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8/18/2019 HAGI 2015 v04 the Effect of Seismic Resolution Enhancement by Sparse Layer Inversion on AVO and Inversion Ana…

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PROCEEDINGS

Joint Convention Balikpapan 2015

HAGI-IAGI-IAFMI-IATMI

5 – 8 October 2015

1

The Effect of Seismic Resolution Enhancement by Sparse Layer Inversion on AVO and

Inversion Analysis

Khairul Ummah1 , Hanif Widya Nugraha 2 , Raisya Noor Pertiwi1 , Herlan Setiadi1 , Erwinsyah 2

1Waviv Technologies2Pertamina EP

ABSTRACT

Increasing vertical resolution of seismic image

becomes a common practice in searching thin

layer reservoir and fracture zone analysis. Several

existing methods, such as Gabor Deconvolution,

Bandwidth Extension, Colored Inversion, Spectral

Blueing, and Sparse Spike Inversion, are aimed to

increase the seismic vertical resolution. Therecent method called Sparse Layer Inversion (SLI)

becoming more popular as it qualified to improve

the vertical and horizontal resolution of seismic

images.

SLI is performed by using Basis Pursuit Inversion

(BPI) to obtain the seismic reflectivity of migrated

stack and gather image. This reflectivity widens

the band spectrum up to Nyquist frequency. In

this study, we demonstrate the effect of

integrating the resulting SLI reflectivity in

reservoir characterization at sand-reservoir class

III AVO in area "X”. The reservoir characterization

includes seismic attribute, AVO/AVA and

inversion analysis are performed after the

application of SLI. Result shows that SLI gives a

competent foundation for resolution

enhancement. Integrating the SLI in reservoir

characterization procedure thus provides a

promising benefit to improve the accuracy in the

identification of thin layer and reservoir, as well

as in delineating the structural and stratigraphic

features.

INTRODUCTION

Identification of thin layer reservoir is commonly

in the exploration problems as the thickness of the

layes are beyond the seismic resolution. There are

various seismic enhancement method where most

of the strategies are classified as cosmetic

enhancement. Sparse Layer Inversion (SLI) use an

inversion strategy which utilizes the key that any

local earth impedance structure can be

represented by superposition of a limited number

of layers, and thus, the seismogram can be

represented locally as the superposition of a

limited number of layer responses as well (Chen

et al., 2001; Zhang, 2008; Zhang and Castagna,

2011). The resulted inversion would then assumea low number of layers that can be parameterized

to resolve thinner layers. With increasing seismic

resolution, it will also enhance the quality of

investigation geological features such as

faults/fractures which are important to be

considered in basement exploration and

development.

However, improper or aggressive seismic

enhancement application can alleviate more

problems rather than revealing important

information. With this respect, as a new

alternative product in estimating the thin bed

reflectivity, the resulting SLI product should be

tested in terms to understand its consistency with

the interpretation or hypothesis.

In this study we use 2D seismic gather and well

data in order to understand and evaluate the

effect of Sparse Layer Inversion in seismic section.

We extend the evaluation to understand the

integration of SLI with extended geophysical

analysis, including seismic attributes, seismicinversion and AVO/AVA analysis.

DATA AND METHOD

The SLI process is first tested in a 2D post-stack

data. The inverted reflectivity is then convolved

with a bandpass-filter to produce a band-limited

seismic section. As in the post-stack case, the SLI


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process is also tested in the seismic gather. It is

then bandpass-filtered before it has to be

transformed into angle gathers.

Using the resulting reflectivity given by SLI for thepost-stack data, we briefly describe the SLI

influence in interpretation. We also evaluate its

correlation with log data, and its performance

whilst being integrated with attribute process. We

also carry inversion analysis by using the resulting

reflectivity gather to understand the lateral

distribution reservoir, in terms of its thickness.

Second, to understand the SLI performance in

seismic gather, we implement the resulting

reflectivity for predicting Bottom Reservoir

reflector by using AVO/AVA analysis. This

procedure is carried to understand the lateral

distribution of top and bottom reservoir.

RESULT AND DISCUSSION

1. Post-Stack Application

Using post-stack data, SLI is performed to resolve

thin layers, and to enhance the stuructural and

stratigraphic information. Examples in Figure 1

and Figure 2, shows that with improving the

frequency bandwidth of the original data in thebandpassed-reflectivity section, it is possible for a

reflector to be represented by extra reflectors/

cycles, allowing the thin layers to be resolve. This

condition is valuable for interpreter to map subtle

onlaps and offlaps, and to identify discontinuities

of the reflector. Notice that the geological event

are more apparent after the SLI process compare

to the original image or the resulting seismic

preconditioning (Figure 2). With respect to the

well log data, we investigate that the AI log is

easier to correlate with the seismic qualitativelyusing the reflectivity image compare to the

original seismic. The correlation coefficient

between the synthetic and the seismic data

increases after the application of SLI. The

correlations given by original seismic and after the

SLI are 0.293 and 0.445, respectively (Figure 3).

