Seismic fault enhancement and skeletonization Jie Qi*, Fangyu Li, Bin Lyu, The University of Oklahoma; Bo Zhang, The University of Alabama;

Oluwatobi Olorunsola, and Kurt Marfurt, The University of Oklahoma

Summary

Faults play important roles in reservoir modeling and

characterization. In general, one convenient way to map

faults, is that computing coherence from 3D seismic

dataset. Although coherence attribute has significant

improvement at fault detection, it is also sensitive to other

structural discontinuous and incoherent anomalies, which

are classified to noise, and are difficultly separated from

faults by automatically picking. In this project, we

introduce a 3D fault skeletonization workflow. We

compute coherence attribute based on eigenstructure

method, which can highlight most discontinuous anomalies.

The highlighted anomalies include faults and other

negligible discontinuities. The fault dip magnitude and dip

azimuth can be calculated by Eigen analysis of windowed

voxels. Using the fault dip magnitude and dip azimuth, we

can trace faults along dip and azimuth with a 3D window in

attribute volumes, then interpolate the center candidate

point of the analysis window onto the window surface. By

comparing interpolated points with the candidate point, we

can reject the lower anomalous candidate point of one

analysis window, which we define as other negligible

discontinuities, and extract highest center candidate points

which we define as the fault anomalies.

Introduction

For large dataset, faults hand-picking by interpreters is time

consuming. Before doing automatically fault picking in 3D

seismic data, it is necessary to generate high resolution

edge detecting attributes. The published literatures on

computer-assisted fault analysis including coherence,

curvature, and dip magnitude, are seismic attributes based

methods. Coherence is routinely used to detect structural

discontinuities in 3D seismic data (Gersztenkorn and

Marfurt, 1999). In addition, coherence can also be used in

multiattribute seismic facies analysis (Qi et al., 2016).

However, dipping reflectors and other types of structural

discontinuities are also highlighted in coherence.

Distinguishing the mixed high anomalies features from

faults in coherence is a big problem for automatically

picking techniques. For other relative attribute-based fault

analysis, Qi et al., (2014) introduced several discontinuous

attributes in fault interpretation, and by data conditioning

workflow to increase resolution of attribute images and

efficiently extract subtle discontinuous geologic feature

from seismic attribute images. Zhang et al., (2014)

proposed an edge-detection algorithm to build fault planes.

Wu and Hale, (2015) proposed a fault imaging method to

map intersecting faults based on Hale (2013) fault

construction technique.

Figure 1. Workflow illustrates the steps used in our 3D fault skeletonization workflow.

In this project, we would like to introduce a 3D fault

skeletonization workflow (Figure 1). Although coherence is

kindly noisy, using smoothing techniques can fairly

increase the resolution of a fault image. Barnes (2006)

employed steeply dipping filter to keep fault anomalies and

suppress other seismic discontinuity. Based on Barnes

(2006) method, Machado et al., (2016) applied the

Laplacian of a Gaussian (LoG) filter to sharpen fault

features within coherence. Our work starts at using fault

enhancement method (Machado et al., 2016) to increase

resolution of the coherence attribute. Then, we generate a

cubic window to trace fault features along fault dip and

azimuth. The center point in one analysis window is

defined as candidate fault point. Using bilinear

interpolation method, four points are interpolated from the

center point, in which two points are on the line that is

parallel to the fault plane and another two points are on the

line that is perpendicular to the fault. By comparing the

candidate fault point with the two interpolated points that

on the perpendicular direction, if the center point is larger

than the two interpolated points, we can extract the center

point as the point that located on the fault.

Method

Key factors of imaging faults include a good edge detecting

attribute, a smoothing filter, an effective scanning method

and a reliable interpolating tool. Coherence as a

discontinuous detection attribute, can highlight faults and

channel edges. However, coherence usually covers by

incoherent noise and also highlights footprints as high

anomalies, which sometime make coherence result

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chaotically. In order to automatically skeletonizing faults,

we should firstly suppress these incoherent noise and

enhance faults trends. Then by knowing fault dip

magnitude and fault dip azimuth, fault image points can be

detected along fault plane.

