paper muhammad alwi dkk - seismic image enhancement using ssa bpi on stacked-delta reservoir -3

8/18/2019 Paper Muhammad Alwi Dkk - Seismic Image Enhancement Using SSA BPI on Stacked-Delta Reservoir -3

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Joint Convention Balikpapan 2015

HAGI-IAGI-IAFMI-IATMI

5–8 October 2015

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Seismic Image Enhancement using Singular Spectrum Analysis (SSA) and Basis Pursuit

Inversion (BPI) on Stacked-deltaic Reservoir.

Case Study: TGA field, Onshore Brantas Block, North East Java Basin.

Muhammad Alwi1 , Rian Novico

1 , Khairul Ummah

2 , Mokhammad Puput Erlangga

2 , Dythia Prayudhatama

2

1 Lapindo Bratas, 2 Waviv Technologies

Abstract

One of the common challenges for seismic interpreters in Indonesia is dealing with below seismic resolution of

thin layers reservoir with limited data availability, like what we have encountered on some seismic lines in

Brantas Block onshore area, especially on TGA field. TGA reservoir type is thin layer sand-shale interbedded

which deposited in fluvial deltaic system. The original seismic lines have poor quality. Surface problems when

acquisition, tuning and gas effect are some of major factors. It contributes in making the seismic image blurry

and hard to be interpreted in detail. Consequently, it is difficult to delineate reservoir thickness laterally,

analyze seismic internal character and stratigraphy.

To overcome such challenges, we came with alternative solution that is by using Singular Spectrum Analysis

(SSA) as a precondition then continued by applying a sparse layer inversion technique using Basis Pursuit

Inversion (BPI) method to the original post-stack of the seismic lines. The result shows that SSA and BPI works

excellent to reconstruct the high frequency seismic that missing on the old section. Furthermore, the technique

gives a significant image improvement, enhances the lateral continuity and vertical resolution, and consequently

gives us a more confidence in interpretation.

INTRODUCTION

TGA gas field are located in onshore area of Brantas

Block, East Java Basin and administrative area of East

Java province of Indonesia. TGA field has 5

production wells and covered by 14 seismic lines

which has variety of vintage. Most of the seismic lines

have low-to-medium quality. The seismic quality in

the reservoir level (0-1500ms) is quite low, as it is

caused by surface problems when acquisition. There

are thin layer reservoirs, which are below seismic

resolution or tuning. The existing gas effect has also

contributed to reduce the high frequency content in the

seismic data. The obstacles are exaggerated by the factthat acquiring new and proper seismic data with higher

resolution has not been an option yet from economic

point of view. Moreover, the surface condition has

become high-dense population, which trigger social

problems; the biggest issue in recording seismic

operation.

As we know, naturally seismic data contains both of

coherent and incoherent noises. Coherent noises

usually have special frequency range but more

challenging than incoherent noise. Besides that, tuningand gas effect is another obstacle that commonly

happens on the thin layer deltaic reservoir. The other

factor that may happen in TGA field are the faulting

with short-displacement and facies changes that hardly

detected by the existing 2D seismic lines.

Considering the technical and non-technical problems,

we tried to come up with seismic enhancement

technique to optimize the data that we already have.

Along with some conventional reprocessing programs,

we are also strained to study directly to the seismic

enhancement processing. In this study we applied a

sparse layer inversion technique using Basis Pursuit

Inversion (BPI) method. Two key lines that exactly

lies on the center of TGA closure, line 168 (S-Ndirection) and line 322 (W-E direction), are the objects

of study in this paper. These lines are old vintages that

acquired on 1991 & 1992. The expected image

enhancement can help the interpreters to understand

the stratigraphy of the area.

GEOLOGY

The North East Java Basin covers an area of

approximately 50,000 km2 from Central Java eastward

across East Java, the East Java Sea, Madura island and

the Madura Straits (Figure-1). The present dayvolcanic arc is the most prominent surface geological


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feature of East Java. South of the volcanic arc

Miocene and Oligocene rocks crop out in the Southern

Mountains. North of the volcanic arc the surface

geology shows two major anti-formal features (the

Kendeng Zone and the Tuban Ridge) that haveoutcrops of Miocene to Pleistocene rocks. Holocene

alluvial sediments cover the remaining area.

