Fracture Prediction Using Low Coverage Seismic Data in Area of Complicated Structures

1. 1
Fracture Prediction Using Low Coverage Seismic Data in Area of Complicated Structures
*Yan Li, Deying Zhong, Haiquan Wang, Jun Li, Mark Mo, Jiaqi Wang, LandOcean Energy Services Co., Ltd
*Mario A. Prince S., Daniel Rojas, Petroleos Del Norte S.A.
Summary
This paper presents an innovative integrated workflow
and methodology which can help to improve the
resolution and accuracy of characterizing a limestone
fractured reservoir based on 3D seismic data with low
fold coverage in complicated structure area, Colombia.
This integrated study used advanced azimuthal seismic
processing technique and leading fracture prediction
technique based on all available data.
A new processing sequence and method was created
and applied to overcome the shortage of seismic data,
which is the prior condition to ensure the accuracy of
fracture prediction in limestone reservoir. The fracture
orientation and intensity can be derived from attribute
variation of different azimuthal seismic data, guided by
seismic forward modeling. Comparing with the existing
well data, the fracture intensity and orientation
prediction are extremely consistent with FMI log. It
indicates that fracture prediction technique based on 3D
P-wave seismic data can get reliable results based on
low coverage seismic data in areas with complicated
structure.
Introduction
The study area is located in the northeast of Colombia,
between middle and east Cordillera Mountain. Because
of the special tectonic background, the strike-slip fault
developed very well and divided the whole study area
into different fault blocks (Fig. 1).
The reservoir is limestone of the Lower Cretaceous with
very low porosity and permeability. The exploration
experience has proved that the fracture development is a
crucial factor to understand storage space, directions of
flow and areas of commercial oil production.
This paper attempts to present an innovative approach
to do azimuthal seismic data division & processing, and
characterize fractured reservoirs based on 3D P-wave
seismic data. The prediction result can describe the
spatial fracture property of target layer in a sense of half
quantity to quantify, which gives us an integrate
understanding of reservoirs. It proved that accurate
fracture prediction result can be got by using
appropriate method of seismic processing to get over
low fold number of seismic data and complicate
structure characteristic.
Core idea of methodology
The theory that fracture development can cause
amplitude variation with offset and azimuth (Shen et al.,
2002) has been universally accepted by now.
It has been proved that thin pores (fractures) have much
greater effects on velocities than rounded pores at the
same porosity and that a very low porosity (less than
0.01 percent) of thin pores could decrease the P- and S-
wave velocities (Kuster and Toksoz, 1974) and
generates seismic anisotropy. Further, Study shows that
seismic waves suffer severe scattering while
propagating through fractured reservoirs which are
Fig.2 Fracture type Reservoir integrate analysis workflow
flow
Data loading and quality control
Seismic data Well data
CMPgather
Azimuthal gather
division and extraction
Interpolation
Azimuthal gather output
PSTM
Post-stack
seismicdata
Azimuthal seismic data
Attribute calculation
Fracture intensity
Fracture orientation
Log data
FMI
S-wave
Rock
physical
forward
modeling
Fig. 1 Structure map of target layer
Well 1

2. Fracture Prediction Using Low Coverage Seismic Data
2
characterized vertically aligned fractures (Lynn et al.,
1995). That means how to detect azimuthal amplitude
variation with azimuth and relate it with fracture
properties is essential issue of predicting fracture
development.
To identify the effects of fracture properties on the
azimuthal amplitude variation, there are two questions
should be figured out based on special geological
condition of study area:
1) How to get precise azimuthal seismic volumes;
2) How to detect azimuthal amplitude variation and
related it with fracture properties.
Seismic data processing
As we know that the azimuthal seismic volumes are
basement of doing fracture prediction, which can affect
fracture prediction result directly. So, how to process
and use the 3D seismic data extensively is the essential
question to ensure the accuracy of fracture prediction
result.
1. Difficulties
Aimed at CMP gathers of study area, there are two key
difficult points should be paid attention to during
azimuthal trace gathers division and processing:
The offset-azimuth cross plot of the CMP gathers
reveal that the fold numbers are relatively low and
uneven (0~26), which can cause the energy
difference or empty trace of azimuthal seismic data.
The tectonic characteristics of study area are very
complex (Fig.1). How to keep the accurate structure
geometry feature of each different azimuthal
seismic volumes during seismic processing of is
another very important and tough issue.
2. Solutions
In order to overcome these two key problems, a new
seismic processing procedure was credited and applied
(Fig. 2).
The interpolation was applied to the CMP gather
which can make the energy uniform in the whole
area. The energy difference and empty trace
problem can be solved;
Three azimuthal gathers are divided reasonably and
output (Fig.4);
PSTM was applied to each different azimuthal CMP
gathers which can ensure the structure geometry
accuracy and good image;
Stack and output three azimuthal PSTM datasets.
3. Results
1) Azimuthal gather division
According to real condition of CMP gathers in study
area (low fold number, medium offset), the CMP
gathers only can be divided into three different azimuth
gathers (0~65°, 60~120°, 120~180°) in offset range of
150m~2800m (Fig. 4), aiming to unify the energy of
each azimuth gather.
2) PSTM
In order to enhance the image quality of each azimuthal
seismic volumes and keep propriety structure geometry,
an advanced PSTM method named bending ray tracing
Kirchhoff integral migration running on GPU/CPU
Co-Parallel computing system was applied to do PSTM.
Figure 5 illustrates that sections of different azimuths
after PSTM show similar waveforms and geometric
characteristics compared with whole stack result, which
confirms that the method and parameters are applicative
and reasonable to this study area.
Fig. 4 Offset and Azimuth crossplot analysis
Fig.3 The layout analysis result of CMP trace gathers
(Color presents fold number)

