indonesian journal on geoscience v1 n2 august 2014

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Indonesian Journal on Geoscience Vol. 1 No. 2 August 2014: 65-70

INDONESIAN JOURNAL ON GEOSCIENCEGeological Agency

Ministry of Energy and Mineral Resources

Journal homepage: hp://ijog.bgl.esdm.go.idISSN 2355-9314 (Print), e-ISSN 2355-9306 (Online)

IJOG/JGI (Jurnal Geologi Indonesia) - Acredited by LIPI No. 547/AU2/P2MI-LIPI/06/2013, valid 21 June 2013 - 21 June 2016

Seasonal variation of 13C content in Poritescoral from Simeulue Island

waters for the period of 1993-2007

S Y C

Research Centre for Geotechnology LIPI, Kompleks LIPI, Jln. Sangkuriang, Bandung

Corresponding author: [email protected] received: March 27, 2014, revised: April 2, 2014, approved: July 14, 2014

Abstract - Variation of 13C content in coral skeletons shows the inuence of metabolic fractionation in aragonite coral.

Understanding coral 13C variation can thus be useful to more understand e.g.past bleaching event which is further

useful for coral health and conservation. In this study, 13C content inPoritescoral from Labuhan Bajau, Simeulue

Islands was analyzed. To know the correlation between variation of coral 13C and light intensity, the monthly varia-

tion of coral 13C is compared to solar radiation and cloud cover. The result shows that for the period of 2003 to 2008,

coral 13C shows it is well correlated (r=0.42p=0.153) with cloud cover variation in annual mean scale. Meanwhile, in

seasonal mean variation, coral 13C is strongly inuenced (r=0.85p


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Indonesian Journal on Geoscience, Vol. 1 No. 2 August 2014: 65-70

66

then ultrasonic bath cleaning followed. X-rayed

coral slabs were then used to perform the coral

banding (Figure 1). Subsampling using hand

drilling (1 mm bit) was done along the coral

growth axis to get the coral powder samples.Coral powder samples were then analyzed for

13C using Gasbench Delta Plus at the Free

University Amsterdam. The powdered samples

were reacted with H3PO

4, and the resulting CO

2

gas was analyzed in the mass spectrometer. All

samples are reported in .

Coral XDS software was used to calculate the

paired high/low density which was used to de-

veloped preliminary chronology in annual scale.

One year growth is represented by a dark and light

coral band in x -rayed coral. The detailed chronol-

ogy (i.e.monthly scale) of 13C is based on coral

Sr/Ca chronology development (see Cahyarini,2011). Paired density band calculation result in

that LBPoritescoral is about ~14 years old. The

chronology development of coral 13C results in

period ranges from July 1993 to August 2007.

Monthly variation of 13C from Porites coral

(sample code LB) for the period of 1993-2007 is

shown in Figure 2.

Historical data used in this study involved

solar radiation, cloud cover, and coral SST. Solar

radiation data are obtained from Fresco v.6 aver-

aged over 2x2 grid boxes resolution (from Wang

et al., 2008) and available from 2002- 2007.Cloud cover data are obtained from ICOADS

with 2x 2 grid resolution and available from 1996-

2007. Variable cldc (Cloudiness Monthly Mean

at Surface) is in okta.ICOADS data provided by

the NOAA/OAR/ESRL PSD, Boulder, Colorado,

USA, from their Web site at http://www.esrl.noaa.

gov/psd/.

SST derived from coral Sr/Ca (further men-

tioned as coral SST) was used in this study to

indicate the inuence of SST to the variation of

coral 13C. Coral SST is obtained from Cahyarini,

(2011).

Ru d Dcu

The analysis of 13C content in coral skeleton

sample LB using Gasbench Delta Plus in monthly

resolution is shown in Figure 2. Monthly variation

of coral 13C (Figure 2) ranges from -3.390.42

to -2.070.42 with the mean value -2.980.42for the period of 1993-2007. For the period of

2003 to 2008, decreasing trend of solar radiation

supposes decreasing trend of coral 13C from this

region (Figure 3), which conrms the published

work i.e.for the healthy coral as solar radiation

decrease, decreased coral 13C is due to decreasing

Figure 1.X -Rayed ofPoritescoral slab sample LB. Dashed

lines are subsampling transect along the growth axis.

Figure 2. Monthly variation of 13C content inPoritescoral (sample code LB) from Labuhan Bajo.

Top Bottom

-2.0

-2.5

-3.0

-3.5

-4.0

-4.5

1994

1993

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007


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Seasonal variation of 13C content in Poritescoral from Simeulue Island waters for the period of 1993-2007(S.Y. Cahyarini)

67

photosynthesis (Grottoli, 2002; Heikoop et al.,

2002). In some year periods, the seasonal cycles of

13C shows out of phase compared to solar radia-

tion seasonal cycles, i.e.from 2005 to 2007, when

the high solar radiation coincides with low 13C.

This suggests that this period coincided with the

weak ENSO event when the seawater temperature

anomaly slowed down the enrichment of 13C in

the coral skeletons. During the normal condition,

coral 13C enrichment normally follows the solarradiation cycle.

Seasonal mean variation of solar radiation

varies out of phase with the cloud cover, i.e.high

Figure 3. Graphic of monthly variation of coral 13C (grey line) and solar radiation (dark line) and its trend lines (bold grey

and dark lines).

Figure 4. Graphic of monthly mean variation of solar radiation (grey line) and (left) cloud cover (dark line) and (right) 13

C(dark line). The data are corrected for two month lag. All time series data are standardized.

0.00

-0.50

-1.00

-1.50

-3.00

-2.00

-3.50

-2.50

-4.00

2002 2003 2004 2005 2006 2007

d13C

Solarradiation

900

400

800

300

700

200

600

100

500

solar radiation coincides with low cloud cover

(Figure 4). In the studied areas, the maximum

solar radiation is in February (641.25 w/m2) and

the minimum is in August (495.09 w.m2), while

cloud cover maximum is in October (0.45) and

minimum is in Februari (0.27). Seasonal mean

variation of solar radiation and 13C coral shows

a good correlation in 1 month time lag. The maxi-

mum 13C is in November and the minimum is

in May. Figure 4 shows monthly mean variationof solar radiation, cloud cover, and 13C coral.

Solar radiation supposes some time to reach the

coral in 25 depths to inuence its 13C variation.

2.5

2.0

1.0

1.5

0.5

0.0

-0.5

-1.0

-1.5

-2.0

-2.5

Jan

Jan

May

May

Sep

Sep

Mar

MarJu

lJul

Nov

Nov

Feb

FebJu

nJun

Oct

Oct

Apr

Apr

Aug

Aug

Dec

Dec

2.0

1.0

1.5

0.5

0.0

-0.5

-1.0

-1.5

-2.0

-2.5

Jan

Jan

May

May

Sep

Sep

Mar

MarJu

lJul

Nov

Nov

Feb

FebJu

nJun

Oct

Oct

Apr

Apr

Aug

Aug

Dec

Dec


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68

Decreasing photosynthesis due to decreas-

ing light condition may be caused by increasing

cloud cover, which causes decreasing 13C in coral

skeletons. Decreasing 13C content in Simeulue

coral is supposed to relate to decreasing light inthe depth ofPorites coral (LB). This is convinced

that the maximum 13C amount in coral skeletons

occurred during a maximum light.

An annual mean 13C of LB coral sample and

cloud cover are compared (Figure 5). The result

shows that during ~10 years, from period of 1996

to 2007, decreasing trend of 13C coral follows

increasing trend of cloud cover. Correlation

between these two series convinces that annual

Figure 5. Annual mean variation of 13C (grey line) and cloud cover (dark line). Linear trend line (dashed line). Data are

standardized to unit variance.

Figure 6. Linear regression of cloud cover and 13C in the annual mean scale.

mean 13C of LB coral is correlated with cloud

cover (r=0.424p=0.169) (Figure 6). The variation

of coral 13C relative to cloud cover variation is

about 0.123/okta (0 clear cloud to 8 overcast).

It suggests that the clearer the cloud, the more13C content in coral.