The Brantas Block is located in the East Java Basin

which initially developed as a result of the north-

westward subduction of the Australian oceanic plate

below the Sunda continent during Late Cretaceous

time. A major extensional tectonic system prevailed

during Early Tertiary time caused by complex

interactions among the Australian, Eurasian and

Pacific plates. This created extensional graben systems

that trended approximately northeast-southwest,

shifting to an east-west direction further to the south.

Reservoir Description

TGA field consists of numerous volcanic clastic sands

ranging in depth from 600 feet to 3000 feet.

Information from sidewall cores, logs, DST’s, and

RFT’s indicate there are 7 zones with potential oil and

gas reserves. The reservoirs are volcanic clastic sand

classified as lithic arkoses or feldspathic litharenites.

Clay content is variable and is dominated by smectite.

The reservoirs are generally poorly cemented.

Figure-2 shows the East-West cross section andcorrelation of the TGA wells. In a total distance of 5

km a large number of reservoir units exist in all

existing wells. This picture shows that reservoir levels

have good correlation between the wells. Faults are

indicated to exist and segmented TGA structure into

four segments. This segmentation will be discussed

later in the fault modeling section below.

Based on this correlation, it is believed that most

reservoir levels are widespread throughout the area.

Paleo-environmental correlation of the main reservoirs

F-10 and G-10 between TGA and the adjacent fields,

namely Carat and Wunut indicate that the reservoirswere deposited in the transitional environment (upper

to lower deltaic plain).

Depositional Model and Trap Description

The Pucangan Formation in TGA is volcanic clastic

sand, consisting of volcanic sandstone with some part

of volcanic lithic fragment. The Pucangan is volcanic

detrital in nature; some of the grains are loosely

cemented together (friable) and some are tight. The

volcanic sands are product of the old Arjuna arc in the

southern part of TGA that was deposited in the marine

environment or possible outer littoral environment.

This was indicated by the foraminifera which are

present in most sample analyzed in cutting and side

sidewall core samples of TGA wells. The shallow

sections of TGA (E to B reservoirs) are shallow

marine to inter-tidal channel.

In TGA, most of the rock is fine to coarse grained, and

moderately to well sorted with variable detrital

volcanic rock fragment (andesite). Most of the matrix

of sand consist of feldspar and heavy mineral like

magnetite, hornblende and augite with volcanic lithic

deposited under a deltaic channel setting. The trap

TGA structure is a four way dip closure with structural

trap mechanism for the B to E reservoirs and

stratigraphic trap mechanism for the F and G

reservoirs.

METHODOLOGY

In this study, we applied a combination of two

methods to the data. First, we applied a Singular

Spectrum Analysis (SSA) as data preconditioning to

enhance the S/N ratio and give better input for the next

process. Second, we applied Basis Pursuit Inversion

(BPI) to increase vertical resolution.

Singular Spectrum Analysis

Singular spectrum analysis (SSA) is a method utilized

for the analysis of time series arising from dynamical

systems. The method is used to capture oscillationsfrom a given time series via the analysis of the

eigenspectra of the so-called trajectory matrix. The

trajectory matrix is composed of multiple data views.

The singular value decomposition (SVD) of the

trajectory matrix can be used for rank reduction and

noise elimination. We apply SSA in the FX domain

and present a comparison with classical FX

deconvolution. The algorithm arising from SSA

analysis is equivalent to Cadzow FX noise attenuation,

a method recently proposed by Trickett (2008). It is

important to stress, however, that Cadzow filtering is a

general framework for noise reduction of signals and

images. Cadzow filtering is equivalent to SSA when

considering sinusoidal waveforms immersed in

additive random noise. The intention of this abstract is

to provide a simple explanation of the basic

assumptions made in SSA and its application to the

modeling of plane waves (Sacchi, 2009).

