VII INGEPET 2011 (EXPR-2-AC-33E)

RISK REDUCTION IN THE DETERMINATION OF FRACTURE’S DENSITY AND ORIENTATION THROUGH AZIMUTHAL PROCESSING AND INTERPRETATION OF

SEISMIC DATA

Alvaro Chaveste (Geokinetics, Inc.)

Abstract The determination of fracture density and orientation is important in plays in which reservoir permeability is associated with micro-fractures. In these reservoirs, effective porosity is dependent upon the density of open fractures and permeability has a directivity component associated to the fractures’ orientation. Accurate determination of fracture density and orientation using surface seismic helps, then, in determining locations of good reservoir storability, as well as directional drilling orientation for optimum permeability. The anisotropy of P- and S-wave velocities is commonly associated to magnitude and orientation of stress fields or open fractures and is; hence, the medium through which factures are characterized using surface seismic data.

A case study in the Marcellus Shale (NE United States) is presented in which wide azimuth seismic data, acquired and processed to preserve the amplitude and velocity information of the source-receiver azimuth, is used to estimate anisotropy attributes. The Marcellus Shale is known to have two sets of micro-fractures associated to different tectonic events. Particular to this play is that one set of micro-fractures is perpendicular to the co-located macro-fractures. In this case although curvature, or other geometric attributes, reveal the strike of macro-fractures, this does not correspond to that of the micro-fractures. Anisotropy analysis through elliptical inversion identifies both sets of micro-fractures and verifies their expected position in relationship to the oroclinal belt and macro-fractures.

Introduction

Conventionally processed seismic data provides a structural image of the sub-surface which helps, among other things, in defining macro-fractures or faults; nevertheless, the micro-fractures that define permeability and porosity within a formation, although possibly associated to macro-fractures, are not necessarily part of the structural picture and, hence, are not commonly observed in migrated stacks. Micro-cracks can be characterized by considering that velocities in cracked media vary as a function of micro-fractures’ orientation and density; the velocity being faster in the fractures direction or maximum horizontal stress and slowest in a directional perpendicular to fractures. Velocity anisotropy provides, then, the means of estimating reservoir properties from seismically derived velocities.

An added benefit of the resultant anisotropic velocity field is that it can be used for anisotropic NMO correction. In this case the correction applied to each trace is based upon the “velocity ellipse” at the trace’s Common Image Point (CIP) as well as the trace’s azimuth and offset. The NMO corrected data results in “flatter” gathers for subsequent pre-stack amplitude analysis and higher resolution stacks.

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The area under analysis is approximately 100 squared miles and is within the Allegheny Plateau Province of the Appalachian Basin in Pennsylvania, USA. In the study area, the Middle Devonian Marcellus formation is at a depth of 6000 feet and is 300 feet thick. Stratigraphically it lies at the basal Hamilton Group and is overlain by the Mahantango Formation, which is a thick gray-green shale with inter-bedded siltstones and sandstones. The Marcellus Formation is divided into two organically rich members separated by the Cherry Valley Limestone. The lower Marcellus shows TOC’s larger than 10% which is of economic relevance since a correlation is seen between organic-rich black shale facies, gas shows and/or production.

Structurally, the Devonian section is influenced by deep-seated structure; which, along with the salt withdraws and/or decollement of the Upper-Silurian Salina Formation produces shallower structure decoupled from the deep structure at the salt layer. As a result, the Marcellus is a thin-skinned system with shorter wavelength folds than deeper ones. Figure 1 shows the structural style as well as the seismic character of the stratigraphic column of interest.

Figure 1 – Structural style and seismic character of the stratigraphic column of interest.

Methodology

The methodology followed makes use of wide azimuth data to estimate, through elliptical inversion, anisotropy’s magnitude and direction. The technique, which fits an ellipse to four or more velocity measurements, is based on the assumptions that the velocity magnitude changes following an ellipse in an anisotropic medium (Figure 2).

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Two philosophies for elliptical inversion are commonly regarded. One in which the seismic data is sectored into azimuth bins prior to elliptical inversion (sectored elliptical inversion) and a second in which all traces are used independently in the analysis (Thompson, 2002). While the second technique provides higher resolution it does not allow for intermediate QC steps and results, usually, in noisy estimation of maximum and minimum interval velocities as well as increasing uncertainty in the estimated azimuth with reflection time as a consequence of the iterative estimation of this attribute. The noise is usually handled by resampling data to larger time intervals; which results in decreasing the resolution sought in the first place with the un-sectored technique.

