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Forty-First Annual Convention & Exhibition, May 2017
Haryono Haryanto*
Wahyudin Bahri Nasifi*
Indah Edria Amorita*
I Wayan Ardana Darma*
Trisakti Kurniawan**
Fithra H. Darmawan**
Azli B. Abu Bakar**
Randy Condronegoro***
Andri Syafriya***
The South Sumatra Basin is one of the most prolific
basins in Indonesia with estimated total recoverable
reserves of 8,046 million barrels of oil equivalent
(IHS, 2016) from four main plays including (1) PreTertiary Fractured Basement, (2) Oligocene Talang
Akar Sandstone, (3) Lower Miocene Baturaja
carbonate, and (4) Lower to Middle Miocene Gumai
Sandstone plays. Previously, most of the wells
drilled in the study area were targeting the deeper
intervals and did not consider the Gumai interval as
one of the potential drilling objectives.
However, recent drilling campaigns in the study area
have showed encouraging results from the Gumai
play which encountered high gas reading in stringers
sandstones which has triggered a look back analysis
on the Gumai interval. The geological data evidence
are aligned by seismic inversion results both
deterministic and geostatistical which correlated
with the presence of reservoirs. The integrated
quantitative interpretation shows three sandstone
units, two of which have good reservoir porosities
and potential to be trapped with hydrocarbon. These
promising results are of paramount significance for
further hydrocarbon exploration strategic planning.
The Gumai interval especially in Intra Gumai can be
categorized as an underexplored play. Previous
exploration wells treat it as a regional seal. This
interval in the Jabung block has been previously
penetrated by thirteen wells which indicate high
value start from 300 to 2000 units of total gas
Petronas Carigali Indonesia Operation
** Petronas Carigali Sdn Bhd
*** Petrochina Jabung Ltd
reading. In the latest drilling campaign, well A was
designed to test the Basement Fracture Play in the
initial drilling program and the Intra Gumai interval
was not identified as an objective, but indications of
total gas 300 units and gas shows resulted in a
decision to conduct tests; it flowed gas & condensate.
It has been succesful in unlocking this interval as an
underexplored play. The effort to re-evaluate this
play has therefore become crucial.
In this work, the authors performed several
integrated geophysical studies such as deterministic
and geostatistical seismic inversion to obtain the
reservoir properties that could be filled by
hydrocarbons and also to identify lateral distribution
of good reservoirs.
Regional geology
The study area is located in the South Sumatra Basin
in Indonesia (Figure 1). This basin is categorized as
the most prolific basin which has recovered more
than 8 BBOE and is yet to find more than 4 BBOE
(IHS, 2016) with new and underexplored plays
(Figure 2). It comprises of a series of grabens formed
by extensional back-arc stress related to the
subduction of the oceanic crust of the Indian Ocean
under the Sundaland Craton in western Sumatra.
These grabens, which are essentially rift basins
(Figure 3), are the main hydrocarbon kitchen in
Sumatra containing thick, restricted syn-rift
sediments, which form the primary source rocks. The
sedimentary succession in the South Sumatra Basin
comprises a single transgressive/regressive secondorder cycle that commenced in Late Eocene to Early
Oligocene with deposition of transgressive alluvial,
fluvio-deltaic and marginal marine facies of the
Lahat and Lower Talang Akar formations. Full
marine conditions were eventually established
during the continuing transgression sequence with
deposition of an open marine facies in the Upper
Talang Akar, which consists of marine shales, marls
and fine-grained sands. Initial uplift of the Sunda
Shield to the East in the Middle Miocene marked the
end of the Early Tertiary transgression and the
beginning of a regression sequence that continues to
the present day, which gave rise to the deposition of
prograding deltaic sediments of the Gumai and Air
Benakat formations as a result of increased sediment
load from the Sunda landmass to the northeast. These
sediments comprise of distal delta front shales,
distributary mouth bar sands, delta bar sands,
channel sands and interdistributary shales.
The Gumai Formation is more commonly known as
the regional seal that sealed the Talang Akar
Formation, which is one of the most prolific
reservoirs in the South Sumatra Basin. At a regional
scale, the Gumai Formation could be divided into
sandstone-rich upper and mud-rich lower sections.
