American Oil and Gas Reporter - November 2017 - 42

SpecialReport: Oil & Gas Computing
The role of seismic data and geophysics in shale plays has
evolved tremendously over the past five years. Today, robust
anisotropic depth imaging methods provide a pathway to ensure
that lateral wells are positioned accurately and that wells are
steered with precision. Multiazimuth seismic inversion procedures
now are routinely deployed to locate areas of natural fracture
intensity and to provide measures of fracture and stress orientation.
The reliability of these methods is strengthened when the data
are properly imaged in situ and in depth, such that the azimuthal
data are properly recovered by the inversion.
Oil and gas operators also have learned to appreciate the role
of structure controls (e.g., faults) and their controlling influence
on the compartmentalization of shale formations and productivity
of field operations. While major faults easily can be identified
and interpreted with traditional seismic reflection processing
and imaging methods, new methods are required to uncover
smaller faults from surface-recorded data. The seismic signature
from these smaller faults are generally of much lower energy
than the reflected energy returned from large faults and formation
boundaries. These small faults are local discontinuities that act
as point sources for the generation of diffraction energy.
Two-Fold Challenge
Consequently, seismic imaging methods that can preserve
and isolate diffraction energy provide an approach for imaging
small-scale subsurface geologic objects and discontinuities such
as faults and even fracture systems. While there are numerous
methods for carrying out diffraction imaging, the challenge for
all of them is two-fold:
* To properly map (image) the low energy diffraction data
recorded at the surface of the earth into in situ prestack data organizations in depth without further attenuating or removing
the diffraction energy; and
* To search for the diffraction energy and separate it from
the higher (specular) energy reflection events in an automated
and efficient manner.
The first challenge is accurately solved with a full-azimuth
depth imaging method using subsurface angle domain decomposition. While details of this method are beyond the scope of
FIGURE 1
Analyzing Full-Azimuth Directivity Gathers using
Deep Learning Workﬂow (Eagle Ford Dataset)
Full-Azimuth Directivity Gathers
Deep learning applied to full-azimuth
directivity gathers

Classification Set

Deep Learning Applied to Full-Azimuth
Directivity Gathers to Isolate Signal and
Noise Patterns
Specular Energy

this article, the method maps the fully recorded surface seismic
wavefield into continuous full-azimuth directivity (dip/azimuth)
components in situ for all subsurface image points. In doing so,
the fully recorded wavefield (specular and diffraction energy) is
preserved in the depth imaging process. More importantly, the
data are organized to facilitate analysis in different dominant
subsurface directions.
Traditional methods for searching these directivity data organizations for diffraction energy (addressing the second
challenge) include dip or energy filtering, where diffraction and
specular energies can have different signatures for both data
types. While these methods are generally intuitive to understand,
their ability to fully decompose the different image characteristics
is somehow limited by their coupling relations. Moreover, these
methods can require considerable human effort or test runs to
optimize the output diffraction image.
Machine learning provides an alternative approach to the
second challenge by enabling automatic classification and separation of wavefield patterns associated with different subsurface
shale features (faults, fractures and continuous events) and even
different styles of noise (e.g., ambient noise, acquisition footprint,
etc.).
The first stage of the process is the application of principle
component analysis (PCA) to the directivity gathers (along relatively small 3-D spatial windows) to isolate the principle directivities (wavefield patterns) associated with subsurface features
and various types of noise at each location. The goal is to
extract a few principle directions that describe the variability of
the data types in the subsurface. This has the added advantage
of providing a better geologic separation of seismic events
while reducing the huge dimensionality of the full dataset.
The second stage involves training a convolutional neural
network (deep learning) with the many geometric patterns (classifications) from the PCA outputs: back-projection of the
principle components to the image domain. Figure 1 shows the
deep learning concept applied to full-azimuth directivity gathers
of an Eagle Ford Shale dataset. Convolutional neural networks
are a popular platform for classifying (labeling) and predicting
differentiating shapes.

FIGURE 2
Fault Imaging using Deep Learning versus
Deterministic Classiﬁcation Methods
(Eagle Ford dataset)

Diffraction Energy

Image of all waveﬁeld types

Acquisition Noise

Use deep learning to separate complex
wavefields into constituent components

42 THE AMERICAN OIL & GAS REPORTER

Ambient Noise

Diffraction-weighted stack image

Deep learning fault classiﬁcation
American Oil and Gas Reporter - November 2017

Table of Contents for the Digital Edition of American Oil and Gas Reporter - November 2017