Sr/Ca content in LB coral shows a local sea

surface temperature (SST) at a coral site (Cahya-

rini, 2011). Reconstructed SST based on Sr/Ca

content in LB coral (further mentioned as coral

SST) (Cahyarini, 2011) was used to understand

the inuence of SST to the variation of coral

13C. Coral SST was compared and correlated

with coral 13C (Figure 7). The result shows that

-0.5

-1

-1.5

-2

0

0.5

1

1.5

2

2002200120001996 1997 1998 1999 2003 2004 2005 2006 2007

7.06.56.05.55.04.54.03.5

-3.4

-3.2

-3.0

-2.8

-2.6

-2.4

-2.2

-2.0

C

Cloud Cover

y = -0.123x - 2.254

R = 0.180R=0.4242


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Seasonal variation of 13C content in Poritescoral from Simeulue Island waters for the period of 1993-2007(S.Y. Cahyarini)

69

Figure 7. Annual mean variation of coral 13C (dark line) and SST (grey line) derived from coral Sr/Ca. Data are standard-

ized to unit variance.

in annual mean resolution, correlation between

coral SST and 13C is high (r=0.54p=0.057). It

suggests that the inuence of SST to the varia-

tion of 13C is higher than that with cloud cover

(solar radiation) in annual mean scale. In seasonal

variation, coral 13C change respond to SST is low

(r=0.387p


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Porter J.W., Fitt, W.K., Spero, H.J., Rogers,

C.S., and White, M.W., 1989. Bleaching in

reef corals: physiological and stable isotopic

responses.Proceedings of Natural Academic

Science,USA, 86, p.9342-9346.Rodrigues, L.J. and Grottoli, A.G., 2006. Calci-

cation rate and the stable carbon, oxygen, and

nitrogen isotopes in the skeleton, host tissue,

and zooxanthellae of bleached and recovering

Hawaiian corals. Geochimica et Cosmochi-

mica Acta, 70, p.2781-2789, doi 10.1016/j.

gca.2006.02.014.

Wang, P., Stammes, R. van der A.P., Pinardi, G.,

and Roozendael, M. van, 2008. FRESCO+:an improved O2 A-band cloud retrieval

algorithm for tropospheric trace gas retriev-

als. Atmospheric Chemistry and Physics, 8,

p.6565-6576.


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Epithermal Gold-Silver Deposits in Western Java, Indonesia: Gold-Silver

Selenide-Telluride Mineralization

E T Y1,2, H M2, M F R1

1Faculty of Geology, Padjadjaran University, Jln. Raya Bandung - Sumedang Km. 21, Jatinangor, Indonesia2The Hokkaido University Museum, Hokkaido University, Japan

Corresponding author: [email protected]

Manuscript received: January 17, 2014, revised: June 24, 2014, approved: July 19, 2014

Abstract- The gold-silver ores of western Java reect a major metallogenic event during the Miocene-Pliocene

and Pliocene ages. Mineralogically, the deposits can be divided into two types i.e. Se- and Te-type deposits with

some different characteristic features. The objective of the present research is to summarize the mineralogical and

geochemical characteristics of Se- and Te-type epithermal mineralization in western Java. Ore and alteration mineral

assemblage, uid inclusions, and radiogenic isotope studies were undertaken in some deposits in western Java combined

with literature studies from previous authors. Ore mineralogy of some deposits from western Java such as Pongkor,

Cibaliung, Cikidang, Cisungsang, Cirotan, Arinem, and Cineam shows slightly different characteristics as those are

divided into Se- and Te-types deposits. The ore mineralogy of the westernmost of west Java region such as Pongkor,

Cibaliung, Cikidang, Cisungsang, and Cirotan is characterized by the dominance of silver-arsenic-antimony sulfosalt

with silver selenides and rarely tellurides over the argentite, while to the eastern part of West Java such as Arinem

and Cineam deposits are dominated by silver-gold tellurides. The average formation temperatures measured fromuid inclusions of quartz associated with ore are in the range of 170 220C with average salinity of less than 1 wt%

NaClequiv.

for Se-type and 190 270C with average salinity of ~2 wt% NaClequiv.

for Te-type.

Keywords: epithermal gold-silver deposit, uid inclusions, selenides, Se-type, tellurides, Te-type, western Java

Introduction

Western Java hosts several gold deposits and

all of the mineralizations follows the Sunda-Ban-

da magmatic arc, which is the longest magmatic

arc in Indonesia (Figure 1). The ore deposits ofwestern Java reect a major metallogenic event

during the Miocene-Pliocene. Mineralogically,

the deposits can be divided into two types, those

are Se-type and Te-type with some different

characteristic features. Telluride and selenide

minerals in many epi- and mesothermal deposits

are often associated with gold and silver that

have an important role worldwide. The principal

characteristics of the Te- and Se- minerals in

epithermal deposit were described by Sillitoe and

Hedenquist (2003).

The Se-type of western Java mineralization

mostly lies within and on the anks of the Bayah

Dome and is represented by Pongkor, Cikidang,

Cisungsang, Cirotan, and Cibaliung deposits,

while the Te-type is located more eastern and

represented by Arinem and Cineam deposits(Figure 2). Studies of ore mineralogy and geo-

chemistry were carried out within the epithermal

ore deposits of western Java by previous authors

such as Pongkor (Basuki et al.,1994; Marcoux

and Milesi, 1994; Sukarna et al., 1994; Milesi et

al., 1999; Sukarna, 1999; Warmada et al., 2003;

Syafrizal et al., 2005; Syafrizal et al., 2007;

Warmada et al., 2007), Cikidang (Rosana and

Matsueda, 2002), Cibaliung (Sudana and Santosa,

1992; Marcoux and Milesi, 1994; Marjoribanks,

2000; Angeles et al.,2002; Harijoko et al., 2004;


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Harijoko et al.,2007), Cisungsang (Rosana et al.,

2006), Cirotan (Milesi et al.,1993; Marcoux et

al.,1993), Arinem (Yuningsih et al., 2012), and

Cineam (Widi and Matsueda, 1998).

Most of the Se- and Te-type deposits in west-

ern Java are in the form of vein. However, the

Cisungsang deposit forms the massive sulde

with some vein association. Vein size of the Se-

and Te-types are various from several meters

to more than 5,000 m in length and from a few

centimeters up to 5 m in width. The gold miner-

alization ages within this area for the Se-type are

mostly of Pliocene and Pleistocene with the range

from 2.4 to 1.7 Ma and Late Miocene (11.18 Ma)

Malaysia

Australia

Central Kalimantan arc

Sumatra

5000 1,000

N

kilometers

125

Eo

SulawesiEast Mindano arc

Sumatra-Meratus arc

Sunda-Banda arc

Philippines

Sulawesi

Kalimantan

Malaysia

Java MedialIrian Jaya arc

Halmahera arc

Magmatic arc

Late Miocene and PliocenePaleogene and Mid Tertiary

Late Cretaceous

Irian Jaya

PNG

125

Eo

100

Eo

Figure 1. Distribution of the magmatic arc within the Indonesia archipelago from Late Cretaceous to Pliocene (modied

after Carlile and Mitchell, 1994). The location of the studied area is bounded by a rectangle.

N

25 kmJAKARTA

Serang

Pandeglang

Cibaliung

Indian Ocean

Cikidang

Cirotan Cisungsang

Sukabumi

Pongkor

PelabuhanratuBandung

Arinem

Cineam

Bogor

106 E

7 S

107 Eo

o

o

Figure 2. Location and distribution of the Se- () and Te- () type epithermal Au-Ag deposits in the western Java, Indonesia.


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Epithermal Gold-Silver Deposits in Western Java, Indonesia: Gold-Silver Selenide-Telluride Mineralization(E.T. Yuningsih et al.)

73

for Cibaliung deposit. K-Ar age dating of Te-type

indicates the mineralization ages are around 9.9

~ 8.5 Ma or Late Miocene, respectively.

The principal objective of this research is to

summarize the mineralogical and geochemicalcharacteristics of Se- and Te-type epithermal

mineralization in western Java. Ore and altera-

tion mineral assemblage, uid inclusions, and

radiogenic isotope studies were undertaken in

some deposits in western Java combined with

literature study from previous authors.

Methods

Thin-, polished- and doubly polished sections

of samples from western Java deposits were

analyzed using transmitted- and reected-light

microscopes. Additional samples of altered host

rocks were investigated by X-ray diffraction us-

ing standard treatment methods for clay mineral

identication. Geochemical analyses for major,

minor, and trace elements of ores were conducted

by ICP at Acme Analytical Laboratories (Vancou-

ver) Ltd., British Columbia, Canada.

