration energy dominates the passive data, and as such, ambient technology captures far more seismic-emission energy than does the MEQ
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The spectrogram for a microearthquake at left compares to that of a long-duration-signal event at right. Both spectrograms are one minute long. Hot colors indicate more energy at a given frequency and time.
method. Ambient monitoring provides the most reliable images of the subsurface and better constrained SRVs. Physical Model Seismic energy is emitted as rocks release stored elastic strain energy. This energy is not distributed evenly in the earth’s crust, but is released preferentially on fracture/fault surfaces and in the damage zones surrounding these surfaces. Fracture mechanics predict stress concentrations associated with fractures. Both field studies and laboratory experiments show clear evidence for these stress concentrations, recorded in the damage zones associated with fractures. Damage zones consist of rock volumes with a high density of smaller fractures that display exponentially higher densities with proximity to the main fracture surface. The brittle crust is in a state of unstable frictional equilibrium, and therefore, very
FIGURE 2
At left, an active production volume is generated from the fully populated depth volume. First a threshold is applied in order to remove voxels with low-amplitude background (center). Then only the high-amplitude locations with direct connection to the wellbore are retained in the APV (right).
small changes in stress (less than 0.01 atmospheres) can cause rock failure. Failure occurs preferentially on small, optimally oriented fractures and in the zones surrounding the fractures where cracktip stress concentrations amplify the stress magnitudes. During well treatment, the unstable equilibrium is disturbed significantly as additional fluid volumes alter the stress state around the wellbore and reduce the normal stress on pre-existing fractures. During production, more subtle movement of fluid produces a similar effect. In both cases, seismic waves are emitted as the rock releases stored elastic strain energy. Ambient Seismic Imaging Our ambient seismic imaging method uses depth migration applied to both lower amplitude MEQs and signals that are continuous for longer durations. The energy from these low-amplitude events and LDS is focused to the location of origin within the earth. The process is a one-way travel time prestack depth migration. The signals travel one way–from the reservoir to the receiver–as opposed to surface reflection seismic data, in which the signal travels over a two-way path from the seismic source to the reservoir and back to the receiver. Field data for ambient seismic imaging are collected using a surface array or shallow buried grid. Downhole data can be processed by this method if there is a sufficient density of receivers and adequate aperture, but this is infrequently
TECH TRENDS The length of the time window is based on the imaging objective. The time window before well completion images the natural, pre-existing fracture network. The time window during the frac treatment images the induced fractures and SRV. Applying this method to a time interval recorded while the well is producing provides a measure of the rock volume contributing to production: the APV. The remainder of this article will describe the workflow and products generated during these production time intervals.
FIGURE 3
APV And SRV
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The stimulated reservoir volume for a well at left is shown alongside the active production volume at right. Map-view slices through the volumes at well depth are shown along with a map-view slice of the natural fracture network at the horizontal well depth.
the case. A velocity model is built and calibrated. This can be a simple 1-D velocity model derived from a sonic log or a 3-D velocity model including anisotropy. The necessity for complexity in the model will depend on lateral velocity variations within the volume of interest. Statics are computed and applied. A volume of interest is defined and divided into subvolumes called voxels. A table of one-way travel times is computed from every voxel to every receiver. The time and location of the ambient seismic signals are unknown. In order to image the signal, it is streamed through the depth imaging algorithm that sequentially focuses each time step into every voxel being imaged. The focusing for a single time step–for a single voxel– consists of time shifting each trace based on the travel times from the voxel to each receiver. The images from each of the time steps are stacked in order to produce a
depth volume for the total time window of interest. This volume can be processed further to generate wellbore activity volumes that show only activity in direct contact with the wellbore, or detailed fracture image volumes can be extracted from the depth volume. The method is applied to time windows before, during and after well completion.
Most production monitoring is recorded with a permanent, shallow buried array. This allows for repeated observations throughout the lifetime of the reservoir. For each observation interval, several hours of field data are recorded and processed. A minimum of three hours of high-quality data are required for a stable, stacked depth volume. This volume is further processed to map the volumewide natural fracture network. APVs for all wells under the array are extracted from this volume also. While there are high-activity locations throughout the volume, only those with connectivity to the wellbore contribute to the producing volume. Figure 2 shows the fully populated volume, the volume clamped to remove the low-activity background, and an APV. The SRV is generated by measuring activity during the hydrofracture stimu-
FIGURE 4 SRV
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APV-3 years
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This SRV and two APVs were generated after two and three years of production. Mapview slices through the volumes at wellbore depth are shown at left. At right are profile views of the same three volumes sliced along the wellbore. Warm colors show high levels of activity and cool colors show lower activity levels.
