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Microseismic monitoring of hydraulic fracturing treatments in unconventional reservoirs has become an accepted industry practice and, for some companies, standard in the last decade. Especially in shale plays, the fractures created during hydraulic stimulation are quite different from the planar bi-wing textbook example that has been an accepted image in the industry for years. In reality, the fracture network created in unconventional plays is extremely complex, and accurate imaging is necessary to understand the formation and to optimize completion.

Microseismic data can be used to model a discrete fracture network (DFN) that serves as an important input for reservoir simulation. The model allows the total rock volume affected by the treatment to be calculated. This can then be further refined by placing proppant in the DFN to help identify the part of the stimulated rock volume (SRV) that likely contains proppant and should therefore be productive. This type of analysis using microseismic data allows operators to understand what proportion of the reservoir is actually productive and helps to answer questions around well spacing, stage length, and alternate treatment options.

FIGURE 1. The productive SRV is shown. The total DFN can be seen in blue in the upper left corner. The proppant-filled portion of the total DFN can be seen in red in the upper right corner. From the total DFN, the total SRV can be determined as illustrated in blue in the bottom left corner. The productive portion of the SRV due to proppant-filled fractures is shown in red in the bottom right corner. (Images courtesy of MicroSeismic Inc.)

Three methods are commercially available to record microseismic data: downhole, surface, and near-surface monitoring. Though single-well downhole monitoring is sufficient in some cases, the broad areal coverage of surface and near-surface monitoring usually provides more detailed information. Surface monitoring makes it possible to determine the way in which the formation is breaking (strike, dip, rake), which is essential to build a highly accurate DFN.

After an acquisition method has been selected and the proper data have been acquired, the DFN is built in two steps. First the strike and dip of the failure plane are determined for each individual event. Then the geometry of the failure plane is determined by incorporating the magnitude of each event as well as the calculated rigidity of the rock and the injected fluid volumes. Once the DFN is completed, the SRV can be determined. In addition, the proportion of the SRV that contains proppant and is therefore productive can be estimated.

This subset volume is known as the productive SRV, and calculating it begins by estimating the propped half-length. Estimating the propped half-length is performed by filling the subset DFN with proppant from the wellbore outward on a stage-by-stage basis. The packing density of the proppant is variable and can be adjusted based on the specific gravity of the proppant and hydraulic fracture simulation. For each stage, the fracture volume inside the DFN is filled with proppant until all of the proppant that was pumped is accounted for. The estimated propped half-length is determined by looking at the statistical distribution of proppant-filled fractures around the wellbore. This accounts for the fact that the fractures are centered on the microseismic events while honoring the distribution of fracture sizes for a given stage.

To calculate the productive SRV, a 3-D grid is applied to the proppant-filled DFN. Every grid cell containing a nonzero fracture property that was filled with proppant is included in the productive SRV. This yields a rock volume that is expected to contribute to production in the long term (Figure 1).

Based on the DFN and SRV, the permeability tensor can be calculated for the rock volume containing microseismic activity. The permeability derived is the fracture permeability for a dual-porosity, dual-permeability reservoir model. It should be noted that it is not representative or in any way indicative of the matrix permeability.

In addition to the fracture permeability calculated from the DFN, a system or bulk permeability can be obtained from an evaluation of the spatiotemporal dynamics of the micro-seismic events and the apparent system diffusivity. This evaluation can help to characterize the reservoir and estimate the results of hydraulic fracturing by estimating improvements to permeability.

Case study

An integrated analysis of hydraulic fracturing treatments in the Marcellus shale was conducted to investigate the relationship between reservoir geology, wellbore completion, stimulation design, and micro-seismic data. These findings were then used to evaluate the correlation between hydrocarbon production and microseismic results relative to changes in geology and the stimulation approach. The observed variability in the microseismic response was used to derive regional trends and optimize field development. Initial production was compared to reservoir and engineering parameters such as treatment pressures, sequence of treatments (toe to heel vs. zipper frac), net pressures, and stage spacing to determine if the variability in the microseismic results is due to engineering differences or to spatially varying reservoir properties.

The microseismic dataset was acquired with a permanently installed near-surface array consisting of 101 geophones (Figure 2).

FIGURE 2. This map shows a view of a surface microseismic monitoring array. Recording stations can be seen as turquoise circles, and well pads are named with letters.

Two fracture sets are present in the Marcellus shale. J1 fractures are oriented northeast to southwest and were formed as natural hydraulic fractures during the Alleghanian orogeny. J2 fractures (oriented northwest to southeast) were formed during hydrocarbon generation and cross-cut the older J1 fracture set.

Ideally, a horizontal well attempting to produce from the Marcellus shale should activate the J1 fracture set to exploit the high permeability of these fractures and activate the J2 fractures to connect parallel J1 fractures. If the J2 fracture sets are stimulated, those fractures will inevitably intersect the J1 fracture set, allowing production from those fractures. Operators drilling in the Marcellus shale have found that orienting wellbores to activate both the J1 and J2 fracture sets will yield the highest production. Additionally, in this case, zipper-fracing was found to better activate both fracture sets and further improve production.

To analyze different treatment attributes, a base DFN model was created and varied on six dimensions (flow rate, treatment pressure, stage duration, stage length, number of perforations, and perforation cluster spacing) with the goal of refining completion designs for optimal economic return.

In this case, stage length had the greatest economic impact. Given the natural fracture density observed in outcrops of the Marcellus shale, it was found that an additional 1.5 m (5 ft) between each of the five perforation clusters would only minutely change the hydraulic fracture network. This finding could permit the elimination of one hydraulic fracturing stage per well. Applied across the entire pad, the potential savings could have approached a seven-figure dollar amount. Additionally, zipper-fracing was found to better activate both the J1 and J2 fracture sets to improve production. To further optimize field development in the Marcellus, productive SRV results can be used to provide information for well spacing and ensure that hydrocarbons are not being left behind.

These new advances in technology integrating geophysical and engineering results can clearly help to provide increased value to oil and gas companies operating in unconventional shale plays. The ability to understand what proportion of the SRV is actually productive allows operators to improve production and lower costs by optimizing well spacing and determining ideal stage lengths.