The compositions of ore minerals were de-termined using a JEOL 733 electron microprobe

analyzer at Hokkaido University. Standards used

were natural chalcopyrite, InP, MnS, CdS, FeAsS,

Sb2S

3, PbS, SnS, HgS, ZnS, and elemental Se, Au,

Ag, Te. The probe was operating at 20kV voltage

and the beam current of 10nA was focused to 1-10

m diameters with peak counting for 20s. The

X-ray lines measured were As, Se, Te, Cd, Ag,

Bi, and Sb (L), S, Cu, Zn, Fe, and Mn (K), and

Pb, Au, and Hg (M). The data were corrected

by ZAF correction.

Doubly polished thin sections were prepared

on 200 m thickness for uid inclusion study

on quartz, sphalerite, and calcite minerals. Mi-

crothermometric analysis was performed on a

Linkam THMSG 600 system attached to a Nikon

transmitted-light microscope. Heating rate was

maintained near 2C min-1for measurement of

homogenization temperature (Thtotal

) and 0.5C

min-1for measurement of ice melting temperature

(Tm). Precision was calculated as 0.1C in thetemperature range of the observed phase changes.

Accuracy between -60 and -10C is estimated

in the order of 0.2C, whereas between -10

and +30C and above +200C is placed at 0.5

and 2C, respectively. Instrumental calibra-

tion was done using synthetic pure H2O (0C),dodecamethylene Glycol (82.0C), benzanilide

(163.0C), sodium nitrate (306.8C), n-tridecane

(-5.5C), n-dodecane (-9.6C), chlorobezene?

(-45.6C), and chloroform (-63.4C) inclusion

standards.

Salinity was determined from the last melt-

ing temperatures of ice, utilizing the equation by

Bodnar (1993). The possibility of the presence of

volatile species (CO2, N

2), hydrocarbons (CH

4,

C2H

6), and solid phases in uid inclusions was

identied by Raman spectroscopic analyses on

limited samples.

Results and Analyses

Ore Mineralogy

The dominant opaque minerals from the Se-

type deposits are Se- and Se-bearing silver miner-

als (aguilarite, naumannite, argentite, polybasite,

and pyrargyrite), electrum, and tetrahedrite withvarious amounts of sulde minerals of sphalerite,

galena, chalcopyrite, arsenopyrite, and pyrite.

Other ore minerals are found in a trace amount.

Some rare minerals of Bi- and Sn-bearing min-

erals such as lillianite and caneldite occur in

Se-type deposit of Cirotan (Milesi et al., 1993).

The Te-type is characterized by the occurrence

of hessite, petzite, stutzite, tetradymite, altaite,

and tennantite-tetrahedrite, with a high amount

of sulde minerals of sphalerite, galena, chalco-

pyrite, and pyrite with occurrences of arsenopy-

rite. Some photomicrographs of the ore minerals

associated in the Se- and Te-type deposits are

presented in Figure 3.

Rare telluride minerals of hessite and altaite

were reported from the Se-type deposit (Harijoko

et al., 2007), but until now there are no selenide

minerals observed in the Te-type deposits of

Arinem, except for the Te-type deposit of Cineam

which contains trace of Se-bearing minerals of

pyrargyrite-proustite. The occurrences of the oreminerals from the two types of deposits are sum-


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marized in Table 1 along with other characteristics

of those deposits.

Ore Geochemistry

The FeS content of sphalerite from the Te-

type is generally similar to those of the Se-type

mostly in the range of 0.1-2.4 mol% (Se-type)

and 0.5~2.0 mol% (Te-type, rare are up to 8.5

mol%). However, the FeS content of sphalerite

from massive deposit of the Cisungsang (Se-type)

is higher, ranging from 13.6-19.6 mol%, and from

Cirotan is between 0.5 and 26.0 mol% (Milesi

et al., 1993). Cadmium content in sphalerite of

Se-type is in the range of 0.1-2.0 mol% and in

Te-type of Arinem around 0.1-1.0 mol%.

The Ag content of electrum from the Se-type

is higher than that from the Te-type, rangingbetween 22-68 wt% and 14-40 wt%. Some ore

minerals from Se-type contain selenium such as

in galena which is up to 1.5 wt%, in acanthite-

aguilarite up to 13.5 wt%, and in polybasite up

to 3.6 wt% (with Te content up to 5.5 wt%).

Tellurium content in proustite is in trace amount

and in uytenbogaardtite is up to 0.8 wt%. Ore

minerals of the Te-type deposit of Arinem contain

selenium such as in galena which is up to 1.9

wt%, in tetradymite 0.1-2.1 wt%, and up to 1.4

wt% in petzite.

Geochemical analyses on the bulk vein sam-

ples inferred Mn are higher in the Se-type, but

low in the Te-type. Bi and Hg are lower in the

Se-type compared to the Te-type deposit. The

comparison of the geochemical composition

between the Te- and Se-type deposits represented

by the Arinem and Pongkor deposits is show inthe Table 2.

Figure 3. Reected-light photomicrographs of the ore mineral association from some deposits at the western Java. (a)

Pongkor; (b) Arinem; (c) Cikidang; (d) Cisungsang. Abv.: alt=altaite, arg=argentite, canf=caneldite, cpy=chalcopyrite,

elm=electrum, gn=galena, hs=hessite, lim=limonite, pr=proustite, py=pyrite, pyrg=pyrargyrite, qtz=quartz, sph=sphalerite.


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77

Host Rocks and Hydrothermal AlterationIn general, the dominant host rocks for both Se-

and Te-type deposit are dacitic-basaltic volcanic

rocks. The Se-type occurs in volcanic rocks and

sometimes some sedimentary rocks intercalated;

while Arinem and Cineam deposits of the Te-type

formed in volcanic rocks. The gangue mineral of

Se- and Te-types is characterized by the presence

of large amounts of quartz, followed by carbonate

and illite. Some of Mn carbonate (manganoan cal-

cite and rhodocrosite) occurred in Se-type deposit.

The characterizing gangue by the presenceof adularia is well developed in some Se-type,

while there is no adularia found at the Te-type.

Hydrothermal alteration patterns in the Se- and

Te-type deposits are similar with the abundance

of the propylitic alteration, and are dominated

by chlorite, illite, mixed layered illite-smectite

and chlorite-smectite. Argillic alteration is char-

acterized by illite, montmorillonite, and some of

kaolinite, usually enveloping the vein where the

silicication and sericitisation occurred. The

association alteration minerals in those deposits

indicate the pH is neutral with slightly acid at thelate stage of mineralization for some of deposit

(such as in Arinem).

Homogenization Temperature and Salinity of

Fluid Inclusions

Fluid inclusion data of quartz indicate that

the Se- and Te-types formed over temperature

ranges between 160 - 330C and 160 - 350C, on

the average (shallower to deeper) of around 170 -

220C and 190 - 270C, respectively. The salinity

of ore uids for the Se-type is estimated to havebeen slightly lower than that for ore uids of the

Te-type. The Se-type has the salinity up to 3.4

wt% NaClequiv.

on the average of less than 1 wt%

NaClequiv.

except for the Cirotan deposit which

is up to 7.15 wt% NaClequiv.

(Milesi et al., 1993)

and for Te-type is in the range of 0.2 - 4.3 wt%

NaClequiv.

on the average of ~2 wt% NaClequiv.

. For-

mation temperature and salinity estimated from

the uid inclusion homogenization temperature

and melting temperature for both types of deposits

are summarized in Figure 4.

Table 2. Comparison of Bulk Chemical Analyses of Te-Type (represented by Arinem Deposit) and Se-Type (represented

by Pongkor Deposit) Ores

Unit in ppm; *no analyses; **in percent (%); ***in ppb; 1Warmada et al.(2003).

Arinem Deposit

Arinem Vein Bantarhuni Vein

IA IB IIA IIA IIA IIB IIC IIC IIB IIC IIC

Se 308 30 116 305 190 35 119 >500 212 >500 >500

Te 32 na* 156 57 53 14 14 209 na na na

Pb** 2.85 0.31 0.03 3.66 2.35 1.24 1.32 15.71 0.10 25.27 21.78

Mn 929 696 1,161 3,407 1,161 1,703 309 2,013 >100 1,084 852

Cd 1,287.3 47.2 2.9 112.1 528.7 101.7 885.3 >2,000 19.2 1,964.1 1,160.2

As 13.2 >10,000 13.6 289.9 247.8 3,134.9 1,669.8 4.4


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78

Discussions

Mineralogically, sulde minerals are abundant

in the Te-type and varied in the Se-type from trace

(e.g. Pongkor) to abundant (e.g. Cirotan). This

phenomenon is contradictory with the Se- and Te-

types in Japan wheresulde minerals, except forpyrite and marcasite are very poor in amount for

the Te-type, but sulde minerals such as argentite,

sphalerite and galena are abundant in the Se-type

(Shikazono et al., 1990).