Given from the well information, there are two

reservoirs identified within this area. The first

reservoir interval called the Upper Reservoir. The

Upper Reservoir represents thin layer sandstones

with thickness approximately 5 meters. Thesecond reservoir interval called the Lower

Reservoir. The Lower Reservoir interval is

identified with thickness of 12 meters.

The reflectivity given by the top and the bottom of

the Lower Reservoir at depth around 1175 ms are

sharper in the SLI section compare to the original

section (Figure 4a and Figure 4b). This also applies

for the reflectivity of the Upper Reservoir or the

thin layer which lies at depth of 1110 ms. By

assuming the dominant frequency of 50 Hz and

velocity of 2300 m/s around this depth interval in

the original seismic section, the thickness that can

be resolved is around 11.5 meters. Eventhough

the thin layer is far below the tuning thickness, the

SLI process are able to define this interval for up

to 5 meters. The application of SLI therefore

allows the horizontal and vertical resolution to

improve. The previous investigation that is

associated with stationary wavelet transform

(SWT) process has also allows the given thin layer

to delineate properly as the resolution is

enhanced (Ummah et al., 2013).

Further, to have a better understanding of the

hydrocarbon bearing location, interpreters usually

integrate the seismic data with attribute analysis.

By the application of sweetness-attribute in SLI

seismic section (Figure 4c and Figure 4d ), the

location of the hydrocarbon bearing interval of

Lower Reservoir is more accurately defined

compare to the sweetness-attribute section given

by the original seismic. With no hydrocarbon

content in the Upper Resevoir, the interval is given

with dimmer sweetness amplitude in comparison

to the Lower Reservoir.

Apart from the application in attribute analysis,

we also challenge the SLI to help resolving the

sand thickness for the Upper and the Lower

Reservoir, as it is beneficial in reservoir

characterization. Using the post-stack seismic


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data, we produce a model-based P-impedance to

understand the corresponding reservoir thickness

with impedance sections. The delineation of the

Lower Reservoir interval is improved in the P-

impedance section that involves SLI process in thebeginning (Figure 5a and Figure 5b). It is clear that

the improvement in structural and stratigraphic

delineation of the reservoir in the P-impedance

section is coherent with the improvement of the

resolution of the seismic input. However, the

Upper Reservoir interval appears to be subtle. The

reason, perhaps, is due to its thickness which is far

below the tuning thickness.

2. Seismic Gather Application

Not only in the post-stack data, the application of

SLI process can be extended for seismic gather

data. Here, the SLI process is performed in the

offset basis, before the gathers are transformed

into the angle gathers. Using the resulted angle

gathers (Figure 6a and Figure 6b), we then create

AVA crossplot. The crossplots involved the Super

Gather angle data given by 10 CDPs around the

well for angles of 0 to 35 degrees along the top

and bottom of the Upper and Lower Reservoir.

For the case before and after SLI application, theAVA crossplot illustrates the top of the Upper

Reservoir to decrease in amplitudes as the angle

increase where the zero-angle amplitude value is

positive (Figure 7 ). Further, with respect to this

information, it is known that the top of Upper

Reservoir is categorized as AVO Class I sandstones

where the intercept is given by positive

amplitudes while the gradient is given by negative

amplitudes. For the case of the top of Lower

Reservoir target, the AVA crossplot illustrates that

the amplitude is also decrease as the angle

increase where the zero-angle amplitude value is

negative (Figure 8). Therefore, the Lower

Reservoir is characterized was AVO Class III

sandstones, where the intercept and the gradient

is given by negative amplitudes.

After generating the intercept and gradient

seismic sections, we would like to predict the

lateral distribution of the top and bottom of Upper

and Lower Reservoir around the well within the

intercept cross-section. By assigning the

information given by the previous AVO/AVA

crossplot in the previous investigation, we definea zone that most typically identifies top and

bottom of Upper and Lower Reservoir

respectively (Figure 9 and Figure 10). As the zone

defined according to the previous investigation,

neither the top and bottom of the Upper Reservoir

as well as the top and bottom of the Lower

Reservoir are not clearly identified along the

intercept cross-section. This applies for both

procedures, before and after SLI process. In this

case, SLI process does not allow the horizontal

and vertical resolution to improve.

CONCLUSION

The application of SLI helps the interpreter to

extract meaningful information from the seismic

data. It has a promising benefit for thin layers/

reservoir identification, structural and

stratigraphic interpretation. Integrating the SLI

with extended geophysical analysis in the post-

stack data, such as attribute and inversion, are

proven to be an advantage to lessen the

misinterpretation. Apart from this advantage, onesignificant benefit deriving reflectivity using SSI is

that the interpreter able to filter it back to

bandwidth that is higher than the input data.