Basis Pursuit Inversion

Basis Pursuit Inversion (BPI) can be thought as 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


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as well (Chen, 2001; Zhang, 2008; Zhang, 2011). The

resulted inversion would then assume a low number of

layers that can be parameterized to resolve thinner

layers. In our study, we improve the BPI algorithm by

optimizing the searching process for the most suitableregularization parameter (! ) in order to obtain

optimum inverted reflectivity results with maximum

noise suppression. General algorithm of BPI relies on

the convolution model of seismic trace:

d = Gm + n, (1)

where d is the seismic trace, G is a dictionary of time-

shifted pre-defined seismic responses, and m are the

weights for these responses that result from

minimizing the objective function:

(2)

where the subscripts refer to the L2 and L1 norms,

respectively, and ! is a regularization parameter that

controls the sparsity of the solution. Basis pursuit

algorithm is used here to find the minimal L1 norm

least square solution by finding the solution that

minimizes the objective function in equation 2.

BPI requires a series of symmetrical and asymmetrical

layer reflectivity pairs over a pre-defined range ofthicknesses to be constructed. The dictionary of

seismic layer responses, G, is then produced by

convolving the series of symmetrical and

asymmetrical layer reflectivity pairs with the seismic

model. If top and base reflectors of a thin bed have

reflection coefficients of c and d , the reflections can be

represented as two impulse functions c!(t) and

d !(t+n"t) where n"t is time thickness of the thin bed

and "t is the seismic sample rate. The reflector pair

can be further decomposed, using dipole

decomposition (Puryear and Castagna, 2008), into

unique odd and even pairs given by equation 3 (Zhang,

2011).

(3)

The entire reflectivity series is represented as a

summation of layer reflectivities:

(4)

where m is the layer number and M is the total number

of layers. Seismogram equation is obtained when

seismic wavelet is convolved on both sides of equation

4:

(5)

where W e and W o are seismic responses constituting G.

Basis pursuit algorithm is used then as a linear

equation to solve for the coefficients an,m and bn,m in

equation 5, and the results can be substituted to obtain

the final reflectivity inversion, so called BPI

reflectivity, given by equation 4.

Original Post-stack Section

Two original lines, PSTM Stack of 168 & 322, are

used as input data in this work. These lines are

primary lines that lays through the existing wells. Both

of this original lines are product of conventional

processing. The quality of the lines as shown on

Figure-3 is defined as low-to-medium quality. These

sections cannot show a detail layers in the reservoir

target zone as we identify from log data. Some parts

are blurry and low lateral continuity reflector. Since

the poor of quality input, it is a good opportunity for

SSA & BPI to show how much improvement can be

achieved.

RESULT AND DISCUSSION

This work conducted in two main steps. First, data

preconditioning using SSA technique and then

continued with BPI method for seismic enhancement.

To get the best of BPI, preconditioning data is a

necessity to avoid inversion of noises. As we know,

BPI works without any well involved and calculates

independently for each trace to predict high frequency

content. Figure-5 and Figure-6 show us thecomparison between original section, SSA result and

BPI result of line 168 and 322.

Both of the lines shows that frequency dominant

spectrum of original line is around 10-70 Hz. SSA

sections that is applied as precondition to increase S/N

ratio of original lines also have the same frequency

with the original one. For BPI lines, the frequency

spectrum is gained up at high frequency and extended

to 125 Hz. In addition, BPI also gains the low

frequency part. The original product of BPI containshigher resolution data, as shown in the amplitude

frequency spectrum. Unfortunately for this data, some

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of BPI’s new reflectors are quite small amplitude and

hardly visible. Therefore, some methods such as

automatic gain control (AGC) or amplitude

weightening cosine phase may be applied to help

provide a better visible image.

Wide Wiew

For wide view, we can see clearer image and higher

resolution as a result of SSA and BPI. The detail

becomes more visible after applying AGC. The SSA

and BPI together produced a significant increase to

the lateral continuity, as well as better layering and

gaining of the vertical resolution. Some seismic

features become more visible. For instance, in the line

168 (Figure-5) on the reservoir zone (from 200-1200ms), it shows a significant improvement. The

blurry area at 600 ms of the original section can be

reconstructed greatly by SSA and BPI. Some pinchout

pattern on 1100 ms and 1300 ms now can be displayed

clearer. Onlapping feature on 1700 ms also becomes

stronger, that we may interpret as a prograding pattern.