The Se- and Te-type deposits in western Java

are characterized by the temperature generally

of


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79

Cibaliung), though Se minerals are uncommon

to coexist with the Te-type deposit. Comparison

with other Se- and Te-types in Japan pointed the

physicochemical conditions of the Arinem and

Cineam deposits exhibited mineral assemblagesmight be deposited closer to the heat source and

shallower than those of the western most deposits

(Pongkor, Cikidang, Cibaliung, Cisungsang, and

Cirotan).

Geochemical analysis of Se and Te elements

from both Se-type of Pongkor and Te-type of

Arinem deposits show the content of Te is higher

at the vein samples of Te-type Arinem deposit,

but the difference of Se content from both types

of deposits is not too signicant (Table 2). Oth-

erwise, the petrographic investigation shows the

occurrence of Se-mineral at Pongkor, but not

at Te-types of Arinem and it is rare in Cineam

deposits. Thus, it is concluded that there are

other factors besides the host rock types, and

the distance from the heat source controlled the

formation of the Se- and Te-minerals among the

Se- and Te-type deposits.

Conclusions

The ore mineralogy of the Te-type deposits of

western Java was characterized by the abundance

of sulde minerals with minor Te-minerals of

hessite, petzite, stutzite, tetradymite, and altaite,

while the Se-type has various amounts of sulde

minerals with the occurrence of minor Se-miner-

als of aguilarite and naumannite, and Se-bearing

minerals of argentite, polybasite, and pyrargyrite.

Other minerals were found as minor or trace in

both types of deposits.

The mineralogic data indicate that the Se- and

Te-type deposits in western Java are characterized

by the presence of a large amount of quartz and

carbonates, with accessories of illite, chlorite, and

smectite. Adularia is present at the Se-type but not

in the Te-type, and generally the propylitic and

argillic alteration zonation of the Se- and Te-types

is similar. Formation temperatures of the Te-type

are generally higher than those for the Se-type.

The comparison with the Se- and Te-typesoccurred in Japan pointed to the conclusion that

the Te-mineralization probably occurred closer

to the volcanic centre and at a higher level of the

geothermal system than the Se-mineralization.

It is also concluded that there might be other

factors controlled the formation of the Se- and

Te-minerals within those deposits.

Acknowledgement

The authors would like to thank PT. Antam Tbk.

for support access to data and samples during the

eld investigation and to acknowledge the con-

tribution of the large member of geologic staff.

This work is funded by the Directorate Generalfor Higher Education (DGHE), Ministry of Edu-

cation, Indonesia, and the Faculty for the Future

Program (FFTF) Schlumberger, France.

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83





Reservoir Modeling of Carbonate on Fika Field:

The Challenge to Capture the Complexity of Rock and Oil Types

E F A1, FA2, M. A A3, andB W4

1Petrophysicist, PT Medco E&P Indonesia2Reservoir Engineer, PT Medco E&P Indonesia

3Development Geologist, PT Medco E&P Indonesia4Geologist Software Support, Schlumberger

Corresponding author: [email protected] received: October 10, 2013, revised: January 21, 2014, approved: August 12, 2014

Abstract - The carbonate on Fika Field has a special character, because it grew above a basement high with the thick-

ness and internal character variation. To develop the eld, a proper geological model which can be used in reservoir

simulation was needed. This model has to represent the complexity of the rock type and the variety of oil types

among the clusters. Creating this model was challenging due to the heterogeneity of the Baturaja Formation (BRF):

Early Miocene reef, carbonate platform, and breccia conglomerate grew up above the basement with a variety of

thickness and quality distributions. The reservoir thickness varies between 23 - 600 ft and 3D seismic frequency

ranges from 1 - 80 Hz with 25 Hz dominant frequency. Structurally, the Fika Field has a high basement slope, which

has an impact on the ow unit layering slope. Based on production data, each area shows different characteristics and

performance: some areas have high water cut and low cumulative production. Oil properties from several clusters

also vary in wax content. The wax content can potentially build up a deposit inside tubing and ow-line, resultedin a possible disturbance to the operation. Five well cores were analyzed, including thin section and XRD. Seven

check-shot data and 3D seismic Pre-Stack Time Migration (PSTM) were available with limited seismic resolution.

A seismic analysis was done after well seismic tie was completed. This analysis included paleogeography, depth

structure map, and distribution of reservoir and basement. Core and log data generated facies carbonate distribution

and rock typing, dening properties for log analysis and permeability prediction for each zone. An Sw prediction for

each well was created by J-function analysis. This elaborates capillary pressure from core data, so it is very similar to

the real conditions. Different stages of the initial model were done i.e.scale-up properties, data analysis, variogram

modeling, and then the properties were distributed using the geostatistic method. Finally, after G&G collaborated

with petrophysicists and reservoir engineers to complete their integrated analysis, a geological model was nally

created. After that, material balance was needed to conrm reserve calculations. The result of OOIP (Original Oil in

Place) and OGIP (Original Gas in Place) were conrmed, because it was similar to the production data and reservoir

pressure. The model was then ready to be used in reservoir simulation.

Keywords:reservoir modeling, carbonate, rock and oil types, simulation, Fika Field

Id

Fika Field is an oil and gas producer that lies

in South Sumatra Basin. Currently, the eld has

38 wells, of which 24 are producers from BRF

(Baturaja Formation). The formation has hetero-

genic properties. Some parts of the eld have

BRF with high permeability, while the other

parts may have BRF with tight permeability that

requires stimulation, such as hydraulic fracture,

in order to be able to produce. The cumulative

production is 8 MMSTB and more than 47

BCF of gas, which originated from associated

gas and gas cap production. Oil recovery factor

is expected to be more than 30%, although the

gas cap has been blow- down since December

2009, which has accelerated reservoir pressure

depletion and reduced oil production. In addi-

tion, hydraulic fracturing has been done in this

eld, resulting in an increase in oil production



20/53


84

from 20 BOPD to 50 - 113 BOPD, while, other

wells produce gas and water.

Because a high demand for gas must be sat-

ised, the Fika Field must produce its gas cap,

which is the main reservoir drive. This will affectreservoir pressure and oil recovery. To minimize

oil loss due to gas cap blow down, and to maxi-

mize gas production, a team was established to

conduct a reservoir study.

The previous workers who studied Baturaja

carbonates relating to hydrocarbon reservoir

properties are Caroline (2005), Handayani (2008),

and Erawati (2013).

The purpose of this paper is to explain how

to build rock typing from carbonate which is

highly heterogenic, and how to generate per-meability transform and steps in model water

saturation by using capillary pressure from core

analysis. At the end of the paper, there is a discus-

sion on reserve conrmation regarding static data

and production data by utilizing material balance.

Gg Sg

This eld has a simple geological structure and

there is more emphasis on stratigraphic aspects.

Musi Platform is bounded by Pigi depression in

the northern area, Lematang depression in the

south-east area, Saung Naga graben in the south-

west area, and Benakat Gully in the eastern

area. This setting indicates the possibility of reef

build-up above basement high (Musi Platform),

when the sea level rose (transgression) during

deposition of Baturaja Formation (Rashid et al.,

1998). The carbonate type that grows on the Musi

Platform is an isolated platform. Carbonate facies

on the Fika Field is divided into reef, platform,

and breccia conglomerates with different quality,uneven distribution, and relatively thin thickness

(up to 20 ft below). The Baturaja carbonate is

Early-Middle Miocene in age with depositional

environment about neritic to shallow marine,

while tectonic settings are in a sagging phase. In

the study conducted with LAPI ITB (2011), the

Musi Platform has hydrocarbon source rock from

Lemat Formation as lacustrine environment. The

lithology is lacustrine shale mixing between algal

lacustrine and organic material from land origin.

Oil expelled on moderate maturity (approximately0.7 - 0.95% Ro) with kerogen type II/III derived

from exinite, liptinit or algae. This generally

indicates gas and oil. Lemat Formation on the

studied area began 22 MYA and has moderate

maturity for producing oil (early oil generation)

in Benakat Gully.

D d Mhd

This research was divided into various

stages of data analysis, as listed below:

Seismic Data Analysis

1. Well seismic tie from seventhcheck-shots.

2. Seismic interpretation and the result as time

structure and depth structure maps of reser-voir and basement.

Petrophysical Evaluation

1. Review available Special Core Analysis

(SCAL) data to determine of a, m, n.