In the following paragraphs the methodology and results, using the sectored elliptical inversion, are presented for an area Northeast USA in which the target formation (Marcellus Shale of the Devonian) has two sets of fractures related to two different geologic events. The identification and extent (areal and vertical) of each of these sets is of economic relevance.

Field data analysis and QC

Estimation, from seismic data, of azimuth dependent attributes or rock properties require uniform data sampling in both, azimuth and offset; as well as a processing sequence that preserves these for later analysis. The Offset Vector Tile (OVT) acquisition technique (Vermeer, 2005, 2007) allows data processing such that offset and azimuth are preserved through pre-stack migration.

It should be noted that adequate field design results in optimum use of the data acquired. Analysis of the azimuth-offset distribution in Figure 3 shows that, for the case presented, data has a homogeneous offset azimuth distribution from zero to, approximately, 10,000 ft. Traces with larger offset are not included in the analysis to avoid bias in the estimation of velocities associated to acquisition design.

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Figure 2 - Velocity ellipse. Velocities in a cracked medium result in different wave velocities for different propagation directions (anisotropy). Elliptical attributes (f, VMAX, VMIN an ), obtained through elliptical inversion, help reduce risk in the estimation of fractures’ density and orientation

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The pre-stack time migrated (PSTM) offset limited data is sorted into four azimuth bins (0o to 45o, 45o to 90o, 90o to 135o and 135o to 180o). Post migration processes, such as Radon filtering, used to increase signal-to-noise ratio are applied independently to each azimuth bin.

Velocity Estimation

The required high-resolution velocities are obtained by automatically updating the handpicked RMS velocities per azimuth bin using a hybrid approach based on discrete picking in high semblance events and sample-by-sample picking using AVO attributes (Swan, 2001) in low semblance areas. This results in the estimation of velocities for every sample of every trace. Small residuals are expected, as seen in Figure 4, and the use of an automated technique insures the consistent, un-biased estimation of velocities.

Figure 4 – High-density velocity analysis. The Pre-Stack Time Migrated (PSTM) gather in the central image is the same as the one in the left after residual NMO velocities applied. The magenta line in the middle panel shows that the residual velocities to apply are in the order of 3%. The panel on the right shows in color, the incidence angles.

(Azimuth-Offset-Fold Distribution from Data)

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Figure 3. Rose diagram of all traces recorded in the survey. The white circle indicates the range off offsets for which there is full azimuthal coverage. Traces outside this circle are not included in the analysis.

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The lateral and vertical resolution of the resultant interval velocity field is improved when compared to the equivalent from the handpicked velocities. In Figure 5 both velocity fields are compared in vertical section and Figure 6 compares these, along with the amplitudes, in time-slice.

Figure 5 – Inline 6015. Interval velocities from seismic. The interval velocities computed from the velocity field estimated through automated velocity analysis (left) results in better spatial and vertical resolution than the equivalent computed from the handpicked velocities (right).

Figure 6 – Time-slice 680 msec. Interval velocities from seismic data. Comparison of interval velocity fields in time-slice shows the increase in spatial resolution of the velocity field estimated through the automated velocity analysis (center) as compared to the equivalent computed from the handpicked velocities (right). Note that velocity structure in the central panel has a good correlation with the structure as seen in the reflection amplitude time slice (left).

Elliptical Inversion

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The interval velocities estimated for each azimuth sector are elliptically inverted to generate, sample-by-sample, volumes of maximum velocity (VMAX), minimum velocity (VMIN), the azimuth to the fast velocity (�) and the error in fitting the ellipse (�). Figure 7 shows VMAX and VMIN extracted at the Lower Marcellus (Top Marcellus + 40 msec.).

The magnitude of anisotropy (�) computed as

� = (VMAX – VMIN) / VMAX Eq. 1

is displayed in Figure 8 along with the azimuth (�).

Figure 7 – Maximum and minimum velocities (VMAX and VMIN) extracted at the Lower Marcellus (top Marcellus + 40 msec). VMAX and VMIN are used to estimate anisotropy (A in equation 1).

Figure 8 – Azimuth (�) and anisotropy (�) extracted at the lower Marcellus (top Marcellus + 40 msec.). The map in the left is the azimuth, in degrees from North, of the fast velocity (VMAX); which is related to that of the maximum horizontal stress (�H) or fractures’ direction. The image to the right corresponds to the magnitude of anisotropy commonly associated to fracture intensity.