Figure 2. (a) One fault point in a 3*3*3 window associated with

fault dip magnitude and fault dip azimuth; (b) bilinear interpolated four points. Line S1-S2 is parallel to the fault plane, and S3-S4 is

perpendicular to the fault plane.

Figure 2a indicates a 3*3*3 cubic analysis window, and the

center red point is the fault candidate point. For a fault

plane (indicated by green square), fault dip magnitude θ

and fault dip azimuth ψ can be calculated by eigenvector

analysis (Machado et al., 2016). Fault dip magnitude θ and

fault dip azimuth ψ are used to describe fault plane

orientation and dipping angle. Generally, the fault dip

magnitude θ and dip azimuth ψ of each point on the same

fault plane should be similar. After computing θ and ψ, we

apply bilinear interpolation to the center fault candidate

point and interpolate the fault point onto four different

surfaces shown in Figure 2b (Surf1, Surf2, Surf3, Surf4).

Interpolated points S1 and S2 are on the line that is parallel

to the fault plane, while interpolated points S3 and S4 are

on the line that is perpendicular to the fault plane.

Compared with other 2D fault skeletonization method, one

can skeletonize fault not only from horizontal plane (time

slice), but also from vertical plane (vertical section). Fault

can be thinned and smoothed. Other incoherent anomalies,

such as channels, and mass transport complexes, that

having disorderly dip magnitude and dip azimuth

anomalies, will be rejected by this process.

Figure 3. Vertical section (a) and time slice (b) at t=0.75ms with

seismic amplitude.

Application

We apply our 3D skeletonization method to the deep water

Gulf of Mexico dataset. Faults, turbidites, mass transport

complexes and channels, are often seen in GOM, which

could be shown as highly incoherent anomalies in

coherence. In seismic vertical section (Figure 3a), we

interpret four major faults across mass transport complexes.

However, faults are not well recognized on seismic time

slice AA’. In order to getting better fault map, we first

compute coherence along structural dip based on

eigenstructure method. Figure 4a shows coherence that can

highlight most discontinuous anomalies, such as faults,

flexures and other incoherent noises. Although interpreters

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can easily pick most major faults for geologic modeling

using coherence map, there are still a lot of work to find out

all faults on a big dataset. Because of other incoherent

discontinuous anomalies effects, resolution of coherence is

not good enough to automatically segment faults. Thus, we

apply fault enhancement filter to suppress incoherent noise

on coherence.

Figure 4. Time slice at t=0.75s through (a) coherence and (b) fault

enhancement result. Note that fault enhancement result can show

better fault image.

Figure 5. Time slice at t=0.75s through (a) fault dip magnitude and (b) fault dip azimuth.

Figure 6. Time slice at t=0.75s through (a) fault skeletonized

image before applying dip filter and amplitude filter; (b) final fault skeletonization result with dip filter and amplitude filter; (c) final

fault skeletonization co-rendered with fault dip azimuth.

Figure 4b is fault enhanced coherence based on the fault

enhancement workflow introduced by Machado et al.,

2016. The method is that using Laplacian operators to

smooth and sharpen images. The Laplacian of Gaussian

filter is widely used in image processing. The filtered

images are directional preservation and highlighted the

edges between sharpen pixels. Compared with original

coherence, fault enhanced coherence shows more smoothed

and more continuous faults. However, fault enhanced

image highlights other stick-like anomalies, which are not

belong to faults. For solving this issue, we compute local

points fault dip magnitude and fault dip azimuth, then

interpolate fault points onto neighboring planes. By

comparing fault points with interpolated points, the real

fault points can be segmented.

Figure 5a and 5b show the fault dip magnitude and the fault

dip azimuth, respectively, which are computed based on

eigenvector analysis (Barnes, 2006 and Machado et al.,

(2016)). All the image points on the same fault plane would

have similar fault dip magnitude and fault dip azimuth

values. Fault planes are fairly continuous on fault dip

magnitude and fault dip azimuth. However, other

incoherent noise may have disorderly fault dip magnitude

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and dip azimuth. Note that most faults that are around 80o

dipping and the faults orientation at -150o direction.

Figure 6a shows the fault skeletonized image, based on our

3D skeletonization method. Comparing with original

coherence and fault enhanced coherence, the skeletonized

image shows a higher signal-to-noise ratio fault image.