Facies carbonate assignment after core

depth matching and core description.

Net Overburden (NOB) core correction for

porosity and permeability and Klinken-

berg correction for permeability.

Defining matrix end-point value from

crossplot: RHOB vs core porosity, DT vs.

core porosity, NPHI vs.core porosity.

Dening a and m from the best straight line

plot log F (Formation Resistivity Factor)

vs.log porosity on every facies (rocktyp-

ing result).

Dening n from the slope of the line plot

log Sw vs.log RI (Ro/Rt).

2. Analyzing log using zonation based on geo-

logical correlation.

Estimating Rw value for Baturaja reser-

voir in Fika eld. Calculating Vcl (mudstone) using SP log

and Density- Neutron log after conrma-

tion with XRD data correlation.

Calculating porosity effective and Sw us-

ing Simanduox and Indonesia. The nal

selection for Sw values will be based on

transition zone analysis (TZA).

Crossplot between log porosity (effective

and total) vs.core porosity (NOB correc-

tion).

3. Lithofacies based on core descript ion androcktyping determination.


21/53


The Challenge to Capture the Complexity of Rock and Oil Type (E.F. Adji et al.)

85

4. Analyzing looping log using parameter zona-

tion by rocktype.

5. Prediction permeability and Analyzing Transi-

tion Zone (TZA) to predict Sw based on core

data.

Fine Grid Model

1. Scaling up properties

2. Analyzing data

3. Modeling variogram

4. Distributing the properties using geostatistic

method.

Reserve Calculation Conrmation

1. Calculation material balance reserve

2. Calculation static model reserve (OOIP andOGIP).

R d D

Seismic Data Analysis

Seismic 3D PSTM was used for this study.

Well seismic tie was implemented in the early

stages of seismic well analysis, using well data

(density and sonic log) and wave model (wavelet)

from seismic data extraction. The same parameters

as 3D seismic data were used, where positive po-

larity is recorded as increasing acoustic impedance

on positive amplitude with zero phase.Figure 1

below shows wavelet extraction parameter, the

model of extraction, and the amplitude spectrum.

The next step was to create a synthetic seis-

mogram and to match the trace with seismic data.

Match value between synthetic seismogram and

seismic trace is called coecient correlation (r).

Positive r value and near with 1 shows that syn-

thetic seismogram and seismic trace has good

correlation.Well to seismic tie analysis was done on the

seventh check shot at this eld. Figure 2 shows

synthetic seismogram from Fika-1 well, while

Table 1 shows the resume of coecient correlation

from each well.

After well seismic tie, some main markers

were dened and distributed on seismic data to

obtain the geological model of each marker and

to interpret the geological history of the eld.

The seismic mapping result of basement and

Baturaja carbonate is shown in Figures 3a and

3b, respectively.

Figure 1. Parameter and the result extraction on Field 3Dseismics.

Figure 2. Parameter and the result extraction on Field 3D

seismics.

Petrophysical Evaluation

This step begins with an analysis of core

data measurement after core depth matching. It

is important to make a reliable denition of the

position of the carbonate facies development

with depositional setting and match with thesubsurface condition. Thereafter, routine core

Well Coefcient Correlation (r)

Fika-1 0.605

Fika-2 0.698

Fika-3 0.781

Fika-4 0.281Fika-5 0.447

Fika-6 0.463

Fika-7 0.845

Table 1. Resume of Coecient Correlation from each Fikas

check Shot Wells


22/53


86

analysis (RCA) and SCAL data were done.

Porosity core requires NOB correction(Figure 4),

while permeability core requires NOB (Figure 5)

and Klinkenberg correction (Figure 6). Based on

the core description, facies carbonate denition is

created and zonation is needed for log analysis.

The correction factors are as below:

NOB= 0.9755

amb

1.0288

kNOB

= 0.5159 kamb

Klinkenberg effect on permeability is esti-

mated from available liquid permeability data and

given trend from text book (Figure 6).

Based on geological setting, the carbonate

facies which developed from the top of Baturaja

to basement are reef, platform, and breccia/

conglomerate clastics. A previous study on the

Figure 3. Time Structure Map. (a) Basement Fika, (b)

Baturaja Carbonates.

Figure 4. Porosity NOB correction on Fika Field.

Figure 5. Permeability NOB correction correlation on Fika

Filed.

Figure 6. Klinkenberg correction correlation of permeability

on Fika Field.

BRF at Fika eld wascarried out in a previousstudy which indicated that seven lithofacies can

be identied based on core calibration from Fika-

A1, C1, D1, E1, and F2 wells, in addition to image

analysis from Fika-B4, C1, and E1 wells.

The G&G groups utilized the available seis-

mic data and well logs to dene three depositional

facies (referred to as zones in the current geologic

model) as follows:

1. Reef Limestone

2. Platform Limestone

3. Breccia/Conglomerate ClasticsInvestigation of available core description (both

whole & plugs) conrms the existence of the

above three depositional facies and indicates the

following lithofacies (Figures 7 and 8; and Table 2).

The distribution of lithofacies described

above (from core data) does not indicate any

specic relationship with subsea elevation (TVD

subsea) within individual depositional facies

(zones) as shown in Figure 9.

The above conclusion is supported by the

previous study as indicated by the distributionof lithofacies with depths shown in Figure 10.

Fika-01

Fika-02

Fika-03

Fika-04Fika-05

Fika-06

Fika-07

0 300 600 900 1200 1500 m

Fika-01

Fika-02

Fika-03Fika-04

Fika-05

Fika-06

Fika-07

0 300 600 900 1200 1500 m

perm NOB vs amb

1.0288y = 0.5159x

Perma

tNOB

Perm at Ambient0.01 0.1 1 10 100 1000 10000

10000

1000

100

10

1

0.1

0.01

0.001

por NOB vs amb

y = 0.9801x

PorosityatNOB

Porosity at Ambient0 10 20 30 40

40

35

30

25

20

15

10

5

0

-1 0 1 2 3 4 5

1

0.8

0.6

0.4

0.2

0

CorrectionFactor

Klinkenberg Effect

3 2y = -0.0024x + 0.013x + 0.0606x + 0.6267

Log kair

Text Book Data

Actual Soka Data

Trendline


23/53



87

Core Calibration Lithofacies

1 2

Limestone

Bedded-laminated

wack to pack

Skeletal pack-stone to grain-

stone

Mottledwackstone

Massive tomicrocrystalline

wackstoneMudstone

Volcanicconglomerate

Volcanicbreccia

Mudstone Conglomerate/breccia

3 4 5 6 7

Figure 7. Core calibration lithofacies.

LF-1 LF-2 LF-3LF-4

Bedded to lamited LS Vuggy to mottled LS Inregular layers to mottled LSMassive to

microcrystalline LS

LF-5 LF-6LF-7

Equal bedded to wavy LS

MudstoneRubble bed

(Breccia/conglomerate)

No core for calibration(Well-D, 3337-3341)

Figure 8. Image and core calibration lithofacies.

Depositional

Facies

Lithofacies Number of

Data Points

Percentage

Reef Vuggy Coral/Grainstone 34 11%

Mottled Wackstone 36 12%

Platform Vuggy Wackstone/Packstone 31 10%

Bedded, Chalky, and microcrystallineLimstone

109 36%

Breccia/Con-glomerate

Diminant Limestone Fragments 60 20%

Dominant Vulcanic/Basement Frag-ments

31 10%

Total 301 100%

Table 2. Facies Distribution from Core Analysis on Fika

FieldRock Type

DepthTVDss

0 1 2 3 4 5 6 72800

2850

Reef Vuggy

Reef non Vuggy

Platform Vuggy

Platform Non Vuggy

Breccia Limestone

Breccia Clastic

2900

2950

3000

3050

3100

3150

Figure 9. Relationship between lithofacies and subsea eleva-tion on Fika Field.


24/53


25/53



89

New zone

codeDescription

Lithofaces

included

0 Non reservoir 01 Breccia/Conglomerate 0, 1 and 2

2 Low energy LS 0, 3

3 High energy LS 0, 5

4 Vuggy LS 0, 4, 6

electrofacies distribution was based on the value

of VGR and corrected SP. After reviewing all

capillary pressure data, it has been concluded that

the permeability/porosity ratio depends more on

rock type from lithofacies and capillary pressuredata. The permeability/porosity ratio will be used

as a basis to create rock typing and TZA. Rock

typing based on permeability/porosity ratio and

TZA will be discussed later in this paper in a

special section.