3. Fracture Prediction Using Low Coverage Seismic Data
3
3) Energy uniformity
Energy uniformity is another very important factor to
verify the azimuthal seismic data processing, because
the essential of fracture prediction is just to detect the
energy variation between different azimuthal seismic
data.
The following figures show that the amplitude energy
of each azimuth seismic data is in the same range
(-40~40). There are no empty traces through the whole
study area. The quality of azimuth seismic data is good
enough to do anisotropy detection.
Fracture characterization
According to the core idea of fracture prediction based
on 3D P-wave seismic dataset, the most sensitive
attribute has to be selected optimally on the foundation
of reliable azimuthal seismic data. As to this project, the
relative impedance was chosen as dynamic parameter to
detect fracture development.
In order to describe and understand dynamic
parameter’s variation of different azimuthal seismic
volumes directly and visually, an anisotropy ellipse as a
vector, which can presents fracture orientation and
intensity, can fitted by different values of each azimuth
in same incidence angle (Fig.7).
Figure 8 is 3D map of fracture density and orientation
prediction result for target layer. The direction of little
bars represents fracture orientation, and color represents
fracture intensity. The rose diagram is statistic result of
fracture orientations within a specified area. It
illustrates that there are two main orientations
developed, NE~SW is the primary one and NW~SE is
the second one.
Fig.7- Anisotropy vector calculated by ellipse fitting
(The axis: orientation of anisotropy; the ratio of major and minor
axis: intensity of anisotropy.)
f2
f5
amplitude
Incidence angle (offset)
f1
f3
f4
N
Fi, azimuth
f2
f5
amplitude
Incidence angle (offset)
f1
f3
f4
N
Fi, azimuth
f2
f5
amplitude
Incidenceangle(offset)
f1
f3
f4
N
Fi,azimuth
a) 0˚~65˚ azimuth stack-migration section
b) 60˚~120˚ azimuth stack-migration section
c) 120˚~180˚ azimuth stack-migration section
Fig.6 Seismic sections of different azimuth seismic data
0~65˚ azimuth stack-migration (Inline 169) section
60~120˚ azimuth stack-migration (Inline 169) section
120~180˚ azimuth stack-migration (Inline 169) section
a) b）
c) d)
Fig. 5 Geometric property comparing with different azimuth
seismic data
A) PSTM volume, B) 0~65°azimuth seismic data, C)
60~120°azimuth seismic data, D) 120~180°azimuth seismic data
0~65 °azimuth seismic data
60~120°azimuth seismic data 120~180°azimuth seismic data

4. Fracture Prediction Using Low Coverage Seismic Data
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Result verification
Seismic data has wider spatial coverage than the well
data, while well logs have high vertical resolution.
Seismic data can make up the shortcomings of well data,
and well data can be taken as the prior condition to
verify fracture prediction results.
There are only one well has been drilled in study area
and it have FMI in target layer. The comparison result
of orientation from well data and seismic data can
approve that the orientation from pre-stack anisotropy
detection results is extremely similar with FMI data
(Fig. 9), and the fracture orientations derived from
seismic data can reflect the real situation of fracture
development in study area.
Conclusions
This paper attempts to demonstrate a credible method to
predict fractures by using 3D P-wave seismic data with
low coverage seismic data in complicated area. It
proved that through applying appropriate seismic
processing method and procedure, the bad influence on
accuracy of fracture prediction can be decreased as
much as possible, which caused by the shortcoming of
seismic data.
The comparison result between prediction result and
FMI log has definitely proved the accuracy and
credibility of fracture intensity and orientation results. It
not only indicated the application effect of fracture
prediction technique based on 3D P-wave seismic data,
but also proved that through appropriate processing
method, the seismic data with low coverage can be used
to do fracture analysis.
Acknowledgements
The author wishes to thank Mario Prince and Daniel
Rojas of Petroleos del Norte S. A. for their great
support of this project and permission to publish this
article. It is appreciate that Dr. Qin Gangping revising
this article.
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b) 3D view of fracture orientation at well location
Fig. 9 Fracture orientation prediction result verification
a) Fracture orientation statistical result comparison
Fig.8 3D map of fracture intensity and orientation for
target formation