Simple Seismic, Complex Fractures

Last month we recognized how recent advances in the processing of 3-D seismic data are providing valuable new tools for the imaging of fracture properties between wells. In order to interpret this data it is necessary to understand how fracture patterns influence the seismic data, and to understand the types of fracture heterogeneity that are likely to occur at this newly imageable scale.

Figure 1
Example of multiple fracture sets and the resulting velocity anisotropy and predicted fracture orientation.

This month we discuss the interpretative steps needed to extract the complex properties of natural fracture systems from the simple seismic fracture models used during processing.

Figure 2
Examples of fracture networks that would have similar seismic attributes over the volume delineated by the square, but very different network permeability values. The pattern in Figure 2a is unconnected; fracture permeability would be zero. On the other hand, the network shown in Figure 2b is well-connected, leading to a permeable fracture network.

Discrete Fracture Network (DFN) models have provided an important tool to make the connection between seismic properties and reservoir.

Interpreting Multiple Fracture Set Properties

Figure 3
Conversion of a DFN model of fracturing into a finite element mesh for use in simulating flow and transport through the fracture network. DFN models make it possible to calculate the network permeability at any scale, and thus provide the link between seismic attribute data and permeability values.

The determination of fracture azimuth and intensity is usually based on the assumption that there is a single dominant fracture orientation, typically vertical.

Figure 4
Snapshot of pressure in the fractures after injection. The colors indicate the pressure variations in the network (Blue colors indicate high pressure, red indicates low pressure). Red arrows show direction of flow out from the injector into the fracture network.

Frequently, fractures occur in several sets with cross-cutting orientations (figure 1), and generally multiple sets are necessary in order to get well-connected plumbing for long-term productivity in the absence of high matrix permeability.

Figure 5
A DFN model with fractures curling around the structure of a plunging anticline. The cyan and blue colors indicate higher permeability, and the magenta cells with lower fracture permeability. Note the high permeability corridor set up along the crest of the anticline.

A number of attributes can be extracted from the seismic data. They can be grouped into two major categories:

• Attributes that sample fracture orientation.
• Attributes that sample fracture intensity.

Orientation attributes such as the fast P or S wave velocity azimuth were initially interpreted as the dominant fracture orientation. In the case of multiple fracture sets, the seismically sampled orientation is a function of the relative intensity of each fracture set.

The net effect of multiple sets appears to be an average azimuth weighted toward the dominant set, although some data appear to show the seismic azimuth switching from one set orientation to another with no intermediate orientations apparent.

For example, in an area characterized by a single dominant regional fracture trend orientation, any additional second fracture set may cause the attribute to appear to rotate away from regional trend, although there is no actual rotation of either of the fracture set orientations.

Anisotropy: Fractures or No-Fractures

In the early development of anisotropic seismic analysis it was thought that high levels of anisotropy, as measured by the difference between the fast and slow P and S wave velocities, indicated a high level of fracturing. It is becoming clear that the influence of multiple fracture sets complicates the seismic intensity measurements.

For example, where fracturing is intense, the seismic properties used to characterize orientation tend to become more isotropic. Small variations in any one set can produce apparent rotations of the interpreted fracture orientation. Isotropy in these seismic properties also exists when fracture intensity is very low.

Thus, the magnitude of the anisotropy does not in itself differentiate between regions of high fracture intensity and low fracture intensity.

Other attributes such as interval velocity must be used to differentiate between an absence of fractures and an excess of fractures.

The Next Step: Calculating Permeability

Once the attributes of the natural fracture system have been mapped, the next step is to take these attributes and use them as a predictive tool.

This process, however, is not as simple as identifying fracture properties at a potential drilling location, as it is the connectivity between the well and the fracture network that is critical. Seismic attributes do not yet quantify any aspects of fracture network connectivity.

For example, in figure 2a the same five fractures occur in each of the two sample volumes, and would exhibit similar seismic attributes. However, only the network on the right (figure 2b) would be conductive.

In order to assess the connectivity of a reservoir, the next step after obtaining the fracture attributes from the seismic data is to use DFN models to understand the consequences of fracture orientation and intensity on permeability.

The DFN approach models fractures as two-dimensional polygonal planar objects, like playing cards, located in three-dimensional space (figure 3a). Each fracture is characterized by its surface area and shape, and has flow properties such as permeability, compressibility and aperture.

Network models can be formed based on an interpretation of seismic attribute data, engineering data and image log data as available.

Once fractures are generated, a finite element mesh can be constructed according to the fracture geometry (figure 3b), and a flow solution can be obtained that takes into account the connectedness of the fracture system.

Figure 4 shows an example of a pressure pulse spreading through a fractured reservoir in response to injection.

Seismic + Fractures = Permeability Prediction

The DFN approach can be combined with seismic attribute mapping by first developing an interpretation of the link between attributes and fracturing.

For example, the difference between the fast and slow P or S velocities can be used to control the fracture intensity of one fracture set within the DFN reservoir model, and the rotation of the P or S fast velocity azimuth can control the generation of the second fracture set.

Once the DFN model has been generated, a grid can be placed over the model and a finite element mesh used to calculate the potential volume of flow within each of the grid cells.

In figure 5, a DFN model is displayed with a grid populated by fractures, with the colors in each grid cell indicating the calculated permeability values. In this case, a high permeability pathway has evolved along the crest of the anticline due to the structural control of fracturing.

Summary

Recent advances in the processing of 3-D seismic data are providing valuable new tools for quantifying fracture properties between wells. In order to make use of this new information, it is necessary to:

• First interpret the connection between the seismic measurement and the naturally occurring fracture sets.
• Connect the fracture pattern to reservoir parameters through techniques such as DFN modeling.

Although uncertainties abound, these attributes provide new insight into notoriously difficult reservoirs, and promise to enhance recovery through focused engineering efforts.

Editor's note: Bob Parney ([email protected], 303-831-0544) is with Axis Geophysics, Denver, and Paul LaPointe is with Golder Associates, Redmond, Wash.