Based on SCAL data from four cored wells,

a and m were dened from the best straight line

plot log F (Formation resistivity factor) vs log

porosity on each rock type (Figure 11). The n

parameter was dened from the slope of the line

plot log Sw vs log RI (Ro/Rt) (Figure 12). Theaverage density was dened on each rock type.

The result is shown in the Table 6.

The next step was to dene matrix end-point

value from crossplot between log data and core

data measurement,i.e.RHOBvs. core porosity,

DT vs.core porosity, NPHI vs core porosity (Table

7). Logically, when porosity value is zero, it isassumed as matrix value on the log data.

The preliminary log analysis used zonation

based on geological correlation, after rock-typing

had been dened. Log analysis uses parameter a,

m, n, and end-point matrix in every rock type.

Rw estimation for Baturaja reservoir was based

on Picket Plot (Figure 13). The Rw estimation

value was equal to 17.000 ppm salinity. A water

test lab analysis result was not appropriate input

for log analysis, due to the inuence of mud on

the water samples.Vcl (mudstone) calculation on Fikas carbon-

ate used GR and density-neutron log. According

to the concept introduced by Asquith (2004), the

sonic log usually reads matrix porosity without the

effect of vugs, but both neutron and density logs

indicate the effect of vugs on porosity reading.

Consequently, more signicant differences were

expected between calculated porosity values from

these logs opposite vuggy limestone intervals

compared to intervals without vugs. The following

chart shows this phenomenon after applying the

concept to Fikas core data (Figure 14).

In the above chart, the porosity difference ratio

is dened as follows:

Figure 11. Formation Factor vs. Porosity to obtain Cementation Factor (m).

Table 5. New Zone Code and Lithofacies

a and m from Rock Type 1 a and m from Rock Type 2

a and m from Rock Type 3

FormationFactor

FormationFactor

FormationFactor

Porosity, v/v

-1.795y = 1.0768x

-1.761y = 1.0995x

-2.16y = 0.702x

Porosity, v/v

Porosity, v/v

100

10

1

100

10

10.1 10.1 1

RT 1 Power (RT1)

0.1 1

100

10

1

RT 2 Power (RT2)

RT 3 Power (RT3)


26/53


90

R=

sonic-

D-N

D-N

Where:

sonic

= sonic porosity

D-N

= average porosity from density and

neutron logs

From Cross Plot

Rock Type a m n

1 1.077 1.795 1.905

2 1.100 1.761 1.9363 0.702 2.160 1.959

Rock Type Ave Grain Dens, gr/cc

1 2.705

2 2.692

3 2.699

Table 6. Resume of Average Value from a, m, n, and Grain

Density on every Rock Type

n from Rock Type 1 n from Rock Type 2

n from Rock Type 2

logResistivity

Index

logResistivity

Index

logResistivityIndex

log Water Saturation log Water Saturation

log Water Saturation

y = -1.905x y = -1.9357x

y = -1.9588x

-0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0

-0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0

1.5 1.5

1.5

1.4 1.4

1.4

1.2 1.2

1.2

1 1

1

0.8 0.8

0.8

0.6 0.6

0.6

0.4 0.4

0.4

0.2 0.2

0.2

0 0

0

RT 1 Linier (RT1) RT 1 Linier (RT1)

RT 3 Linier (RT3)

Figure 12. Formation Resistivity Index vs Brine Saturation to obtain Saturation Exponent (n).

Table 7. Resume of Average Value from a, m, n, and Grain

Density on every Rock Type

Parameter RT1 RT2 RT3

Matrix density 2.68 2.7 2.71Fluid density 1.1 1.2 1.1

NPHI for matrix 0 0.07 0

NPHI for uid 0.83 0.94 1.1

Matrix transit time 50 52 53

Fluid transit time 225 198 210

It should be noted that this denition of poros-

ity difference ratio does not align with the results

from this study, since theoretically speaking,

sonic porosity should be lower than density-

neutron porosity for interval with vugs. However,

log analysis results indicated sonic porosity tobe higher than density-neutron porosity for most

intervals.

Even with the incorrect denition, the results

in the above chart do not indicate any correlation

for dening criteria to identify vuggy intervals. It

should be noted that Vsh values initialy calculated

for this study were based on minimum values

among several methods available in the Petrolog

software. The study team revised this concept

in view of the questionable applicability of GR

logs in carbonate reservoirs. Accordingly, onlydensity-neutron logs were used to dene Vsh.

The core data do not include platform with

vugs. Accordingly, the study team decided not to

include this rock type in the current model. This

decision was further supported by the geologic

concept of low probability for nding vugs in

platform carbonate intervals overlain by reef

carbonate.

The fact that vuggy intervals that cannot be

recognized from well logs is supported by visual

investigation of available core material. Figure15 shows that vugs were scattered within thin


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91

existence of an appreciable amount of mudstone

within the BRF, including the reef zone. The

mudstone in BRF is believed to be the result of

internal diagenetic and lithication effects and

probably some external effects from gravity

settling of ne materials.

Three thin sections were analyzed quanti-

tavely by Lemigas in order to determine the

mudstone content in the side wall samples from

well Fika-I1 (1). The results are shown below.Measured mudstone contents in the three sec-

tions are 15, 16 and 62.5% by volume. Average

Vsh value for the sorted data sample is 21.6%,

which is rather low for the mudstone content

range indicated by thin section analysis.

After dening appropriate Vsh, total porosity

must be correctly calculated to be effective po-

rosity. Comparative results between log porosity

(total and effective) vs.core porosity (NOB) are

shown in Figure 16.

Sw calculation was made using Simanduoxand the Indonesia method. The Indonesia meth-

od is more appropriate in this eld, because the

result is more sensitive to transition areas. The

nal selection for Sw values was based on TZA.

Permeability transform of four lithofacies

on electrofacies was slightly modied. Vuggy

limestone has data distribution near low en-

ergy limestone, so the permeability transform

between low energy limestone (mud supported

dominated) and vuggy limestone used the same

transform value. The formula can be seen in the

Figures 17a, b, and c.

PHIA(v/v)-ApparentPorosity

(fromPHICP,PHID,PHIS,PHIN,orPHIM)

RT vs PHIAvs VCLFika A1 (S) pro log data: Zone 5 - 5260.000 Ft to 5512.5000 FT

1000

0.100

0.0100.2 2 20 200 2000

RT (OHMM) - Formation Resistivity

0 VCL (v/v) 1

Figure 13. Picket plot to determine Rw of Baturaja Formation.

Figure 14. Porosity difference ratio for various rock type

on Fika Field.

Meteoric diagenesisDissolution of large benthic forams due to fresh

Water leaching during subaerial exposure

Leaching is more intense in exposed marinelimestone on topographic highs

0 5mm

Figure 15. Meteoric diagenesis from thin section on Fika

Field.

intervals that cannot be read and cannot af-

fect log response. Investigation of side wall core

samples from well Fika I1(1) as well as thinsection analysis of these samples indicate the

Porosity difference ratio for various rock types

Porosity

diffence

ratio

Rock Type

1 2 3 4 5 6

2

1.5

1

0.5

0

-0.5

-1


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92

Trend

A1

F2

E1

C1

Trend

A1

F2

E1

C1

Porosity Plot Porosity Plot

Core Por NOB

0 5 10 15 20 2 5 30 35 40 0 5 10 15 20 25 30 35 40

Core Por NOB

40

35

30

25

20

15

10

5

0

40

35

30

25

20

15

10

5

0

PHIE

PHIE

Figure 16. Comparation between plot between log porosity (total and effective) vs. core porosity (NOB).

Figure 17. Permeability transform for high energy lime-

stone (a), low energy limestone and vuggy limestone (b),and breccia clastics (c).

log k vs porosity for Rt2(low energy carbonate)

Porosity

log

k

y = 16.167x - 1.9623

2y = -36.591x + 27.873x - 2.4362

4

3

2

1

0

-1

-2

-30 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

vuggy mud support ed Linear (linear method) Poly (polynomial method)

log k vs Por for Rt1(Breccia clastics)

Porosity

log

k

4.0

3.0

2.0

1.0

0.0

-1.0

-2.00 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Clastics Linear (Clastics)

y = 14.148x - 1.92092

R = 0.8757

log k vs porosity for Rocktype 3(high energy carbonate)

Porosity

log

k

2y = -40.415x + 29.399x - 2.3904

y = 14.543x - 2.1684

vuggy grain supported Poly (polinomial method) Linear ( linear method)

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

4

3

2

1

0

-1

-2

-3

Transition Zone Analysis

To dene Sw in each grid, transition zone

analysis (TZA) was applied. The application was

conducted in the following procedure:

1. Dening permeability transforms and a

suitable Swc trend in terms of permeability,

which for Fika Field can be seen in the fol-

lowing graph (Figure 18).