Faults in Figure 6a are much thinner and clearer than

Figure 4a and 4b. Channel edges, mass transport

complexes, and turbidites that associated with

discontinuous dip magnitude and dip azimuth are

smoothed. Incoherent noise is smeared after fault

skeletonization, but the noise is still existed. Generally,

most kinds of incoherent noise are disorderly in fault dip

magnitude and fault dip azimuth. Additionally, the

incoherent noise usually shows as lower amplitude than

faults or other structural discontinuities. Figure 6b is the

fault skeletonized image after dip filter and amplitude filter.

Note that some low dipping angle anomalies and low

amplitude anomalies are disappeared. Figure 6c shows the

final skeletonized image co-rendered with fault dip

azimuth. Note that faults are segmented from other type

structural discontinuities and incoherent noise. Fault

azimuth of these six major faults (indicated by red arrows

in Figure 6c) are at around -150o. Figure 7 shows vertical

section through seismic amplitude co-rendered with

skeletonized image. Note that skeletonized image shows

faults that are similar as picked in Figure 2a.

Figure 7. Vertical section through with seismic amplitude co-

rendered with skeletonized fault result. Note that skeletonized fault shows the same fault trends as Figure 2a.

Compared coherence (Figure 8a) with skeletonized image

that co-rendered with seismic amplitude (Figure 8b), the

skeletonized fault image preserves faults information, but

remove all other negligible discontinuities, such as

channels, mass transport complexes, and turbidites. What’s

more, faults after skeletonized are much thinner and more

sharpen.

Conclusions

We have proposed a 3D fault skeletonization method to

skeletonize and segment fault images. Firstly, we computed

coherence which can highlight faults and other

discontinuous structures. By applying fault enhancement

workflow, fault images are enhanced. Then, we compute

the fault dip magnitude and the fault dip azimuth. By

interpolating the candidate point along fault dip magnitude

and fault dip azimuth, if the image point on one fault plane

are larger than the interpolated points that is perpendicular

to the fault plane, we defined the point as fault points. If the

candidate point is smaller than the interpolated points, we

defined as other negligible discontinuity and removed it.

After fault skeletonized, faults are much thinner and more

sharpen. What’s more, other structural discontinuities, such

as channels, mass transport complexes, and turbidites, were

also smoothed. Applying dipping filter and low amplitude

filter, we can reject all other low amplitude and low

dipping angle incoherent noise. The final skeletonized

image can preserve faults information, but suppress other

information.

Figure 8. Time slice at t=0.75s through (a) coherence, and (b)

seismic amplitude co-rendered with skeletonized fault. Note that skeletonized faults are more continuous and clearer than the faults

in coherence.

Acknowledgements

We thank the PGS for providing the data volume for use in

research and education. We also thank the sponsors of the

OU Attribute-Assisted Processing and Interpretation

Consortium and their financial support.

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EDITED REFERENCES Note: This reference list is a copyedited version of the reference list submitted by the author. Reference lists for the 2016

SEG Technical Program Expanded Abstracts have been copyedited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web.

REFERENCES Barnes, A. E., 2006, A filter to improve seismic discontinuity data for fault interpretation: Geophysics,

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volumetric fault image: Interpretation (Tulsa), 4, SB51–SB61, http://dx.doi.org/10.1190/INT-2015-0104.1.

Gersztenkorn, A., and K. J. Marfurt, 1999, Eigenstructure based coherence computations as an aid to 3D structural and stratigraphic mapping: Geophysics, 64, 1468–1479., http://dx.doi.org/10.1190/1.1444651.

Hale, 2013, Methods to compute fault images, extract fault surfaces, and estimate fault throws from 3D seismic images, Geophysics, 78, no. 2, O33–O43, http://dx.doi.org/10.1190/geo2012-0331.1.

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Qi, J., B. Zhang, H. Zhou, and K. J. Marfurt, 2014, Attribute expression of fault-controlled karst — Fort Worth Basin, Texas: A tutorial: Interpretation (Tulsa), 2, SF91–SF110, http://dx.doi.org/10.1190/INT-2013-0188.1.

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