At a local scale, the Lower Gumai could be further
broken into upper and lower intervals. The lower
interval comprises of predominantly transgressive
marine shales (including marl) whereas the upper
interval contains limestones streaks formed when
sediment influx was locally restrained. Due to
differential compaction within the Betara Graben, a
localized deeper marine environment was
superimposed on the regional subsidence and
transgression during the deposition of the Lower
Gumai. In contrast, the deposition of the Upper
Gumai sandstones took place in more regressive
conditions. During this depositional period (From
Early to Middle Miocene), marine conditions
regressed to approximately middle neritic conditions
in response to the initial Barisan Mountains Uplift.
The general stratigrapy of the South Sumatera basin
is shown in figure 4.
The Intra Gumai play can be categorized as
underexplored play. Previous exploration wells did
not become target objective and it has been
recognized as regional seal. In fact, the sourounding
wells indicate high value start from 300 to 2000 unit
of total gas reading. Latest drilling campaign, well A
gave good contribution of test result; gas and
condensate flow. It has been succed to unlock this
interval as underexplored play.
Seismic Inversion
Acoustic impedance (AI) is an important rock
property which describes density (ρ) and velocity
(Vp) of the subsurface. In zero offset or full stacked
data, seismic trace can be modeled as the convolution
of the acoustic impedance reflectivity (RAI) with the
wavelet (W). The simple formula can be stated as
Most seismic inversion methods are based on
minimizing the differences between synthetic
seismic and real seismic responses. Synthetic seismic
responses are the result of convolution of wavelet
and earth reflectivity, which is a function of acoustic
impedance. The inversion methods which operate in
minimizing error are known as deterministic
inversion (Ansri, 2014).
Deterministic seismic inversion such as sparse spikes
or model-based inversion produces smooth
resultsdue to its limitations. Francis discussed some
of these limitations (Francis, 2006). The significant
limitation is missing low frequency information due
to the band-limitation of real seismic data. Since the
source wavelet is band-limited and does not cover all
frequencies, low and high frequency components are
hidden in the seismic responses. Missing the low
frequency is important due to the fact that low
frequency components contain critical information
about the absolute impedances values (Francis,
2010). Low frequency information can be obtained
from well log data. For adding log data to seismic
data, one may encounter a serious problem, which is
known as the support of scale measurement of data.
Well data has a high vertical resolution compared to
the seismic data. In deterministic methods, the scaleup of well data to larger support measurements is
used. Scale-up is an averaging method, which
reduces variability of measurements, when they are
scaled up to larger supports.
Overcoming these problems, geostatistical seismic
inversion is an alternative to improve deterministic
inversion results. The geostatistical inversion
process is more advantageous than the deterministic
inversion process due to its ability to handle
uncertainties. In addition, the process can also be
improved by constraining it using geological
information (Sams et al, 2011).
This joint impedance and facies geostatistical
inversion of seismic, (geo)statistics and well data is
based on a combination of Bayesian inference and
the Markov Chain Monte Carlo (MCMC) sampling
algorithm, to creat equally probable realizations of
the subsurface reservoirs (Contreras et al., 2004)
Reservoir Characterization
There are three stages in this work (see Figure 5).
Firstly, we gathered all initial information of
geology, geophysics, petrophysics and rock physics.
Secondly, we performed predictive interpretation
using deterministic inversion and geostatistical
inversion (Bayesian inference and Markov Chain
Monte Carlo (MCMC) sampling algorithm). Thirdly,
we analyzed the product of llithofacies definition and
reservoir properties.
The initial stage of study began with petrophysical
and rock physics analyses that included log data
conditioning, sensitivity analysis, lithofacies log
generation and depth trend analysis. The analyses not
only require accurate elastic log curves, but also
demand the consistency of lithology, porosity and
saturation. Therefore, the first step began with data
QC and conditioning of the logs from the three
available wells (Well A, B & C) (see Figure 6) to
ensure that the data is free from borehole effect, etc.
The neutron, sonic and density logs were checked
and found to be generally consistent across all three
wells. However, the logs were conditioned in bad
borehole section and invalid data was removed
(Figure 7).
The petrophysical model was developed from the
density-neutron cross-plot in order to determine the
lithology fractions and calculate V-clay as well as the
porosity of the rocks. Rock physics analysis was then
used to build a reliable set of elastic logs (e.g. pwave, s-wave and density logs) and to understand the
relationships between the petrophysical properties
with the elastic properties that can be measured from
seismic data. A comprehensive rock classification
based on mechanical behavior was also built in
order to perform reservoir and fluid delineations.