2. Dening J-function derived from core data

and normalize all available data in a singlechart. The core data were divided into three

regions based on range of k/ (Table 8 and

Figure 19).

3. Dening J-max from chart in no. 2

4. Calculating k, Swc

and Sw* from the explora-

tion well log (i.e.the log which was surveyed

when the reservoir was not yet producing) and

using it to calculate h and (J cos ) for every

depth-log above OWC.

J cos = h(w-

o)gk/

Swc Transform

Swc

Log (k)

0.5

0.450.4

0.35

0.30.25

0.2

0.15

0.1

0.05

0-1 0 1 2 3 4

from Relative Permeability from Pc

4 3 2y = -0.0007x + 0.0068x - 0.0178x - 0.052x + 0.3618

Figure 18. Crossplot between Swc vs. log permeability toget Swc transform.


29/53



93

5. Ploting (J cos ) versus Sw* and tting the

best J-function curve. Calculating ( cos )

resfor each reservoir rock type at determined

value of Sw*

( cos )res

= (Jcos ) J

6. Calculating the reservoir J-function for each

reservoir facies using the average value of( cos )

res.

7. Comparing the curves of laboratory and

reservoir J-functions versusSw*.The chart

should show a good match, as shown in the

following graph (Figures 20a, b, c):

8. Estimating the value of for each reservoir

rock facies if is known.

9. Calculating the coefcient of J-function for

use in Petrel model.

10. Using the reservoir J-function to formulate

a transform relating Sw* to Jresor a selectedfunction of J

res.

11.Using the value ( cos )res

to calculate re-

quired capillary pressure curves for various

facies from their normalized J-Functions.

12. Calculating corresponding values of Sw*.

Fine Grid Model

The 3D geology model was built by using

Petrel 2011 software. There were several stages

during the process, including: 1) creating hori-

zon, zone, and layering, 2) scaling up properties,3) analyzing data, 4) modeling variogram, and

5) distributing the properties by geostatistic

method. The area of interest was limited by

polygon boundary, which was created based on

oil-water contact area.

Since there was no fault in this Fika Field,

the 3D grid model was built using simple grid.

The rst step in creating a simple grid was cre-

ating horizon, using several surfaces as input

data, such as BRF (top surface), PLTFRM, BX-CGL, and BSMT (bottom surface). All of these

input surfaces were already in depth domain.

The next step was to determine the grid bound-

ary, which was based on surface boundary and

grid geometry, such as X minimum, X maxi-

mum, Y minimum, Y maximum, and grid size

increment. After this, the grid increment was

determined. The grid increment should represent

the geological features on a lateral distribution

and also for simulation purposes. 50x50 grid

increment was chosen, because it would give agood result for distribution of reservoirs. If the

low k/por k/por


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94

grid increment exceeds 50x50, there will be a

greater possibility of error when calculating

bulk volume (reected in negative value in bulk

volume), and also the value of properties will

not be accommodated. But if less than 50x50 isinput, many active grids will be created, which

will be ineffective, and furthermore the calcula-

tion process will be slowed down. As a result,

there are four horizons with three zones. Zone

one is reef zone, zone two is platform zone, and

zone three is breccias/conglomerate zone. After

creating zonation, the following step was the

layering process. Each zone was divided into

several layers based on cell thickness, so every

layer will have an average thickness of 0.88 ft.

Determining the number of layers was basedon the depositional pattern of carbonate in Fika

Field. As a result, the layering process created

972 layers for all zones, with a total number of

5458752 3D cells.

Scale-up properties were calculated for

several well logs,i.e.porosity, horizontal per-

meability, vertical permeability, water satura-

tion, facies and net to gross ratio. All of these

propert ies were upscaled based on the ne

grid that was created before, using weighted

averages and the following criteria: a) faciesand rock type: most of average weighted bulk

volume, b) net to gross: arithmetic average

weighted bulk volume, c) porosity: arithmetic

average weighted bulk volume, d) horizontal

permeability: arithmetic average weighted net

volume, e) vertical permeability: harmonic

average weighted bulk volume, f) water satura-

tion: arithmetic average weighted pore volume.

A histogram was used to do a comparative QC

of the well logs before and after upscale logs.

A similar trend in the histogram represented

a good correlation between well logs before

upscale and upscale logs. Data analysis wasrequired, because the petrophysical modeling

used a variogram from data analysis. The rea-

son for this was to dene the major, minor, and

vertical direction of several properties.

Porosity modeling was built using SGS with

conditioning to facies and subfacies for each

zone. Even though the distribution of porosity

followed the variogram, it also referred to the

AI model with applied collocated co-kriging.

So for, any area that was out of variogram, the

porosity would follow the trend of AI model(Figure 21).

Similarly, permeability modeling was built

using SGS and conditioning to facies and subfa-

cies, but referred to the porosity model (Figure

22).

Meanwhile, facies modeling was built using

SIS (Gslib) method, because this method was

able to distribute the discrete data very well.

The SGS (Gslib) honoured the well data and

distributed to four different facies,i.e. nonres-

ervoir, breccias and vuggy LS, low energy LS,and high energy LS, (Figure 23).

The other properties, vertical permeability,

net to gross, and rock type, were distributed by

applying a formula using a PETREL calculator.

The water saturation was also distributed by

using a formula obtained from the relationship

between J-function and capillary pressure based

on TZA above.

Porosity

0.28

0.240.2

0.16

0.12

0.08

0.04

Soka-A2

Soka-A1 (3) Soka-B1

Soka-B5

Soka-B6Soka-B/1(2)Soka-B

Soka-B2

Soka-B3

Soka-H3

Soka-H2

Soka-I2

Soka-H4

Soka-H1

Soka-D725

00

2500

32503200

3200

2150

3000

3000

2700

2750

3250

0 250 50 0 750 m

Soka-D10

Soka-D6

Soka-A3

Soka-D Soka-D3

Soka-D1

Soka-H1Soka-D4

Soka-D2ST

Soka-C4

Soka-C2

Soka-H1

Soka-D5

Soka-F1

Soka-F2

Soka-F4

Soka-F4Soka-F5

Soka-G2

Soka-E2

Soka-E1(2)Soka-D9

Soka-G1

Figure 21. Porosity map after co-krigging stage.


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95

Figure 22. Permeability co-krigging stage.

Permeability (mD)

180325610.100.0320.00560.0010.00010

Soka-A2

Soka-A1 (3) Soka-B1

Soka-B5

Soka-B6Soka-B/1(2)Soka-B

Soka-B2

Soka-B3

Soka-H3

Soka-H2

Soka-I2

Soka-H4

Soka-H1

Soka-D72500

2500

32503200

3200

2150

3000

3000

2700

2750

3250

Soka-D10

Soka-D6

Soka-A3

Soka-D Soka-D3

Soka-D1

Soka-H1Soka-D4

Soka-D2ST

Soka-C4

Soka-C2

Soka-H1

Soka-D5

Soka-F1

Soka-F2

Soka-F4

Soka-F4Soka-F5

Soka-G2

Soka-E2

Soka-E1(2)Soka-D9

Soka-G1

0 250 500 750 m

Fika-A1 (3)

Subur-1

Fika-I1(1)

Fika-D1Fika-F-4

-3600

-3200

-2600

-2400

-3600

-3200

-2600

-2400

FAC

Non ReservoarBrecciaLow energy LstHigh Energy LstVuggy Lst

Figure 23. Vertical cross section that shosw facies model-

ing distribution.

Reserve Calculation Conrmation

Reserve conrmation utilizing material balance

analysis

Material balance was utilized to conrm

oil and gas in-place in Fika Field. This eld

has been producing since January 2001, so

there are enough production and pressure data

(Figure 24).

A straight line material balance analysis can

be seen in the graph (Figure 25).

where

and:

N = OOIP, STB

Np = cumulative oil produce, STB

Wp = cumulative water produced, bbl

Bt = current two-phase FVF, RB/SCF

Bg = current gas FVF, RB/SCF

Bo = current oil FVF, RB/STB

Rsi = initial solution GOR, SCF/STB

Rpc = cumulative producing GOR, SCF/STB

Bti = initial two-phase FVF, RB/SCF

Bgi = initial gas FVF, RB/SCF

m = gas cap size

P = pressure, psi

C = water inux constant, RB/psi

tD = dimensionless time

QD = dimensionless owj = time step index for water inux constant

n = number of time steps used in water inux

calculations

From the straight line original oil in-place

in Baturaja Formation = 26 MMSTB

Water inux constant = 2500 bbls/psi

The m value of 7.3 originated from static

model (without assumption of any basement

bald). This calculation used aquifer character-

istics as shown in Table 9.