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Interpretation of Results

In unconventional shale-gas plays drilling is often designed to cut across open joints, if present. When additional stimulation is necessary, common practice is to drill normal to the maximum horizontal stress (SH) of the contemporary stress field. The identification of the magnitude and orientation of open fractures and/or the maximum horizontal stress plays then an important role in reservoir management.

According to Engelder (2008) “Three joint sets are common in the Appalachian Basin, designated as J1, J2, and J3. Where present in outcrop, J1 maintains its ENE orientation regardless of location relative to the oroclinal bends of the Central and Southern Appalachian Mountains (i.e. Engelder and Whitaker, 2006). In the Valley and Ridge, J2 is found normal to fold axes, qualifying this set as a systematic cross-fold joint set”.

In the area of analysis the oroclinal bends trend East-West (Figure 9) and the orientation of the J2 joints associated to the valley and ridges is North-South as determined by the estimated azimuth (Figures 8 and 9). Away from the oroclinal bends, the estimated azimuths are, approximately, 90 degrees corresponding to the J1 set in agreement with Engelder’s observations.

Figure 9 – The top Marcellus time structure map at left shows the valley and ridges of the oroclinal belt. The fracture orientation in these is North-South as observed in the right image corresponding to an extraction of the estimated azimuth (�) at the lower Marcellus (Top Marcellus + 40 msec.).

The cross-plot of anisotropy versus azimuth of Figure 10 shows clearly both sets of fractures.

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Analysis of the estimated azimuth and anisotropy in vertical section (Figure 11) provides information about seal integrity. Note that in the valley and ridges the estimated anisotropy associated to the J2 set goes beyond the top Marcellus suggesting that, in this location, the seal is non-existent. Away from the valley and ridges, where the J1 set prevails, the seal appears to exist as no fractures go across the top Marcellus into the Mahantango Shale. In the study area the locations where the J1 set exists and shows large anisotropy magnitude may prove to be good locations for gas entrapment.

Figure 11 – Anisotropy azimuth (left) and magnitude (right). Fractures’ orientation in the valley and ridges is North-South as expected for the J2 set. Away from the valley and ridges, the fractures’ orientation is East-West corresponding to that of the J1 set.

An added benefit of the anisotropic velocity field (� and �) is that it can be used to NMO correct the traces as a function of both, offset and azimuth. This would result, in an anisotropic environment, in higher resolution stacks than those obtained with conventional velocity estimation techniques (Figures 12 and 13).

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Figure 10 – The cross-plot of anisotropy and azimuth extracted at the lower Marcellus (Figure 8) shows two clusters associated to J1 and J2 joint sets.

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Figure 12 – PSTM stack. Stack with handpicked NMO velocities

Figure 13 – PSTM stack with high-resolution anisotropic (HTI) NMO correction. Note the improvement in resolution, continuity and fault definition when compared to the stack in Figure 12.

Conclusions

The methodology presented estimates anisotropy’s magnitude and orientation that match the expected fracture’s density and orientation. The definition of the fractures’ set (either J1 or J2) and its areal and vertical extent is of importance as they determine fractures’ conditions directly associated to, seal integrity, orientation and intensity of open fractures; all of which help in placing wells and defining orientation for optimum performance.

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Acknowledgements

The author thanks Geophysical Pursuit Inc. (GPI) and Geokinetics Inc., for permission to publish the data. My appreciation goes to Alex Villasana who contributed processing the data and to, Mary Edrich and Tony Rebec for their input on the stratigraphy and structural style.

References

Engelder, T., 2008, Structural geology of the Marcellus and other Devonian gas shales: Geological conundrums involving joints, layer-parallel shortening strain, and the contemporary tectonic stress field: Pittsburg Association of Petroleum Geologists Field Trip (Sept. 12-13, 2008).

Engelder, T., and Whitaker, A., 2006, Early jointing in coal and black shale: Evidence for an Appalachian-wide stress field as a prelude to the Alleghanian orogeny: Geology, v. 34, p. 581-584.

Swan, H. W., 2001, Velocities from AVO Analysis: Geophysics, 66, 1735-1743.

Thompson, L., 2002, Distinguished Instructor Short Course, SEG (2002).

Vermeer, J. O. Gijs, 2005, Processing orthogonal geometry – what is missing? SEG Expanded Abstracts, 24, 2201-2204.

Vermeer, J. O. Gijs, 2007, Reciprocal offset-vector tiles in various acquisition geometries. SEG Expanded Abstracts, 26, 61-65.