Shale plays have become one of the important sources of hydrocarbons, both domestically and globally. Due to their unique nature, shale plays have demonstrated challenges to geoscientists in reservoir E&P. In searching for the sweet spots of a shale formation, geoscientists must rely on seismic data, microseismic data, core data, well data, and a wealth of other technologies to characterize and model what is frequently a highly heterogeneous formation. The defining factors of a sweet spot include rock properties such as brittle/ ductile quality, in situ stress, and total organic content.

Project review

The Eagle Ford shale is located in the Western Gulf Basin and trends across South and East Texas. This Cretaceous formation is the source rock for the Austin Chalk oil and gas formation. The seismic data cover about 340 sq km (130 sq miles) across Karnes and Live Oak counties and was acquired by Seitel. The seismic survey is in the primary production zone of a wet gas/condensate window. There are two existing wells inside of the seismic survey. The seismic data were processed through full-azimuth prestack depth migration with continuous azimuth reflection angle gathers as the primary outputs.

Figure 1. Stress intensity is represented by both the color and the length of the vector. The orientation of the vector represents the azimuth of the symmetry axis perpendicular to the orientation of the maximum horizontal stress.

Objectives

Shales are frequently highly heterogeneous in nature. Seismic data, with properly preserved amplitudes and sampled in the angle domain, contain information related to lithology and rock properties. Properly processed and analyzed, the seismic data are not only relevant in prospect identification but also are an important data source for well planning in a shale play. The objectives of this study are:

To observe shale heterogeneity by examining and analyzing different seismic attributes such as frequency-dependent attributes, structural attributes, and trace shape attributes;

To determine and map the shale brittleness using rock mechanical attributes such as Poisson’s ratio and Young’s modulus; and

To estimate stress and its orientation by deriving, evaluating, and integrating amplitude versus angle versus full azimuth (AVA(Z)) and residual moveout attributes.

Figure 2. Sweet spot identification requires a workflow that takes well and seismic data into account. (Images courtesy of Paradigm)

Well data analysis

There are two vertical wells inside the seismic survey that include both sonic and density logs. The well data are used in seismic-to-well calibration operations to determine the shale formation depth and thickness, to derive wavelets from seismic angle stacks, and to build the impedance background models.

Seismic attribute visualization/interpretation

Post-stack seismic amplitudes are used to interpret structural horizons and to generate many types of seismic attributes. The key process in post-stack seismic attribute analysis is to examine and analyze different seismic attributes and then narrow them down to a manageable number that contribute to the understanding of the target.

Structural attributes such as curvature and coherence are used to delineate seismic scale discontinuities such as faults. Physical attributes like spectral decomposition sample seismic energy variations with frequency and can be useful in evaluating thin bed effects like tuning. Organized classification attributes based on trace shape similarity, for example, are useful in detecting changes in facies, lithology, and rock properties.

Figure 1 is a co-visualization of a curvature attribute and the trace shape classification attribute for the interval of Eagle Ford shale. Major faults trending northeast are easily identified with the curvature attribute. One of the wells penetrates a visible fault. Trace shapes are delineated by nine organized and structured groups of “seismic facies” represented by different colors that indicate changes within the shale interval. This attribute demonstrates the heterogeneous nature of the shale formation. Co-visualizing the two attributes not only allows us to describe the geology with each attribute but also the attributes’ relationship to each other. For example, we can visualize how the facies change relative to faulting to understand if the faults act as the boundary for the facies and are responsible for their compartmentalization behavior. Faulting could help explain the distribution of ductile and brittle behavior in the Eagle Ford shale.

Figure 3. In a co-visualization of curvature and facies classification map, colors represent facies as delineated from seismic trace shapes. Faults are easily identified with the curvature attribute.

Geomechanical attribute generation and interpretation

One property that defines sweet spots is shale brittleness. Where the shale is brittle, it responds favorably to hydraulic fracturing and stimulation when compared with the ductile regions within the shale. Mapping the shale brittleness is important in the process of prospect identification and characterization. Shale brittleness can be described by some of the mechanical attributes that can be derived from seismic and well data, such as Pois-son’s ratio and Young’s modulus.

Seismic inversion is a pathway to estimate these attributes and properties in 3-D. Great care must be given to the data preparation (e.g. wavelet analysis and background model building), inversion parameter optimization, and quality control to ensure the accuracy of the compressional (P) and shear (S) wave impedances. Pois-son’s ratio and Young’s modulus (times Rho) can subsequently be derived from the P and S impedances.

A shale brittleness indicator can be developed using both the Poisson’s ratio and Young’s modulus Rho attributes. Figure 2 shows the result of the 3-D distribution of the shale brittleness indicator in the Eagle Ford formation. Geobodies that represent higher brittle zones can be isolated and mapped using cross-plotting operations. The extracted geobodies can be incorporated into the reservoir model.

Figure 4. A distribution of a shale brittleness indicator in the Eagle Ford formation shows geobodies representing higher brittle zones.

In situ stress estimation and mapping

Understanding stress and its orientation is critical in well planning and hydraulic fracturing program design. The acquired seismic data include a rich surface sampling of azimuth data. An in situ decomposition of the recorded seismic wavefield into continuous azimuth reflection angle gathers in depth can be used to estimate stress and stress orientation. In the study area, the Eagle Ford formation ranges in depth from approximately 3,445 m to 3,845 m (11,300 ft to 12,600 ft) dipping from northwest to southeast at an average dip less than 2 degrees. Horizontal transverse isotropy (HTI) media could be assumed for the local Eagle Ford layer. Two independent approaches were used to estimate layer anisotropy in a HTI media, one based on full-azimuth amplitude sampling and the other based on full-azimuth residual (velocity) moveout sampling. Both measure the azimuthally dependent behavior of the data in depth.

Figure 3 shows the stress map derived using AVA(Z) inversion from the full azimuth reflection angle gathers. The stress intensity is represented by both the background color and the length (magnitude) of the vector. The orientation of the vector represents the azimuth of the symmetry axis perpendicular to the orientation of the maximum horizontal stress. Full-azimuth reflection angle gathers are used to systematically measure HTI anisotropy effects of less than 1% with repeatability and confidence. These types of results are not possible with conventional surface azimuth sectoring approaches.

Seismic data carry critical information related to rock properties and stress, which are among the determining factors for sweet spot prospecting. Technologies are available to generate seismic attributes that can fully characterize the geology of the shale plays, including structural features, formation heterogeneity, rock properties, and stress. Highly desired information such as shale brittleness and stress can be extracted from the seismic data, making seismic data more relevant in the exploration and exploitation of shale resource plays.