The material balance calculation conrmedthe volumetric calculation that the OOIP was

around 22 - 26 MMSTB and that OGIP was

around 131 BCF.

Static model reserve calculation (OOIP and

OGIP)

Volumetric calculation was done to check

the original oil and gas in-place in the Fika BRF

reservoir. In addition, the original oil in place

results were compared with the material bal-

ance analysis to ensure there were similaritiesbetween these two methods.

Np [Bt + Bg (Rpc - R )]si + Wp n

1 (Pj- 1 - P)j QD f or tDN-tDj- 1= N + C

Y1 Y1

mBti(B

g- B

gi)

Bgi

Y1= (B

t- B

ti) +


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96

C

Carbonate facies on Fika Field geologically

were divided by reef, platform, and breccia con-

glomerate. Based on core analysis, the carbonatefacies were divided into six lithofacies as jus-

tication on permeability prediction. TZA was

divided into three rock types which represent

ow unit as low quality, medium quality, and

high quality limestones.

All reservoir property models were built

taking into consideration the complexity of

rock and oil types, because the properties were

tied into facies and water saturation distribution

based on the relationship between J- function

and capillary pressure.Reserve calculation from static model was

conrmed by comparing reserve analysis with

material balance analysis on production data of

about 26 MMSTB.

Further research into the complexity of rock

and oil types, mainly for reservoir modeling,

should be continued using reservoir simula-

tion so that oil ow behaviour can be observed

clearly.

Akwdg

The authors wish to thank PT Medco E&P

Indonesia and SKK MIGAS for their permission

to publish this paper. In addition, the authors

would like to thank the management of PT

Medco E&P Indonesia for their encouragement

and support.

R

Asquith, G. B., 2004. Basic well log analysis

for geologist.American Association of Pe-

troleum Geologist. Methods in Exploration.

Tulsa, Oklahoma, 16, p.12-135.

Erawati, F.A., 2013. Utilization of Advance Seis-

mic Interpretation for Estimation Reservoir

Hydrocarbon Distribution on Carbonate,

FIKA Field Study Case, South Sumatera

Basin,M.S. Thesis, University of Indonesia.

Based on the distribution of reservoir prop-

erties, i.e.porosity and water saturation, the

value of OOIP was about 25.3 MMSTB, while

the value of OGIP was about 131.7 BCF.

Because the hydrocarbon in-place has been

already conrmed, this static model could be

used in reservoir simulation (initialization and

history matching).

Observed Reservoir Pressure in Fika FieldFrom Static Bottom Hole Survey

1600

1550

1500

1450

1400

1350

1300

1250

1200Jan-00 Jun-01 Dec-04 May-07 Nov-09 Apr-12

Pressure,psig

Date

Figure 24. Bottom hole pressure prole from Baturaja

Formation of Fika Field.

Straight Line Material Balance Plot With Aquifer

Wtihdrawal/Y1,MMSTB

60

50

40

30

20

10

0

0 1

(p.Qd)/Y1, M.psi

2 3 4 5 6 7

Figure 25. Straight line material balance plot with aquifer

of Fika Field.

Aquifer Properties

h. ft 95

k.mD 106

w. cP 0.24

. fraction 0.18

cw, 1/psi 3.26E-06

cf, 1/psi 5.72E-06

Bw. RB/STB 1

re. ft 35.000

. degree 180

Table 9. Aquifer Propeties dened for Material BalanceAnalysis for Baturaja Formation in Fika Field


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97

Handayani, R.S.W., Setiawan, D., and Afandi,

T. 2008.Reservoir Characterization of Thin

Oil Column to Improve Development Drill-

ing in a Carbonate Reservoir: Case Study

of Gunung Kembang Fields. Proceedingsof Indonesian Petroleum Association, 32nd

Annual Convention and Exhibition, Jakarta,Paper, IPA 08-E-160, 15pp.

Caroline L.T. J, 2005.Lithofacies Characteriza-tion of Soka Field Based on Core Calibra-tion on Image Data and Wireline Log for

better Prediction of Reservoir Properties of

the Baturaja Formation, Soka Field, South

Sumatera.M.S. Thesis, University of Brunei

Darussalam.

LAPI ITB, 2011. Geochemical Study and Evalu-

ation in South Sumatra Basin (Soka, Lagan,

Matra and Iliran Regions),Final Report.Rashid, H., Sosrowidjojo, I. B., and Widiarto,

F. X., 1998. Musi Platform and Palembang

High: A New up Straight Look at The Pe-

troleum System.Proceedings of Indonesian

Petroleum Association, 26thAnnual Conven-

tion and Exhibition,Jakarta, Paper, IPA 98- I - 107, 11pp.


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99


A Drowning Sunda Shelf Model during Last Glacial Maximum (LGM)

and Holocene: A Review

T S

Center for Research and Development of Marine and Coastal ResourcesJln. Pasir Putih 1, Ancol Timur, Jakarta 14430

Corresponding author: [email protected]

Manuscript received: April 14, 2014, revised: July 16, 2014, approved: August 15, 2014

Abstract - Rising sea levels since the Last Glacial Maximum (LGM), some ~20,000 years ago, has drowned the

Sunda Shelf and generated the complex coastal morphology as seen today. The pattern of drowning of the shelf will be

utilized to assess likely timing of shoreline displacements and the duration of shelf exposure during the postglacial sea

level rise. From existing sea level records around Sunda Shelf region, sea level curve was assembled to reconstruct

the shelf drowning events. A ve stage drowning model is proposed, including 1) maximum exposure of the shelf at

approximately 20,500 years Before Present (y.B.P.), when sea level had fallen to about -118 m below present sea level

(bpl.), 2) melt water pulse (MWP) 1A at ~14,000 y.B.P. when sea level rose to about -80 m bpl., 3) melt water pulse

(MWP) 1B at ~11,500 y.B.P., when sea level was predicted around -50 m bpl., 4) Early-Holocene at ~9,700 y.B.P,

when sea level was predicted at about-30 m bpl, and 5) sea level high stand at ~4,000 y.B.P., when sea level jumped

to approx. +5 m above present sea level (apl.). This study shows that the sea level uctuated by more than 120 m at

various times during LGM and Holocene. Also conrmed that sea level curve of Sunda Shelf seems to t well whencombined with sea level curve from Barbados, although the comparison remains controversial until now due to the

considerable distinction of tectonic and hydro-isostatic settings.

Keywords:Last Glacial Maximum, sea-level changes, transgression, drowning shelf

Introduction

The Sunda Shelf is located in Southeast Asia

and it represents the second largest drowned

continental shelf in the world (Molengraaff and

Weber, 1921; Dickerson, 1941). It includes parts

of Indonesia, Malaysia, Singapore, Thailand,

Cambodia, Vietnam coast, and shallow seabed

of the South China Sea (Figure 1). During the

LGM, when sea levels are estimated -116 m be-

low present sea level (bpl.), the Sunda Shelf was

widely exposed, forming a large land so-called

Sunda Land connecting the Greater Sunda

Islands of Kalimantan, Jawa, and Sumatra with

continental Asia (Geyh et al., 1979; Hesp et al.,

1998; Hanebuth et al., 2000, 2009).

The Sunda Shelf is also considered as a

tectonically stable continental shelf during the

Quaternary (Tjia and Liew, 1996) and categorized

as a far eld location (far away from former

ice sheet region), providing the best example for

observing sea level history and paleo-shoreline

reconstruction. In such environment, the effects

of seaoor compaction, subsidence, and hydro-

isostatic (melt water release from the ice sheets)

compensation are negligible during the relatively

short time interval of thousands of years (Lam-

beck et al., 2002; Wong et al., 2003).

This study reviews some published sea level

observations then presenting a summary of the

Sunda Shelf drowning model. Moreover, it dis-

cusses some sea level records from different lo-




IJOG/JGI (Jurnal Geologi Indonesia) - Acredited by LIPI No. 547/AU2/P2MI-LIPI/06/2013, valid 21 June 2013 - 21 Ju

indonesian journal on geoscience v1 n2 august 2014

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