Apart of its success in post-stack seismic

application, the test of SLI application in seismic

gather that utilizes AVO/AVA is not proven to be

beneficial.

REFERENCES

Chen, S. S., Donoho, D. L., Saunders, M. A., 2001,

Atomic decomposition by basis pursuit,Society for Industrial and Applied

Mathematics, 43, 129–159.

Ummah, K., Condronegoro, R., Sutjiningsih W.,

Setiadi, H., Putri, S.A., 2013, Direct

hydrocarbon indicator for thin layer reservoir


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after resolution enhancement, Proceedings

HAGI-IAGI Joint Convention Medan 2013.

Zhang, R., 2008, Seismic reflection inversion by

basis pursuit, Doctoral dissertation atUniversity of Houston.

Zhang, R., Castagna, J., 2011, Seismic sparse-layer

reflectivity inversion using basis pursuit

decomposition, Geophysics, 76, P. R147–

R158.


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Figure 1 (left ) Original seismic (right ) Seismic after Waviv preconditioning. The images are overlaid with

the facieson the left-side and AI logs on the right-side.

Figure 2 (left) After SLI process (right) Bandpassed reflectivity section (5-10/50-70 Hz). Each image is

overlaid with the facies and AI logs.


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Figure 5. The P-impedance given by (a) original seismic and (b) after SLI. The resolution of the reservoirinterval is enhanced in (b).

Figure 6. Pre-stack angle gather display of original seismic (a) after SLI process (b) in the reservoir area. The

thin layer interval is named the upper reservoir target with total thickness of 5 meters while the lower

reservoir target has a total thickness of 12 meters.

Reservoir Zones Reservoir Zones

Thin Layer Interval Thin Layer Interval

Upper

Reservoir

Target

Lower

Reservoir

Target

Angle-Gather Seismic SLI Angle-Gather Seismic

(a) (b)


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Figure 7. Using pre-stack seismic data right in the well position, the upper reservoir target is represented by

Class 1 AVO. (left ) Amplitude versus Angle crossplot (right ) Intercept vs Gradient. Both crossplot utilizes aSuper Gather data given by 10 CDPs around the well for angles of 0 to 35 degrees along the Top and Bottom

Upper Reservoir Target.

Amplitude versus AngleOriginated from Angle-Gather Seismic

Top Upper Reservoir Target

Bottom UpperReservoir Target

Amplitude versus Angle

Originated from SLI Angle-Gather Seismic

Intercept vs Gradient Originated from Angle-Gather Seismic

Intercept vs Gradient


(a) (b)

(c) (d)


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Figure 8. Using pre-stack seismic data right in the well position, the lower reservoir target is represented by

Class 3 AVO. (left ) Amplitude versus Angle crossplot (right ) Intercept vs Gradient. Both crossplot utilizes aSuper Gather data given by 10 CDPs around the well for angles of 0 to 35 degrees along the Top and Bottom

Upper Reservoir Target.

Top Lower Reservoir Target Bottom Lower Reservoir Target

(a) (b)

(c) (d)

Amplitude versus AngleOriginated from Angle-Gather Seismic

Amplitude versus Angle


Intercept vs Gradient Originated from Angle-Gather Seismic

Intercept vs Gradient



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Figure 9. The lateral distribution of the top and bottom Upper Reservoir around the well given by theintercept and gradient crossplot given by intercept and gradient seismic sections. The intercept and gradient

seismic sections are extracted from (a) original seismic (b) SLI processed seismic. Zones are interpreted by

following the previous AVA well information. The interpreted zones of the top and bottom Upper Reservoir in(a) and (b) are illustrated respectively in the intercept cross-sections (c) and (d).


Bottom Upper Reservoir Target


Bottom Upper Reservoir Target

(a) (b)

(c) (d)

Intercept Originated from PrestackSeismic Section

Intercept Originated from SLI Prestack Seismic Section

Intercept vs Gradient Originated from Prestack Seismic Section

Intercept vs Gradient Originated from SLI Prestack Seismic Section


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Figure 10. The lateral distribution of the top and bottom Lower Reservoir around the well given by theintercept and gradient crossplot given by intercept and gradient seismic sections. The intercept and gradient

seismic sections are extracted from (a) original seismic (b) SLI processed seismic. Zones are interpreted byfollowing the previous AVA well information. The interpreted zones of the top and bottom Lower Reservoir in

(a) and (b) are illustrated respectively in the intercept cross-sections (c) and (d).

Top Lower Reservoir Target

Bottom Lower Reservoir Target

Top Lower Reservoir Target

Bottom Lower Reservoir Target

(a) (b)

(c) (d)

Intercept vs Gradient Originated from Prestack Seismic Section

Intercept Originated from PrestackSeismic Section

Intercept Originated from SLI Prestack Seismic Section

Intercept vs Gradient Originated from SLI Prestack Seismic Section

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