The result shows a unique elastic parameter
transition in vertical and lateral direction (Figure 8).
High impedance sand stringers turned out softer in
acoustic impedance domain when good porosity
and hydrocarbon were introduced. Therefore, the
overlap between sand and shale seems to be high
(Figure 9).
In addition, Vp/Vs ratio is found to be a good
lithology indicator due to the ability to detect
variations of rock quality and fluid in seismic data
through variation in reflectivity coefficient character
as a function of offset/angle. However, this attribute
is not used in this study due to the unavailability of
angle stack data. Therefore, in the absence of seismic
angle stack data, only acoustic impedance is usable
both as a lithology discriminator and as a tool to
predict reservoir properties. Since the overlap
between the different lithologies is high, we have
opted to use the geostatistical inversion, which could
integrate geological information and statistics
together as constrained input and deliver a plausible
Once the well-based feasibility analysis was
established, a trace-based single stack sparse spike
deterministic inversion followed by geostatistical
inversion were applied to the real seismic data using
the proposed technique/workflow. The seismic data
was rigorously conditioned to improve the reliability
of the data through post stack processing. However,
the main concern on the data quality of this onshore
dataset is the noise level. Therefore, two processes
were applied: (1) Cadzow filter; and (2) Spectral
blueing (Randy, 2012). The application of those
processes has resulted in the seismic having clearer
event, better continuity and optimum frequency
bandwidth (Figure 10).
Then, a deterministic inversion process was
performed using (1) a simple, single shale
compaction trend, (2) wavelet extracted from multiwell over the Intra Gumai interval. This process is
critical because the result will be used as a reference
to determine signal/noise level, reliability of wavelet
and the data statistical range in the geostatistical
inversion process.
The geological constraints include (1) three different
compaction trends, used here to ensure good
correlation with the regional geology trend (Figure
11), and (2) acoustic logs derived from rock physics
modeling, which provide more reliable histograms
(Figure 12) and probability density function (PDF)
information (Figure 13). Comparison between the
deterministic and geostatistical inversion results was
performed to ensure inverted impedances have
consistent distribution (Figure 14). This process,
among others, ensures that the geostatistical
inversion generated is suitable with the seismic, well
and geological data.
Fifteen realizations of inverted acoustic impedance
and lithology have been produced and two reservoir
properties, namely V-clay and effective porosity
have been delivered as the products through a cosimulation process using the impedance and
lithofacies results (Figure 15).
The geostatistical seismic inversion result shows
three fascinating sandstone units, two of which have
good reservoir porosities and potential to be
charged by hydrocarbon (Figure 16). The upper part
of the Intra Gumai interval has been proven by
testing of well A that contained gas and condensate
with total gas unit of 300. Those two good
reservoirs show positive correlation with total gas
finding around 500 to 1200 units. The rest is poor
sand and is located in the lower interval; they also
correlated with the result of tests in well B; no fuild
recovery and only trace oil. The result of
geostatistical seismic inversion has been
successfully modeled and is appropriate with
surrounding well evidence. It has captured the Intra
Gumai Sandstone in the lower interval as a clear
channel feature, roughly in the NW-SE direction
which could be interpreted as influx from the NW
direction (Figure 17).
Those reservoirs are of paramount significance and
it is recommended to explore more to support
further exploration strategies.
South Sumatera Basin is a productive basin in
Indonesia. Currently, hydrocarbon discovery in
conventional play may have already reached plateau.
Therefore, it need to review again for underexplored
play especially Intra Gumai play. It is important to
maintaining current hydrocarbon production from this
Operation (PCINO) especially for Mr. M Zaini Md
Nor, Mr. Jamin Jamil B Mohd Idris, Mr. Nasaruddin
B Ahmad, Rizki Krishna Pratama, and also special
thanks to PetroChina International Ltd, MIGAS,
SKKMIGAS and BKPM for the approval to publish
this paper. We also thank the members of
Exploration Department, CAA and Legal
Department, who assisted in internal approval.
Ansri, H.R., 2014, Comparing Geostatistical Seismic
Inversion Based on Spectral Simulation with
Deterministic Inversion: A Case Study: Iranian
Journal of Oil & Gas Science and Technology, Vol.
3 (2014), No. 1, pp. 01-14.