While for line 322 (Figure-6), the blurry part at time

1200 ms has the most notable improvements.

Ultimately, the continuity reflectors inside the blurry

zone are now more convenient to be interpreted.

Ant View

If we look more into detail, as shown in the Figure-4,

BPI's wiggle found to be sharper and slimmer

compared to the original traces and SSA. It brings us

an advantage; we now have a better quality image that

can be interpreted more precisely and with higher

confidence. If we analyze some traces which close to

the well, it is shown that BPI delivers reflectors which

have good match to lithology marker from well.

Without side lobes, BPI reflectors can be easily to tied

up to the well marker. We try to visualize interpreted

trace section (right) using similar color to GR-log. It

shows that most of lithology or facies that represented

with different color in GR-log can be identified much

more convenient than by using the original section.

Notice that almost of all of lithology boundaries from

well now has its reflector on seismic. Moreover, it has

a very good lateral continuation on traces. The BPI’s

reflectors which are sharper than original can help the

interpreters to be more confident defines the thickness

of each reservoir layers precisely. In other words, BPI

has lessened the ambiguity and increased the

consistency of seismic interpretation. Hence, using

BPI image we can estimate the reserve more

accurately.

Wavelet Issue

Similar to other inversion technique, wavelet is always

an essential issue. Phase and frequency dominant in

the wavelet are variables that can affect to the

inversion result. In BPI works, wavelet has crucial

rules. The information of original line we give to the

BPI algorithm is all carried on by the wavelet. Thus,

wavelet time variant extraction from seismic should be

ensuring very careful. Wavelet has to be extracted

from the interest zone and should be low noise ratio.

In this research we went through a priceless

experience, that is when we used wrong wavelet andended up with a new section produced by BPI which

quite different from the original. Besides the

enhancement of the resolution, using wrong wavelet in

BPI will produces artifact reflectors in unexpected

zone, while removes some confirmed reflectors. From

that experience, we found that BPI is very sensitive to

time variant wavelet. We overcome this problem using

average wavelet from more traces, hence suppressing

the noise. The result becomes stable and consistent to

the well-log.

CONCLUSIONS

In our case, seismic enhancement works to the thin

layers stacking-delta reservoir using BPI method,

which is previously preconditioned by SSA. The result

shows a big improvement of seismic image quality.

Combination of SSA and BPI method can enhance the

reflectors consistency, lateral continuity and vertical

resolution. The workflow can also tackle the gas effect

through the data due to great amplitude enhancement

in the dimming reflektor zones. Inversion process can

predict and restore missing reflectors by reconstruct

high frequency spectrum, which is more correlable to

the lithology information from well. BPI also shows

its robustness to the presence of noise.

The BPI’s reflectors which are sharper than original

can help the interpreters to be more confident in

defining the thichkness of each reservoir layer

precisely. In other words, BPI has lessened the

ambiguity and increased consistency of seismic

interpretation.


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ACKNOWLEDGEMENTS

Authors would like to thank Lapindo Brantas Inc. for

permission to publish this paper.

REFFERENCES

Zhang, R., 2008, Seismic reflection inversion by basis

pursuit, Doctoral dissertation at University of

Houston.Mauricio D. Sacchi, 2009, FX Singular Spectrum

Analysis, CSPG CSEG CWLS Convention,

Calgary, Alberta, Canada.


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LIST OF FIGURES

Figure-1 Regional Geology of East Java Basin.

Figure-2 Wells correlation of TGA field.


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Figure-3 Original section of line 168 S-N (left) and line 322 W-E (right).

Figure-4 Comparison of logs, original seismic (left, 12 traces), SSA (center left, 12 traces), BPI (center right, 12

traces), and interpreted BPI (right, 24 traces). Interpreted BPI is a manual interpretation of lithology; facies color

is referred to GR-log color. Inserted curve (red bold) in seismic traces is GR-log.

paper muhammad alwi dkk - seismic image enhancement using ssa bpi on stacked-delta reservoir -3

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