JANUARY 2016 lation. We generate the SRV with the same workflow used to generate APV. It is expected that once stimulation is completed and fluid pressures decline, some of the fractures will close and become inactive. Figure 3 shows the SRV imaged during well stimulation and the APV for the same well after two months of production. The APV is about 40 percent smaller than the SRV. Both volumes are shown along with the natural fracture network. The most active locations along the wellbore in the APV are where active natural fractures intersect the wellbore. Monitoring APV over years of production shows that the size of the volume and the active locations along the wellbore change over time. Though the characteristics of these changes appear to vary among reservoirs, the example in Figure 4 shows typical results for the wells in one unconventional reservoir. FIGURE 6 Well B
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APV for well A (right) and potential production volume for well B are presented as map-view, semi-transparent, orthogonal projections of the volumes.
FIGURE 5
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Potential production volume (left) and a combination of APV for 2.5 years of production (center) are shown as map-view projections of the volumes. The APV is overlaid on the potential volume at right.
The SRV and two APVs are shown. The first APV was measured after two years of production and the second was measured after three years of production. There is a decrease in the size of the volume of rock activated by production over time, as production declines. Monitoring Production Potential production volumes can be generated from data collected before a well is drilled. The volumes show activity associated with the pre-existing fracture network, which appears to exert strong control over future production. Figure 5 shows a potential production volume generated from data recorded two months before the well was drilled. Also shown is a volume generated by combining APVs generated during the first two and a half years of production. The data for these volumes were collected inadvertently while monitoring well completions under a buried grid. Planned, regular monitoring of wells is expected to give a more comprehensive image of production changes over time. Monitoring production for multiple wells under a grid allows for mapping interactions or potential interactions be-
tween wells. Volumes of rock that are not contributing to production can be identified for either recompletion or as sites for infill wells. Differences in activity can be used to design recompletions on a stage-by-stage basis. Figure 6 shows potential interactions between two wells. The APV for well A is shown while producing, just prior to completion of well B. The potential production volume was generated for well B. This analysis was carried out after completing well B, but indicates a high potential for interactions between the two wells. Interactions could have been predicted before completion. When well B was completed, a frac hit on well A adversely affected production. If this analysis had been completed in advance of the fracture, well A could have been shut in for the completion, or stage locations in well B could have been modified in an attempt to avoid the frac hit. Conclusions Ambient production monitoring is a valuable new technology that provides regular monitoring of production activity
TECH TRENDS over the lifetime of a reservoir, and also provides predictive value for future wells drilled in the reservoir. Regular monitoring of wells is made
possible by installing a shallow buried grid over the reservoir. Existing grids, used for frac monitoring purposes, record valuable production data for wells that
were producing under the grid at the time of monitoring. Existing grids can be reactivated on a regular schedule to monitor current and future production. ❒
JAN VERMILYE
Jan Vermilye serves as Global Geophysical’s manager for microseismic processing and interpretation, and is the company’s principal geologist for Tomographic Fracture ImagingTM. She has more than 20 years of experience working and teaching in the field of geology, and has expertise in fracture mechanics and structural analysis of natural fracture systems from the microscopic to the field scale. Vermilye has been published in “First Break,” the “Oil and Gas Journal,” the “Journal of Structural Geology,” the “Journal of Geophysical Research” and “Geology.” She holds a bachelor’s in geology from the State University College of New York, a master’s in geology from Columbia University, and a doctorate in geology from Columbia University.
CHARLES SICKING
Charles Sicking is vice president of research and development at Global Geophysical Services, and led development of the company’s microseismic processing system. Before joining Global Geophysical, he was chief geophysicist at Weinman GeoSciences. He also formerly served as a research geophysicist at ARCO. During his career, Sicking has developed multiple seismic processing algorithms and technologies for 2- and 3-D velocity model building using time-todepth and depth imaging applications. He also led development of the seismic wavelet processing flow and methodology for ARCO’s seismic processing system. Sicking holds a B.A. in physics and a Ph.D. in geophysics from the University of Texas at Austin.