Bishop, M.G., 2001, South Sumatra Basin Province,
Indonesia: The Lahat/Talang Akar-Cenozoic Total
Petroleum System: in U.S. Geological Survey OpenFile Report 99-50-S.
Contreras, A., Torres-Verdin, C., Kvien, K.,
Fasnacht, T. and Chesters, W., 2004, AVA stochastic
inversion of pre-stack seismic data and well logs for
3D reservoir modeling,The 67th EAGE Conference
& Exhibition, Extended Abstracts, F014.
IHS, 2016, Cumulative Production of South
Sumatera Basin, IHS.
Ginger, D. and Fielding, K., 2005, The petroleum
system and future potential of the south Sumatra
basin, The 30th IPA Annual Convention and
This integrated reservoir characterization study
shows three fascinating sandstone units, two of
which have good reservoir porosities. Total gas
evidence in the surrounding area indicates that those
two good sandstone reservoirs are filled by
hydrocarbon. They have been successfully modeled
to unravel the underestimated hydrocarbon
potential in the Intra Gumai interval. The results are
of paramount significance and it is recommended to
explore and discover more in this play.
Pulunggono, A., 1986, Tertiary Structural Features
Related to Extensional and Compressive Tectonics
in the Palembang Basin, South Sumatra, The 15th
IPA Annual Convention Proceedings.
Sams, M., Millar, I., Satriawan, W., Saussus, D.,
Bhattacharyya, S., 2011, Integration of geology and
geophysics trough geostatistical inversion: a case
study, First Break, 29, August, 47-56, EAGE.
The authors wish to express their appreciation to the
Management of Petronas Carigali Indonesia
Randy C. et. al, 2012, Seismic 3D image
enhancement to extract possible thin reservoir and
fractured basement in Jabung block - South Sumatra,
The 37th HAGI Annual Convention and Exhibition.
Figure 1 - Location and database. Area of study is located in the South Sumatera Basin.
Figure 2 - South Sumatera Basin Performance curve. It has produced around 8 BBOE of discovered oil and gas resources (IHS, 2016).
Figure 3 - Regional schematic cross-section crossing Tungkal, Betara, and Geragai Graben.
Figure 4 - General stratigraphic column of South Sumatra Basin.
Figure 5 - Methodology of study.
Figure 6 - Well A is not targeted for Intra Gumai sand. Lithologically dominated by thin sandstone and shale.
DST result confirmed gas flowed from that particular level.
Figure 7 - Data QC example using Well A & B showing the effect of before (Blue) and after (Red)
conditioning on the P-sonic and density logs.
Figure 8 - (a) Well log correlation between Well A, Well B and Well C. Rapid transitition changes both in
vertical and lateral dimension representing marine depositional environment is observed.
Dominant frequency is around 42 Hz (b) and tuning thickness is between 11 – 25 m (c).
Figure 9 - Histograms of acoustic impedance from individual well A, B, C (a) and Multi well (b) indicate
high overlap between lithology in acoustic impedance domain. Therefore, it is recommended to
perform probabilistic analysis.
Figure 10 - The current seismic data set was applied with imaging enhancement in 2012 and the result is quite
impressive compared to the legacy dataset.
Figure 11 - Multi well depth trend analysis color coded according to (a) well, (b) lithology and (c) lithology
plus porous sand category. The lithology was based on cut-off value of the Vclay log and the
porous sand classification was based on effective porosity content. This classification is important
to highlight and narrow down the chances of getting more precise prediction.
Figure 12 - The graphics show P-impedance vs frequency at different layers. A general increase of impedance
with depth is observed in the study area.
Figure 13 - A 3D facies probability model was generated based on extrapolation of 3 wells in order to capture
observed geological changes within the area. Top Intra Gumai marker was used as reference
padding layer analysis as shown in the picture.
Figure 14 - A relatively consistent inversion result is achieved between deterministic and geostatistical
inversion. A relatively stable geostatistical inversion is achieved as well for both cases;
constrained and unconstrained cases.
Figure 15 - Seismic section and geostatistical seismic inversion products such as inverted impedance,
lithofacies, volume of clay and effective porosity.
Figure 16 - Geostatistical inversion result integrated with well data indicates that the Intra Gumai Formation
potentially has several good reservoir porosities and they could be charged by hydrocarbon.
Figure 17 - Lateral distribution of sand reservoir Intra Gumai reflects channelized feature.